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Multivariate extremal dependence and risk measures Hua, Lei 2012-06-11

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Multivariate Extremal Dependence and Risk Measures by Lei Hua B.Econ., University of Science and Technology of China, 2002 M.Sc. University of Calgary, 2008 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE STUDIES (Statistics) The University of British Columbia (Vancouver) June 2012 c Lei Hua, 2012Abstract Overlooking non-Gaussian and tail dependence phenomena has emerged as an im- portant reason of underestimating aggregate financial or insurance risks. For mod- eling the dependence structures between non-Gaussian random variables, the con- cept of copula plays an important role and provides practitioners with promising quantitative tools. In order to study copula families that have different tail pat- terns and tail asymmetry than multivariate Gaussian and t copulas, we introduce the concepts of tail order and tail order functions. These provide a unified way to study three types of dependence in the tails: tail dependence, intermediate tail dependence and tail orthant independence. Some fundamental properties of tail order and tail order functions are obtained. For multivariate Archimedean copulas, we relate the tail heaviness of a positive random variable to the tail behavior of the Archimedean copula constructed by the Laplace transform of the random variable. Quantitative risk measurements pay more attention on large losses. A good statistical approach for the whole data does not guarantee a good way for risk as- sessments. We use tail comonotonicity as a conservative dependence structure for modeling multivariate dependent losses. By this way, we do not lose too much accuracy but gain reasonable conservative risk measures, especially when we con- sider high-risk scenarios. We have conducted a thorough investigation on the prop- erties and constructions of tail comonotonicity, and found interesting properties such as asymptotic additivity properties of risk measures. Sufficient conditions have also been obtained to justify the conservativity of tail comonotonicity. For large losses, tail behavior of loss distributions is more critical than the whole distributions. Asymptotic study assuming that each marginal risk goes to in- finity is more mathematically tractable. However, the asymptotic study that leads iito a first order approximation is only a crude way and may not be sufficient. To this end, we study the second order conditions for risk measures of sub-extremal multiple risks. Some relationships between Value at Risk and Conditional Tail Ex- pectation have been obtained under the condition of Second Order Regular Vari- ation. We also find that the second order parameter determines whether a higher order approximation is necessary. iiiPreface This thesis has been written up under the supervision of my advisor Professor Harry Joe, and it is based on four papers coauthored with Dr. Harry Joe. Two of them have been published, one is to be published and the other is under review. This thesis also contains a small portion of research that has not been published or submitted anywhere by the date of completing the thesis. Chapter 1 was prepared for the purpose of presenting the general shape or taste of the main research within the thesis. More than half of the contents are newly written and the others are scattered in the following Chapters 3 to 6. Chapter 3 is based on a published paper: Hua and Joe (2011a), Tail order and intermediate tail dependence of multivariate copulas. Journal of Multivariate Analysis, 102: 1454–1471. This chapter covers the topics in the paper but not exclusively. Dr. Joe identified the main research questions and I contributed to the paper mainly through completing the majority of the proofs, writing the first version of the manuscripts and conducting follow-up revisions. The key idea for the proof of the important Lemma 3.4 was contributed by Dr. Joe, and the proof for Proposition 3.5 also benefited from the idea. Chapter 4 is based on a submitted paper: Hua and Joe (2012b), Tail comono- tonicity: properties, constructions, and asymptotic additivity of risk measures. The concept of tail comonotonicity was partially motivated by Dr. Joe, especially with his concrete examples. I proposed some main ideas such as the asymptotic additiv- ity of risk measures, completing almost all of the proofs, writing the first version of the manuscripts and taking care of follow-up tasks. Chapter 5 is based on a paper in press: Hua and Joe (2012a), Tail comono- tonicity and conservative risk measures. to appear in ASTIN Bulletin, 2012. I ivconducted all of the research here, including theoretical development, simulation study and data analysis under the supervision of Dr. Joe. I wrote the first version of the manuscripts and conducted follow-up tasks. Chapter 6 is based on a published paper: Hua and Joe (2011b), Second order regular variation and conditional tail expectation of multiple risks. Insurance Math. Econom., 49: 537–546, 2011. I proposed the main ideas and conducted all of the research, including mathematical proofs and simulations under the supervision of Dr. Joe. I wrote up the first version of the manuscripts and dealt with other follow- up tasks. For all of the above referred research, in addition to the contributions men- tioned, Dr. Joe spent a huge amount of time motivating research questions, check- ing the results and proofs, improving the writing and making all the other contri- butions to make the research contained in this thesis better. vTable of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Data examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Relevant practical needs . . . . . . . . . . . . . . . . . . . . . . 7 1.5 Overview of literature . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6 Summary of main concepts . . . . . . . . . . . . . . . . . . . . . 11 1.6.1 Tail order and strength of dependence in the tails . . . . . 11 1.6.2 Tail order and conditional copula . . . . . . . . . . . . . 15 1.6.3 Tail order and Conditional Tail Expectation . . . . . . . . 21 vi1.6.4 Tail order and tail order functions . . . . . . . . . . . . . 24 1.6.5 Concordance ordering in the tail . . . . . . . . . . . . . . 26 1.6.6 Sub-extremes and second order conditions . . . . . . . . . 27 1.7 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.8 Highlights of selected new results . . . . . . . . . . . . . . . . . 28 2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.1 Risk measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.2 Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3 Regular variation . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.4 Maximum Domain of Attraction . . . . . . . . . . . . . . . . . . 36 3 Tail order and intermediate tail dependence . . . . . . . . . . . . . . 38 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Tail orders: definitions and properties . . . . . . . . . . . . . . . 40 3.2.1 Multivariate tail order and tail order functions . . . . . . . 40 3.2.2 Further properties of tail orders . . . . . . . . . . . . . . 49 3.3 Intermediate tail dependence : Archimedean copulas . . . . . . . 51 3.3.1 Laplace transform and univariate tail heaviness . . . . . . 52 3.3.2 Upper tail . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.3 Lower tail . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3.4 A new parametric Archimedean copula . . . . . . . . . . 57 3.4 Intermediate tail dependence: Mixture of max-id copulas . . . . . 59 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.6 Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4 Tail comonotonicity . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.2 Definitions of tail comonotonicity and properties . . . . . . . . . 80 4.3 Construction of tail comonotonic copulas . . . . . . . . . . . . . 86 4.3.1 Archimedean copulas . . . . . . . . . . . . . . . . . . . . 86 4.3.2 Heavy tail mixtures . . . . . . . . . . . . . . . . . . . . . 89 4.3.3 Extreme value copulas . . . . . . . . . . . . . . . . . . . 93 4.4 Asymptotic additivity of risk measures . . . . . . . . . . . . . . . 93 vii4.5 Concluding remarks and future research . . . . . . . . . . . . . . 98 4.6 Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Tail dependence and conservativity . . . . . . . . . . . . . . . . . . . 111 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.2 Conditional specifications . . . . . . . . . . . . . . . . . . . . . . 113 5.2.1 The case: E[X1jX2 > t] . . . . . . . . . . . . . . . . . . 115 5.2.2 The case: E[X1jX2 = t] . . . . . . . . . . . . . . . . . . 120 5.2.3 Second order conditions and conservativity . . . . . . . . 121 5.3 Asymptotically worst dependence structures . . . . . . . . . . . . 123 5.3.1 Conditional tail expectation . . . . . . . . . . . . . . . . 124 5.3.2 Value at risk . . . . . . . . . . . . . . . . . . . . . . . . . 125 5.4 Simulation study . . . . . . . . . . . . . . . . . . . . . . . . . . 126 5.4.1 Conditional specifications . . . . . . . . . . . . . . . . . 126 5.4.2 Asymptotically worst dependence structures . . . . . . . . 127 5.5 Application on a claim dataset . . . . . . . . . . . . . . . . . . . 131 5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 5.7 Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6 Second order regular variation and risk measures . . . . . . . . . . 143 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 6.3 Univariate cases . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 6.4 Multivariate cases . . . . . . . . . . . . . . . . . . . . . . . . . . 153 6.5 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . 161 6.6 Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 7 Conclusions and future research . . . . . . . . . . . . . . . . . . . . 170 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 7.1.1 Strength of dependence in the tails . . . . . . . . . . . . . 170 7.1.2 Tail patterns and risk measures . . . . . . . . . . . . . . . 171 7.2 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 7.2.1 Tail behavior of CTEs . . . . . . . . . . . . . . . . . . . 172 7.2.2 Intermediate tail dependence from hidden regular variation 180 viiiBibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 ixList of Tables Table 1.1 Tail order of various bivariate copulas . . . . . . . . . . . . . . 14 Table 1.2 Tail order and conditional copula . . . . . . . . . . . . . . . . 21 Table 3.1 Tail order of some Archimedean copulas that interpolate inde- pendence and comonotonicity . . . . . . . . . . . . . . . . . . 59 Table 5.1 Comparisons between Gumbel, Joe, BB1 and BB7 copula families117 Table 5.2 Condition 0(  1(u)) for parametric Archimedean copula fam- ilies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Table 5.3 (More dependence) VaR and CTE for X1 + X2. The MLEs were based on the whole sample generated from s.BB1 ( = 1:57;  = 1:68;  = 0:77, more dependence) with a sample size of 2000. The bold AIC value is the smallest. . . . . . . . . . . 130 Table 5.4 (Less dependence) VaR and CTE forX1 +X2. The MLEs were based on the whole sample generated from s.BB1 ( = 2;  = 0:4;  = 0:42, less dependence) with a sample size of 2000. The bold AIC value is the smallest. . . . . . . . . . . . . . . . 131 xTable 5.5 VaR and CTE for X1 + X2. The first 4 columns of values are the means of corresponding quantities calculated from 40 random samples generated from the same settings as before. The rest are frequency of those quantities being greater than those for s.BB1.o. (A: more dependent, large sample size; B: less dependent, large sample size; C: more dependent, small sample size; D: less dependent, small sample size. More de- pendence:  = 1:57;  = 1:68;  = 0:77; less dependence:  = 2;  = 0:4;  = 0:42. Large sample: N = 2000; small sample: N = 50.) . . . . . . . . . . . . . . . . . . . . . . . . 132 Table 5.6 Estimates for margins with/without 1% largest ALAE removed 133 Table 5.7 Comparison between the s.BB2 and Gumbel copulas: from the second order condition of BB2, we know that  is the second order parameter that dominates the tail behavior of the copula. If we fit  ;  of s.BB2 simultaneously for the data, then the MLE of  tends to be very close to 0; that is, s.MTCJ could better fit the data than s.BB2 (since as  ! 0, BB2 becomes MTCJ). But s.MTCJ will lose the asymptotic full dependence structure. The aim of this comparison is not to find the best fitting copula but to study how conservative s.BB2 is. So we fix two values for the parameter  for s.BB2 ( = 0:01; 0:1), and then obtain the MLEs of  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Table 6.1 How large p must be to get a good second order approximation: the values are the corresponding p for which the absolute dif- ference between the second order approximation and the true value is 5% of the true value. . . . . . . . . . . . . . . . . . . 152 xiList of Figures Figure 1.1 Data examples - LOSS vs Expense (ALAE) . . . . . . . . . . 5 Figure 1.2 Data examples - Florida flood . . . . . . . . . . . . . . . . . 5 Figure 1.3 Data examples - DJI vs FTSE . . . . . . . . . . . . . . . . . 6 Figure 1.4 Data examples - N225(t) vs N225(t+ 1) . . . . . . . . . . . 7 Figure 1.5 Copula is a joint cumulative distribution function (cdf) of uni- form margins . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Figure 1.6 Contour plots: Gaussian copula + standard normal margins . . 15 Figure 1.7 Contour plots: Student t copula + standard normal margins . . 16 Figure 1.8 Contour plots: Frank copula + standard normal margins . . . 16 Figure 1.9 Contour plots: Gumbel copula + standard normal margins . . 17 Figure 1.10 Contour plots: MTCJ copula + standard normal margins . . . 17 Figure 1.11 Contour plots: BB2 copula + standard normal margins . . . . 18 Figure 1.12 Tail order and CTE plots of the form E[X1jX2 > t]. The range of t in each plot was chosen to cover the support of t between the 1% and 95% quantiles. Gumbel( = 2), s.Gumbel( = 2) and MTCJ( = 1). . . . . . . . . . . . . . . . . . . . . . . . 23 Figure 1.13 Tail order and CTE plots of the form E[X1jX2 = t]. The range of t in each plot was chosen to cover the support of t between the 1% and 95% quantiles. Gumbel( = 2), s.Gumbel( = 2) and MTCJ( = 1). . . . . . . . . . . . . . . . . . . . . . . . 23 Figure 3.1 Contour plots: ACIG copula + standard normal margins . . . 58 Figure 4.1 Florida flood data - Comonotonicity vs Upper comonotonicity 81 xiiFigure 4.2 Simulation of BB2 with/without the univariate margins being transformed to the standard Normal; in the left and middle plots  = 0:2,  = 0:4 and in the right plot  = 0:2,  = 0:2 . 85 Figure 4.3 Simulation of BB3 with/without the univariate margins being transformed to the standard Normal; in the left and middle plots  = 0:2,  = 1:7 and in the right plot  = 0:2,  = 1:3 . 86 Figure 5.1 The value of y-axis is E[X1jX2 > VaRp(X2)]. In the first plot, X1; X2 have Exponential distributions with cdf F (x) = 1  Exp( x= ). In the second plot, X1; X2 have Pareto dis- tributions with cdf F (x) = 1 (1 + x= )  . . . . . . . . . . 128 Figure 5.2 The value of y-axis is E[X1jX2 = VaRp(X2)]. In the first plot, X1; X2 have Exponential distributions with cdf F (x) = 1  Exp( x= ). In the second plot, X1; X2 have Pareto dis- tributions with cdf F (x) = 1 (1 + x= )  . . . . . . . . . . 129 Figure 5.3 The value of y-axis is E[ALAEjLOSS  VaRp(LOSS)]. “Em- pirical” is based on the data with the 1% largest removed; “Em- pirical0” is based on the original data. . . . . . . . . . . . . . 135 Figure 6.1 Sub-extremal relationships between CTE and VaR ( = 2) for Hall/Weiss class . . . . . . . . . . . . . . . . . . . . . . . . 151 Figure 6.2 First/Second order approximations for Burr scale mixture of normal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Figure 7.1 CTE plots for E[X1jX2 > t]. The range of t in each plot was chosen to cover the support of t between the 1% and 99% quantiles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Figure 7.2 CTE plots for E[X1jX2 = t]. The range of t in each plot was chosen to cover the support of t between the 1% and 99% quantiles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 xiiiGlossary cdf cumulative distribution function ACIG Archimedean Copula based on the Laplace Transform of Inverse Gamma MTCJ Mardia-Takahasi-Cook-Johnson copula MRV Multivariate Regular Variation HRV Hidden Regular Variation 2RV Second Order Regular Variation CTE Conditional Tail Expectation VaR Value at Risk MDA Maximum Domain of Attraction ALAE Allocated Loss Adjustment Expenses LOSS Indemnity Payment DJI Dow Jones Industrial Average Index FTSE FTSE 100 Index N225 NIKKEI 225 Index AMA Advanced Measurement Approaches LT Laplace Transform xivAcknowledgments I am greatly indebted to my supervisor Professor Harry Joe. Professor Joe is an encyclopedia to me. Every time when I asked questions, I can get prompt and instructive suggestions from him. Professor Joe has written up many inspiring notes to motivate research questions, and to help me develop my own research skills; I feel that he cares about my research and even my future careers very much. He masters the art of supervision: he gave me sufficient pressure to move forward, and also allowed me to adjust my own pace to it. With the excellent supervision of Harry, I, just like a baby who starts learning to walk, have accumulated great encouragement to step forward, because I know that a powerful arm is just around and is always ready to pull me up. I would like to express my sincere thanks to Professors Haijun Li, Natalia Nolde and Matı´as Salibia´-Barrera, who are members of the Comprehensive Com- mittee and/or the Supervision Committee. Professor Li is very warm-hearted and he has pointed out several interesting topics to motivate my research interests. Spe- cial thanks are given to Natalia; she helped me a lot for improving my writing, not only for the thesis but also for other application documents. I also want to thank Professor Christian Genest for acting as the external examiner, and Professors Ed- win Perkins and Ruben Zamar for serving as the university examiners. I want to extend my sincere gratitude to the faculty and staff of the department, to those who have directly or indirectly helped me during my study at UBC. I have taken two courses in statistical consulting with Professor John Petkau, who helped me a lot in improving my statistical thinking and writing. I also want to say Thank You to Peggy Ng, Elaine Salameh, Andrea Sollberger, and Viena Tran for lots of prompt helps and routine work. xvI also thank the department Ping Pang players Professors Ruben Zamar and Lang Wu, and Chen Xu, Xu Wang and Yongliang Zhai, for many fun times, and thank the other fellow students who have helped me during these enjoyable four years. Special thanks to Professor Ka Chun Cheung, my Master’s supervisor, and the Department of Statistics and Actuarial Science at the University of Hong Kong; I was preparing the thesis proposal with the kind hospitality of Ka Chun and the financial support from Canada Graduate Scholarships - Michael Smith Foreign Study Supplements Program. This thesis has been written up with the generous financial support from the Natural Sciences and Engineering Research Council of Canada, to which I want to express my sincere thankfulness. Lastly and most importantly, I sincerely thank my wife, Chaoxiong Xia, for her love, encouragement and support in every moment of my life, and my two little kids Claire and Daniel, for bringing me so much happiness during the journey of completing the thesis. xviDedication xviiChapter 1 Introduction 1.1 Motivation “The interviewed firms described some general common lessons learned from the crisis, of which probably the most commonly mentioned was the necessity (but difficulty) of capturing tail risks and dependencies resulting from tail events.” Developments in Modelling Risk Aggregation Basel Committee on Banking Supervision, 2010 The research activities on dependence modeling of large losses and their risk assessment have been sparked by the contagion of the global financial crisis arising in the year of 2007. There have been tons of articles debating the roots of the crisis from many perspectives. One common opinion from the viewpoint of researchers in quantitative fields is that the high-risk scenarios as well as their comovements in the global financial system have been ignored, which hides the high possibility of extremal losses that could happen simultaneously. This thesis is not going to join the discussion, but attempts to contribute to the understanding of quantitative and/or statistical methodologies that account for the phenomenon of heavy tails of each individual risk and their dependence as well. Among many issues of modeling insurance losses or financial returns, esti- mating, predicting and pricing potential extremal risks are particularly important. 1Moreover, many extremal losses have been observed to have occurred consecu- tively or simultaneously. The comovement, in the meanwhile, could considerably increase the entire risk. Thus, the dependence of extremal risks is expected to play an important role for integrated risk management. Although the statistical methodologies for studying overall properties of random samples such as means are relatively mature, sound methodologies for studying the dependence relation- ship between high quantities are far from being well-developed. 1.2 Notation The following notation will be used throughout the thesis. We use bold letters for vectors, such as x := (x1; : : : ; xd)T, where T is the transpose operation of a matrix. 1d = (1; : : : ; 1) with d elements, and then u1d = (u; : : : ; u); Id = f1; : : : ; dg. N+ = f1; 2; : : : g; R := ( 1;1); R+ := [0;1); R+ := [0;1]; R := [ 1;1]; A  B means that A is a subset (not necessarily a proper subset) of B; Cartesian product is denoted as [a;b] := [a1; b1]      [ad; bd]; let x := (x1; : : : ; xd) and y := (y1; : : : ; yd), then x  y means that xi  yi for all i = 1; : : : ; d. [x] = maxft integer : t  xg;  (i)(s) is the ith order derivative of  evaluated at s; 1A(x) is the indicator function such that 1A(x) = 1 if x 2 A, and 1A(x) = 0 if x =2 A. For a random variable X , the generalized inverse of its cumulative distribution function (cdf) is defined as F 1X (p) = inffx 2 R : FX(x)  pg: Also, note that for all x 2 R and p 2 [0; 1], we have (e.g., Dhaene et al., 2002a) F 1X (p)  x () p  FX(x): (1.1) A random variable is said to be continuous if its cdf is continuous. The survival function is F j = 1 Fj if Fj is a cdf. For a d-dimensional multivariate cdf F , the survival function is F = 1 + ( 1)jIj P ;6=I f1;:::;dg FI , where FI is the cdf for the I-margin. X d = Y means X and Y equal in distribution. For a bivariate differen- tiable cdf F , we write (@1F )(x1; x2) := @F (x1; x2)=@x1 = F2j1(x2jx1)f1(x1), 2where f1 = F 01. Unless otherwise mentioned, a distribution is assumed to be abso- lutely continuous with respect to Lebesgue measure. Throughout the thesis, we use R(F1; : : : ; Fd) to represent the Fre´chet space of all d-dimensional random vectors with F1; : : : ; Fd as the marginal distributions. T ( ) is the cdf of the standard univariate Student t distribution with degree of freedom  , and t ( ) is the corresponding density function. T ; ( ) is the cdf of the standard multivariate Student t distribution with degree of freedom  and covariance matrix  , and t ; ( ) is the corresponding density function.  ( ) is the cdf of the standard univariate Normal distribution and  ( ) is the density function. For the standard multivariate Normal distribution, the cdf and density function are   and   , respectively. Throughout this thesis, % will be used to represent a correlation coefficient and  is reserved for the second order parameter of Second Order Regular Variation (2RV). The notation C will always represent a copula function, a multivariate cdf with U(0; 1) univariate margins. The conditional distributions of a bivariate copula are written as C1j2(ujv) = @C(u; v)=@v and C2j1(vju) = @C(u; v)=@u. A density function of copula is written as c(u; v) := @2C(u; v)=@u@v. Second order partial derivatives are D22C(u; v) = @2C(u; v)=@2v. For a copula, say, the Gumbel copula, the corresponding survival copula is denoted as “s.Gumbel”; that is,“s.” symbolizes the survival copula. A lower tail dependence parameter is denoted by  L, and an upper tail dependence parameter is denoted by  U . We use  for tail order, and  L and  U are lower and upper tail orders, respectively. For positive functions f; g, asymptotic equivalence is denoted as f  g if lim f=g = 1, asymptotic inequality f & g if lim inf f=g  1, and asymptotic inequality f . g if lim sup f=g  1. The notation f(t) % g(t); t ! 1 means that f is ultimately greater than g; that is, 9t0 such that t  t0 implies that f(t)  g(t). h(t) = o(g(t)) means limt!t0 h(t)=g(t) = 0, and h(t) = O(g(t)) means 0  limt!t0 h(t)=g(t) <1, where  1  t0  1 is a given limiting point. The notation RV represents the class of functions that are regularly varying at 1 with index  2 R, and RV (0+) represents the class of functions that are regularly varying at 0 with index  2 R. MRVd( ) represents the class of d- dimensional multivariate regularly varying random vectors with  2 R the regular 3variation index. ‘(x) is used as a slowly varying function. The letter E represents mathematical expectation, and Var is for variance. Note that the Var here is different than the notation VaR (Value at Risk) defined in Def- inition 2.1; the latter is used in insurance and finance. x _ y := maxfx; yg; x ^ y := minfx; yg. 1.3 Data examples In this subsection, we will present some data examples to motivate various pat- terns of tail dependence and tail asymmetry. The marginal distributions are usually transformed to a standard Normal distribution to visualize the patterns of upper and lower tails. Let us look at a well known dataset in the actuarial literature (Frees and Valdez, 1998). This dataset comprises 1500 general liability claims with Indemnity Pay- ment (LOSS) representing the amount of payment and Allocated Loss Adjustment Expenses (ALAE) that are specifically attributable to each claim and may include legal expenses, investigation expenses, etc. A typical pattern for this dataset is that the margins are heavy-tailed and the degree of positive dependence between large values is larger than that between small values. This kind of tail asymmetry pattern is not clear as one looks at the scatter plot for the original data (left panel of Figure 1.1). In order to visualize the asymmetric tail dependence, we usual transform each margin to be distributed as univariate standard normal, and then look at the scatter plot for the transformed pairs (right panel of Figure 1.1); such transformed values are referred to as normal scores. A stronger upper tail dependence for this dataset appears, and it makes sense since a claim with a larger loss amount tends to involve more legal expenses and demands more time and costs for investigation. The asymmetric tail dependence pattern can also be observed in environmental data, such as a Florida flood loss dataset. There are 67 counties in Florida. The dataset comprises the monthly amount of losses for each county from 1977 to 2006, and it is a part of the Spatial Hazard Events and Losses Database for the United States (SHELDUS) that is maintained by the Hazards and Vulnerability Research Institution. We choose randomly 30 counties and add the loss amount among these counties together to get a monthly aggregate loss for these counties. Then we 4Figure 1.1: Data examples - LOSS vs Expense (ALAE) G G G G GGG G G G G G G G G GG G GG G G G G G GG GG GG G G G G G GG G G G G G GGG G G G G G G G G GG G G G G G G G G GG G G G G GG GG GG G GG G G G G G GG G G G GG GGG G G G G GG G G G G G G G G G G G GG G G G G G G G G G 0 500000 1500000 2500000 0e+0 0 2e+0 5 4e+0 5 6e+0 5 LOSS ALA E G G GG G G G G G G G G GG G G G G G G G G G G G GG G GG GG GG GG GG G G G GGG G G G GG GG G G G G G GG G GGG G GG GG GG GG GG GG G G G G GGG G GG GG G G G G G G G G GG G G G G GG G G G G G GG G G GG G G G G G GG G G G GGG GG G GG GG GG GGG G G G G G G G G G G G G G G GG G GG GG G GG GG GG GGG GGG GG GG G G G G G G G G GG G G G G G G GG GG GG G G G G G GG G G G GG G G GG G G G GG G G G GG GG G G G G G G G G G G G G G G G G G GGG GGG GG GG GGG GGG GG GGG GGG GGG G G GG G G G G G G G GG G GG G G G G G G G G G GG GG G G G G G G G GG G G GG GG GG G GG GG GG G G G G G G G GG G G G G G G G G G G G G G G G G G G G GG GG GG GG GG GG GGG GGG GG GGG GG GG G G G G GG GG G G GG G G G G GG G G GG G G GG G G G G G G G G G G G G G G G G GG GG GG GG GGG GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG GG G GG G G G G G G G G G G G G G GG GG G G G G G G G G GG GG GGG GG GG GG GG GG G G G G GG G G G G GG G G GG G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG GG GGG G GGG G G GG G G G G G G G G GG G G G G G G G GG G G G G G G G G G G G G G G GGG GG GGG GG G G G G G G GG G G G G G G G G G G G G G G GG G GG GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG GG G G G G G G G GG G G G G G G G G GG G G G G G G GG G G G G G G G G G G GG G G GG G G G G −4 −2 0 2 4 − 4 − 2 0 2 4 Norm(LOSS) No rm(ALAE ) randomly choose another 30 counties from the rest counties to get the monthly aggregate loss similarly. The dependence between these two aggregate losses can be demonstrated by Figure 1.2. It is very clear that the upper tail dependence is stronger than the lower tail dependence. This phenomenon of stronger upper tail dependence for aggregate losses is related to a concept of micro-correlation that is studied in Cooke et al. (2011). Figure 1.2: Data examples - Florida flood Florida flood aggregate loss (1977~2006) Normal score of aggregate loss of 30 counties No rmal score of aggregate loss of other 30 countie s −2 −1 0 1 2 −2 −1 0 1 2 G G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G 5In the natural world or in our human society, there seems to be certain mecha- nism that drives the happening of clusters of high-risk events. Not only in insurance practice, but also in the financial market, we may observe such tail asymmetry phenomena. Figure 1.3 is a scatter plot of normal scores for weekly log returns between Dow Jones Industrial Average Index (DJI) and FTSE 100 Index (FTSE) during the period of October 1st, 2005 to June 30th, 2007. The plot illustrates that the dependence between large losses of two stock markets is higher than the dependence between large returns. Figure 1.3: Data examples - DJI vs FTSE DJI vs FTSE (20051001~20070630, weekly) Normal score of weekly logreturn of DJI No rmal score of weekly logretu rn of FTS E −2 −1 0 1 2 −2 −1 0 1 2 G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G Serial dependence within a univariate financial time series may also appear to be tail asymmetric. For example, if we take the squared daily log returns as approx- imations to observed volatilities, then the asymmetric pattern of serial dependence between such observed volatilities can be illustrated by Figure 1.4. Here we use NIKKEI 225 Index (N225) during the period of October 1st, 2005 to March 9th, 2009 as an example. The observed volatilities tend to clutter together, which has already been noticed by many researchers. We refer to Engle (2004), written by one of the two holders of Nobel Prize of 2003 in economics, for an excellent review on volatility clustering. Note that the above scatter plots are simply used to visualize the asymmetric tail dependence patterns; those are not necessarily meaning that certain bivariate 6Figure 1.4: Data examples - N225(t) vs N225(t+ 1) NIKKEI 225 (20051001~20090309, daily) Normal score of squared logreturn at t No rmal score of squared logretu rn at t+ 1 −3 −2 −1 0 1 2 3 −3 −2 −1 0 1 2 3 G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G GG GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G GG G G G G G G G G G GG G GG G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG G G G G G G GG G G G G G G G G G G G G G GG G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G GG G G G G G G G G distributions are ready to be used for fitting those data (or transformed data), as the data points in the plots may not even be a random sample. For specific questions, one needs to fully consider the dependence structures that saturate the data of in- terest to decide where a dependence model such as a copula can be appropriately applied. 1.4 Relevant practical needs The term of tail modeling in this thesis is referred to as statistical modeling for marginal distributional tails and their dependence structure in the tails of the joint distribution. Traditional areas that require tail modeling include rate makings for reinsur- ance, auto insurance, etc., and statistical modeling in finance and various subject scientific fields such as environmental science and Internet engineering. There are also some emerging needs for tail modeling in quantitative risk management for the bank and insurance sectors. For instance, under Basel III – a global regulatory standard on bank capital adequacy, stress testing and market liquidity risks – oper- ational risk charges can be calculated based on the so-called Advanced Measure- ment Approaches (AMA). Operational risk is typically very heavy-tailed (Dutta and Perry, 2006), and the modeling and risk assessment is actually very challeng- 7ing (Chavez-Demoulin et al., 2006; Cope et al., 2009). In parallel, under Solvency II, there are relevant regulatory requirements on operational risks for insurers. First of all, AMA itself is far from being well developed. Sound methodolo- gies/strategies for risk assessment and management are in high demand. Secondly, the developing market of operational risk mitigation products would entail huge potential for research on tail modeling; for example, how to conduct tail risk secu- ritization and how to design and price relevant products? 1.5 Overview of literature Before dealing with any particular models, a fundamental question is how to model the dependence between various univariate random variables, especially for those with non-Gaussian distributions. In the past practice of actuarial science, quantita- tive finance and so on, dependence structures between random variables were often studied with some summary quantities, such as, correlation coefficient, Kendall’s  and Spearman’s  . Especially the usage of correlation coefficient has been popular. It is well known that the maximum correlation of 1 will be reached if and only if the two random variables have the same distribution up to a linear transformation (Rodgers and Nicewander, 1988). Shortcomings of using correlation coefficient to model the dependence for insurance and finance have been observed by many papers. We refer to Embrechts et al. (2002) for an inspiring reading. Apparently, every single summary statistic is not able to fully describe the dependence struc- ture between random variables. Instead, the copula approach becomes more and more popular for modeling dependence structures of insurance losses or financial returns where marginal distributions are often heavy-tailed and skewed. We list here, among many of others, some advantages of copula approaches: (1) the cop- ula itself is a joint cdf, which captures much more information than any single quantity; (2) when the univariate margins are all continuous, the copula function can be uniquely determined, thus fully characterizing the dependence structure; (3) as a multivariate cdf, copula can be parametrized and is useful for statistical model- ing and inference; (4) the copula provides a great deal of flexibility for constructing multivariate distributions. The copula itself is a joint cdf with Uniform(0; 1) marginal distributions. Let 8F be the joint cdf, F1; : : : ; Fd be the univariate cdfs, and C be a copula, then F can be written as F (x1; : : : ; xd) = C(F1(x1); : : : ; Fd(xd)): For the insurance loss data in Figure 1.1, if we transform each marginal data via the corresponding empirical distribution to get data that is approximately uniformly distributed on [0; 1], then the scatter plot for the pairs of transformed data is Figure 1.5. Roughly speaking, the copula C can be used to fit such transformed data. A more formal introduction to copulas is in Section 2.2. Figure 1.5: Copula is a joint cdf of uniform margins G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG G GG G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G GG G GG GG G G G G G G G G G GG G G G G G G G GGG G GG G G G G G G G G G GG G G G GG G G G GG GG GG G G G G G G GG G G G G G G G G G G G G G G G G G G GG GG G G G GG G GG GG G GG G G G G G G G G G GG G G G G G G G G G G G G GG GG G GG G GG GG GG G G GG GG GG G GG GG G G GG G G G G G G G G G G G G G G G G G G G GG G GG GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G GG GG G G G G G G G G G GG GG G G G G GG G G G G GG GG G G G G G G G GG G GG GG G G G G G G G G GGG G GG GG GG GG GG GG GG G G GG G GGG GGG GG GG GG GGG GG G G GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G GG G G G G G G G G G G G G G G G G G G G G G G G G GG G GG GGG GG G GG GG GG G GG GG G GG G GG G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG G G G G G G G GG GG G GG GG GG GG G GG GG G G G G GG GG GG GGG GG GG GG GG G G G G G G G G G GG G G G G G G G G G G G G GG G G G G GG GG G G G G G G G GG GG G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G GG GG GG G G GG G G GG G G GG GG G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG GG G G G G G G G G G G GG G G G G G G GG G G G GG GG G G G GG GG GG GG G G GG G G G G G G G G G G GG G G G G G G G G G G G G G G G G GG GG G G G G GG G G G G G G G G GG G G G G G G GG G GG G GG G GG GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G GG G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G GG GG GG GG G G G GG G GGG G GG G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG GGG GG G G G G G G G GG G G G G G G G G G G G GG G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G GG G G G G G G G GG G G GG G G G G 0.0 0.2 0.4 0.6 0.8 1.0 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 Unif(LOSS) Unif(ALAE ) A pioneer paper on copula theory is Sklar (1959), which established the well known Sklar’s theorem: there is a copula function for joint distributions and the copula is unique if the univariate marginal distributions are continuous. Standard references for dependence modeling and copulas are Joe (1997) and Nelsen (2006). The former is a very informative monograph on dependence concepts and relevant statistical modelings, where copula is one fundamental tool for statistical depen- dence modeling. The latter deals with the concept of copula in a more mathematical way, and focuses on relevant theories of copulas. For contributions to the classic statistical issues on copula modeling, such as statistical inference and goodness-of- fit tests, we refer the reader to the following contributions and references therein: 9Genest and Rivest (1993), Genest and Favre (2007), Genest et al. (2009b) and Brechmann et al. (2012). Research activities on copula modeling in actuarial sci- ence and quantitative finance have sparked since Frees and Valdez (1998), and Li (2000). Two excellent reference books on dependence modeling in actuarial sci- ence and quantitative risk management are Denuit et al. (2005) and McNeil et al. (2005). Most recently, Kurowicka and Joe (2011) and Jaworski et al. (2010) have provided good summaries of copula theories and applications: the former promotes a relatively new concept of vine copulas, and the latter contains several survey pa- pers on copulas. The copula approach is particularly useful for studying tail behavior of mul- tivariate non-Gaussian distributions (Joe, 2006; Mikosch, 2006). It is extremely flexible to use copula to construct multivariate distributions that are able to account for various distributional tails. In the practice of actuarial science and quantitative risk management, a more relevant task is to model and conduct risk assessment for the joint tails of dependent losses, especially for large or extremal risks. The classic univariate extreme value theory provides a sound theoretical ground for building up useful approaches for assessing univariate marginal tails. Compared with the univariate extreme value theory, the multivariate extreme value theory has not been well developed. In parallel with the copula approach, researchers find that trans- forming the marginal distributions to those having the same power law can be more convenient in some situations. This approach is referred to as Multivariate Regular Variation (MRV), and its root, the theory of univariate regular variation has been nicely developed. There are some excellent reference books for extreme value theory, and for regular variation. de Haan and Ferreira (2006) is a nice monograph on theoretical aspects of extreme value theory and includes some discussion on 2RV, Embrechts et al. (1997) has become a classic for modeling univariate extremal events, and Coles (2001) provides a clear and more intuitive introduction to statistical model- ing of univariate extreme values. The books Beirlant et al. (2004) and Reiss and Thomas (1997) comprise many interesting topics for statistical modeling of ex- treme values that include not only univariate but also multivariate extreme values, and Falk et al. (2010) is a comprehensive monograph on theories and applications of multivariate extreme values such as multivariate Peaks Over Threshold (POT) 10approaches. For researchers who study the theory of regular variation and who may use regular variation as a tool, Bingham et al. (1987) should be a must-have and it provides encyclopedic reference on the theory of univariate regular variation. For MRV, Resnick (1987) and Resnick (2007) are standard reference books, and the latter also reflects some recent development of MRV. The above literature review is about the general idea of copula theory and ex- treme value theory. More specific literature reviews for concrete topics that belong to these research themes will be conducted in later chapters when it becomes nec- essary. 1.6 Summary of main concepts In this section, we will highlight different concepts for copulas and their joint tails; this is to emphasize that different copula families have different tail prop- erties which may have a large effect on inferences such as joint tail probabilities (chance of simultaneously large losses from different risks) and conditional tail ex- pectations. The bivariate case will be employed here to explain the basic ideas, and those ideas can be extended to the multivariate case. 1.6.1 Tail order and strength of dependence in the tails One theme of the thesis is to study the strength of dependence in the tails. In statistical modeling, a basic idea is to find a summary quantity that can capture useful information of interest. Such a quantity should be simple and be able to account for as much as possible relevant and critical information. If we use a copula C to model the dependence between two continuous random variables X and Y , then the graph of the map u 7! C(u; u); u 2 [0; 1] describes the evolution of joint probabilities ofX and Y . In order to study the tail behavior of the map and thus the tail dependence betweenX and Y , we hope to have a certain simple function g that can be used to approximate C(u; u) as u! 0+; that is, C(u; u)  g(u); u! 0+. Such a function g(u) should be bounded by u and go to 0 as u ! 0+. Therefore, a suitable class of functions for g can be the class of regularly varying functions with the minimum regular variation index being 1, e.g., g(u) = u ‘(u) where 1   and ‘ is a slowly varying function such as ‘(u) = ( log u) 1=3. A formal 11introduction to regular variation is in Section 2.3. For the function g, the term u dominates the behavior of g(u) as u! 0+. So we choose  as such a summary quantity that can be used to capture the information of tail association patterns, and  is referred to as tail order. A smaller  means higher joint probability in the lower corner as u! 0. Although obviously a single quantity such as the tail order  can not capture all the information on the tail, it provides relevant and critical information of interest, especially when we compare tail orders of copulas within the same copula family, or when the information of interest is for a sufficiently small u. Therefore, such a simple quantity may also be useful in statistical modeling. When  = 1, that is, C(u; u)  u‘(u); u ! 0+, and limu!0+ ‘(u) =  , if 0 <   1, it is said that the copula C has lower tail dependence and the limit  (if exists) is referred to the tail dependence parameter (some papers use “tail dependence coefficient” for the same meaning). The concept of tail dependence has been studied intensively in the literature, e.g., Joe (1997) and Schmidt (2002). As a special case of the usual tail dependence, the so-called tail comonotonicity corresponds to that the tail order  = 1 and the tail order parameter  = 1 as well. Tail comonotonicity is the strongest tail dependence structure. In situations where no sufficient data in the upper tail of certain joint distributions is available and the statistical inference of interest is aimed at the region beyond a very high threshold, which is often the situation that one meets in quantitative risk management, tail comonotonicity may provide a conservative dependence structure. When 1 <  < 2, we refer to this case as intermediate tail dependence, and the limit (if exists) of the slowly varying function ‘ is referred to as the tail order parameter. When  = 2 and the limit (if exists) of the slowly varying function ‘ is finite and nonzero, then we use the notion of tail quadrant independence (Reiss, 1989). For d-dimensional cases with  = d > 2, we employ the term of tail orthant independence. Note that, in the literature of the extreme value theory, terms of “asymptotic independence” or “tail independence” have often been used to represent the cases where  > 1. However, in this thesis, in order to discriminate the two cases of 1 <  < 2 and  = 2, we use the notation of “intermediate tail dependence” and 12“tail quadrant independence”, respectively. Intermediate tail dependence for the multivariate case corresponds to 1 <  < d. Upper tails can be dealt with similarly: the upper tail behavior of copula C(u; v) is simply the lower tail behavior of its survival copula bC(u; v) := u + v 1 +C(1 u; 1 v). If bC(u; u)  u ‘(u); u! 0+, then the  here is referred to as the upper tail order of the copula C. For any of the above cases, the limit of the slowly varying function ‘ can be referred to as the tail order parameter no matter it is the case of tail dependence, intermediate tail dependence or tail quadrant independence. For comparing the strength of dependence in the tails, we first compare tail orders that dominate the tail dependence pattern. If tail orders are the same, then tail order parameters can be used. Examples of lower and upper tail orders for commonly used Archimedean cop- ulas are listed in Table 1.1, and the contour plots of these copulas are given in Fig- ures 1.6, 1.7, 1.8, 1.9, 1.10 and 1.11. Note that, the first three examples of Gaussian, Student t and Frank copulas are all reflection symmetric; that is, the pattern of the upper tail is the same as the pattern of the lower tail. But tail asymmetry appears in the contour plots of the other three examples. When tail order  > 1, such as the tails of Gaussian copula and the lower tail of the Gumbel, the contour plots look more round than the contour plots for tails with  = 1. We refer to Balkema and Nolde (2010) for sufficient conditions of  > 1 from a geometric perspective. 13Table 1.1: Tail order of various bivariate copulas  of C(u; v)  of c(u; v) Copula cdf Lower Upper Lower Upper Gaussian      1(u);  1(v)  ;  : correlation matrix; : cdf of normal 21+% 2 1+%  2% 1+%  2% 1+% Student t T ;  T 1 (u); T  1  (v)  ; 0 <  ; : correlation matrix; T : cdf of Student t 1 1 -1 -1 Frank  log  [1 e   (1 e  u)(1 e  v)]=(1 e  )  = ; 0   2 2 0 0 Gumbel exp n   ( log u) + ( log v)  1= o ; 1   21= 1 21= -2 -1 MTCJ  u  + v   1   1= ; 0   1 2 -1 0 BB2 C = h 1 +   1 log  e (u    1) + e (v    1)  1  i 1= ; 0 <  ; 0 <  1 2 -1 0 14When the tail order for the copula density function is negative such as Gaussian copula (Table 1.1), the density at the boundaries (0; 0) and (1; 1) of the copula is infinity. The quantities of tail order and tail order parameters only capture the tail be- havior along the diagonal of the copula. We may use a function b to obtain more information about limiting behavior of copulas, namely, lim u!0+ C(uw1; uw2)=u  ‘(u) =: b(w1; w2); w1; w2 > 0: The b function is referred to as the tail order function of the copula C. For detailed study on tail order and tail order functions, we refer the reader to Chapter 3. Figure 1.6: Contour plots: Gaussian copula + standard normal margins ρ = 0  0.02  0.04  0.06  0.08   0.1  0.12 −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.2  0.02  0.04  0.06   0.08   0.1   0.12  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.4  0.02  0.04  0.06   0.08   0.1  0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.5  0.02  0.04  0.06   0.08   0.1  0.12  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.6  0.02  0.04  0.06   0.08  0.1   0.12  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.8  0.02  0.04  0.06   0.08   0.1   0.12   0.18  −2 −1 0 1 2 − 2 − 1 0 1 2 1.6.2 Tail order and conditional copula Now let us look at how tail order will affect conditional copulas of the form C1j2(ujv) as v goes to 0 or 1. We use C1j2(uj0) and C1j2(uj1) to represent these 15Figure 1.7: Contour plots: Student t copula + standard normal margins ρ = 0.4, df = 4  0.02  0.04  0.06  0.08   0.1  0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.6, df = 4  0.02  0.04  0.06  0.08   0.1  0.12   0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.8, df = 4  0.05  0.1  0.15   0.2  0.25  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.4, df = 2  0.02  0.04  0.06  0.08  0.1  0.12   0.14   0.16   0.2  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.6, df = 2  0.02  0.04  0.06   0.08   0.1  0.12   0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 ρ = 0.8, df = 2  0.05  0.1   0.15   0.2   0.25  −2 −1 0 1 2 − 2 − 1 0 1 2 Figure 1.8: Contour plots: Frank copula + standard normal margins δ = 0.2  0.02  0.04  0.06  0.08  0.1   0.12   0.14 −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 0.8  0.02  0.04  0.06  0.08  0.1   0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 1.6  0.02  0.04  0.06  0.08  0.1   0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 2  0.02  0.04  0.06  0.08  0.1  0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 4  0.02  0.04  0.06  0.08   0.1   0.12   0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 6  0.02  0.04  0.06  0.08   0.1   0.12   0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 16Figure 1.9: Contour plots: Gumbel copula + standard normal margins δ = 1.2  0.02  0.04  0.06  0.08  0.1  0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 1.4  0.02  0.04  0.06  0.08  0.1  0.12  0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 1.6  0.02  0.04  0.06  0.08  0.1   0.12  0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 1.8  0.02  0.04  0.06  0.08   0.1  0.12   0.14   0.16   0.18  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 2  0.02  0.04  0.06  0.08   0.1  0.12   0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 4  0.05  0.1   0.15   0.2   0.25   0.3  −2 −1 0 1 2 − 2 − 1 0 1 2 Figure 1.10: Contour plots: MTCJ copula + standard normal margins δ = 0.1  0.02  0.04  0.06  0.08  0.1   0.12  0.14 −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 0.2  0.02  0.04  0.06  0.08  0.1   0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 0.8  0.02  0.04  0.06  0.08   0.1   0.12  0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 1  0.02  0.04  0.06  0.08  0.1   0.12  0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 1.2  0.02  0.04   0.06  0.08   0.1   0.12  0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 δ = 1.4  0.02  0.04   0.06  0.08   0.1   0.12  0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 17Figure 1.11: Contour plots: BB2 copula + standard normal margins δ = 0.1, θ = 0.1  0.02  0.04  0.06   0.08   0.1  −4 −2 0 2 4 − 4 − 2 0 2 4 δ = 0.1, θ = 0.4  0.02  0.04  0.06   0.08  0.1  −4 −2 0 2 4 − 4 − 2 0 2 4 δ = 0.1, θ = 0.6  0.02  0.04  0.06   0.08  0.1   0.12  −4 −2 0 2 4 − 4 − 2 0 2 4 δ = 0.4, θ = 0.1  0.02  0.04  0.06   0.08   0.1  −4 −2 0 2 4 − 4 − 2 0 2 4 δ = 0.4, θ = 0.4  0.02  0.04  0.06   0.08  0.1  −4 −2 0 2 4 − 4 − 2 0 2 4 δ = 0.4, θ = 0.6  0.02  0.04   0.06   0.08  0.1   0.12  −4 −2 0 2 4 − 4 − 2 0 2 4 two limits, respectively. Explicit formulas for conditional Gaussian, Student t, Frank, Gumbel, MTCJ and BB2 copulas are presented in what follows. The limits for those conditional copulas are summarized in Table 1.2. For those examples, if 1   L < 2, then C1j2(uj0) =: &L with 0 < &L  1. The limit &L = 1 for such a  L, except for the conditional Student t copula of which &L is strictly less than 1. The case &L = 1 means that the limit of the conditional copula degenerates and becomes a single point at 0 as the conditioning random variable goes to 0. But for the Student t copula that has 0 < &L < 1, the limit of the conditional copula degenerates to be two points at u = 0 and u = 1, respectively, as the conditioning random variable V = v ! 0+. Looking at Figure 1.7, one may find that the bivariate Student t copula possesses tail dependence in all of the four directions, which explains why the limit of the conditional copula degenerates as two points. In parallel, for upper tails, if 1   U < 2, then the limit C1j2(uj1) degenerates to a single point at u = 1 except that for the Student t copula, it degenerates into two points at u = 0 and u = 1, respectively. If  L = 2, that is, if the copula is lower tail quadrant independent, then the limit 18of the conditional copula as V = v ! 0+ is still a non-degenerate cdf. Similarly, if  U = 2, then the limit is a non-degenerate cdf as well. In the sense of such a limit of conditional copulas, that 1   < 2 indicates a relatively higher degree of positive association in the tail. This is also a reason why we want to use different notions to discriminate intermediate tail dependence (1 <  < 2) and tail quadrant independence ( = 2), although in the literature of the extreme value theory, these two cases are both referred to as asymptotic independence or tail independence. Gaussian copula Let (X1; X2) be standard bivariate Normal with correlation %. Since X1jX2 = x  Normal(%x; 1 %2), the conditional Gaussian copula is C1j2(ujv) =     1(u) %  1(v) p 1 %2 ! : Thus C(uj0) = 1 for 0 < u  1, and C(uj1) = 0 for 0  u < 1. Student t copula Let (X1; X2) be standard bivariate Student t distributed with  the degree of free- dom and % the correlation coefficient in the correlation matrix. Since by (5.30) of McNeil et al. (2005), r  + 1  + x2 X2  %x p 1 %2      X1 = x  t +1(0; 1); the conditional Student t copula is C1j2(ujv) = T +1  s  + 1  + (T 1 (v))2  T 1 (u) %T  1  (v)p 1 %2 ! : Thus C1j2(uj0) = T +1  % p  + 1= p 1 %2  for 0 < u < 1, and C(uj1) = T +1   % p  + 1= p 1 %2  for 0 < u < 1. 19Frank copula The conditional Frank copula is straightforward to get, and it is C1j2(ujv) = (1 e  u)e  v 1 e   (1 e  v)(1 e  u) : Therefore, C1j2(uj0) = (1  e   u)=(1  e  ) for 0  u  1, and C1j2(uj1) = ((1 e  u)e  )=[(1 e  )e  u] for 0  u  1. Gumbel copula The conditional Gumbel copula is C1j2(ujv) = 1 v exp    ~u + ~v  1=  " 1 +  ~u ~v   # 1+1= ; where ~v :=  log v and ~u :=  log u. Thus, C1j2(uj0) = 1 for 0 < u  1, and C1j2(uj1) = 0 for 0  u < 1. MTCJ copula The conditional Mardia-Takahasi-Cook-Johnson copula (MTCJ) copula is C1j2(ujv) = (v   + u   1) 1=  1v   1; and C1j2(uj0) = 1 for 0 < u  1, and C1j2(uj1) = u 1+ for 0  u  1. BB2 copula The conditional BB2 copula is C1j2(ujv) = h 1 +   1 log  e (v    1) + e (u    1)  1  i 1=  1 e (v    1)v   1 e (v   1) + e (u   1)  1 : Thus, C1j2(uj0) = 1 for 0 < u  1, and C1j2(uj1) = u 1+ e (1 u   ) for 0  u  1. 20Table 1.2: Tail order and conditional copula Copula  L C1j2(uj0) Gaussian 2=(1 + %) 1 for 0 < u  1 Student t 1 T +1  % p  + 1= p 1 %2  for 0 < u < 1 Frank 2 (1 e  u)=(1 e  ) for 0  u  1 Gumbel 21= 1 for 0 < u  1 MTCJ 1 1 for 0 < u  1 BB2 1 1 for 0 < u  1 Copula  U C1j2(uj1) Gaussian 2=(1 + %) 0 for 0  u < 1 Student t 1 T +1   % p  + 1= p 1 %2  for 0 < u < 1 Frank 2 ((1 e  u)e  )=[(1 e  )e  u] for 0  u  1 Gumbel 1 0 for 0  u < 1 MTCJ 2 u1+ for 0  u  1 BB2 2 u1+ e (1 u   ) for 0  u  1 1.6.3 Tail order and Conditional Tail Expectation Two fundamental conditional specifications that are often useful in more specific modelings are E[X1jX2 > t] and E[X1jX2 = t]. In actuarial science, these two quantities are often referred to as certain forms of Conditional Tail Expectation, and they are useful as risk measures to assessing magnitude of losses. Moreover, the study of the tail behavior as t!1 of these two conditional specifications may also be meaningful when one needs to develop diagnostic plots for discriminating types of tail dependence, or formulate certain regression models that account for tail dependence patterns. The case: E[X1jX2 > t] Let X1; X2 be non-negative random variables with copula C and the same Pareto distribution F (x) = 1 (1 +x)  ;  > 1. Now we consider the effect of tail order 21on the following conditional specification. Letting v := F (t), E[X1jX2 > t] = (1 v) 1  1=(  1) + Z 1 0 f[ v + C(u; v)]  [f(F 1(u))] 1gdu  : (1.2) Similar to that we use regularly varying functions to describe the tail behavior of copula functions, we may assume that there exists a regularly varying function g(t) := t ‘(t) such that E[X1jX2 > t] = g(t) = t ‘(t); t!1: Then we can briefly discuss the following three cases: 1. For the usual tail dependence case,  = 1 with some regularity conditions. The derivation of this form can be conducted through the theory of MRV. 2. For the intermediate tail dependence case, with certain regularity conditions, 0 <  < 1 can be a function of the shape parameter  for the Pareto margins. 3. For the tail quadrant independence case, with some regularity conditions,  = 0. A special case is whenX1 andX2 are independent, then clearlyE[X1jX2 > t] = E[X1] is a constant and does not rely on t. In Figure 1.12, a comparison between Gumbel, s.Gumbel and MTCJ copulas is illustrated. Based on it, we find that when the upper tail order is 1 (Gumbel), the CTE plot seems to be linear in t on the whole support no matter what  is. When the upper tail order is 2 (MTCJ), the CTE becomes very flat. The CTE plots for the intermediate upper tail dependence with the upper tail order 1 <  < 2 (s.Gumbel) are located between the above two cases. The case: E[X1jX2 = t] Let X1; X2 be non-negative random variables with copula C and the same Pareto distribution F (x) = 1 (1+x)  ;  > 1. Then we compare the effect of tail order on the following conditional specification. Letting v := F (t), E[X1jX2 = t] = Z 1 0  1 C1j2(ujv)   [f(F 1(u))] 1du: 22Figure 1.12: Tail order and CTE plots of the form E[X1jX2 > t]. The range of t in each plot was chosen to cover the support of t between the 1% and 95% quantiles. Gumbel( = 2), s.Gumbel( = 2) and MTCJ( = 1). Pareto margins with θ = 1.5 t E( X 1  | X 2 > t ) 5 10 15 0 2 4 6 Gumbel s.Gumbel MTCJ Pareto margins with θ = 10 t E( X 1  | X 2 > t ) 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 Gumbel s.Gumbel MTCJ Figure 1.13: Tail order and CTE plots of the form E[X1jX2 = t]. The range of t in each plot was chosen to cover the support of t between the 1% and 95% quantiles. Gumbel( = 2), s.Gumbel( = 2) and MTCJ( = 1). Pareto margins with θ = 1.5 t E( X 1  | X 2 = t ) 0 1 2 3 4 5 6 0 2 4 6 Gumbel s.Gumbel MTCJ Pareto margins with θ = 10 t E( X 1  | X 2 = t ) 0.05 0.10 0.15 0.20 0.25 0.30 0.0 0.1 0.2 0.3 Gumbel s.Gumbel MTCJ 23From Figure 1.13, we can also observe the similar pattern of linearity for the Gum- bel copula, and that the line for MTCJ copula becomes flat. Those are just illustrations of the effect of tail order on Conditional Tail Expec- tation (CTE)s. In Chapter 5, more detailed study will be conducted for comparisons of CTEs under different dependence in the tails; in Chapter 7, more detailed study on such CTE plots are presented to illuminate future research on this topic. 1.6.4 Tail order and tail order functions In this subsection, we briefly introduce the concepts of tail order and tail order functions for the following three general copula families: extreme value copula, Archimedean copula and elliptical copula. More detailed study is in Chapter 3. Extreme value copula If a copula C satisfies C(ut1; : : : ; u t d) = C t(u1; : : : ; ud) for any (u1; : : : ; ud) 2 [0; 1]d and t > 0, then we refer to C as an extreme value copula. For any extreme value copula C, there exists a function A : [0;1)d ! [0;1) such that C(u1; : : : ; ud) = expf A( log u1; : : : ; log ud)g, whereA is convex, homogeneous of order 1 and satisfies max(x1; : : : ; xd)  A(x1; : : : ; xd)  x1 +    + xd. For the bivariate case, C(u; u) = expfA(1; 1) log ug = uA(1;1); and the lower tail order is  L = A(1; 1). Also, C(1  u; 1  u) = 2u  1 + (1  u)A(1;1) 1  [2 A(1; 1)]u; u! 0+. Therefore, the upper order is  U = 1. Tail order functions for extreme value copulas are relatively easy to get. For the lower tail, b(w1; w2) = limu!0+ C(uw1; uw2)=u A(1;1) = wA1(1;1)1 w A2(1;1) 2 , where Ai = @A(x1; x2)=@xi; i = 1; 2. For the upper tail, b (w1; w2) = w1 + w2  A(w1; w2). Archimedean copula The Archimedean copula studied in this thesis is constructed by a Laplace Trans- form (LT)  of a positive random variable; that is C(u1; : : : ; ud) =     1(u1) +    +   1(ud)  : 24This form covers most of the commonly used Archimedean copulas, and it has the following representation C(u1; : : : ; ud) = Z 1 0 dY j=1 G j (uj) dFH( ); where FH is the cdf of the resilience random variableH ,Gj(u) = expf   1(u)g (0  u  1) for all j, and (s) =  H(s) = R1 0 e  s dFH( ) with lims!1  (s) = 0. When  becomes larger, the density of the joint distribution Qd j=1G  j (uj) will be pushed to the right end of the support; as  goes to a smaller value, the density of the joint distribution Qd j=1G  j (uj) will be pushed towards the left end of the support. Therefore, the behavior of H at1 will affect the upper tail of the copula, and the behavior of H at 0 will affect the lower tail of the copula. For a positive random variable Y with LT  , if the maximal non-negative mo- ment MY := supfm  0 : E(Y m) < 1g is non-integer such that 1 < MY < d, then the upper tail order ofC is  U = MY under some mild regularity conditions; if 0  MY < 1, then the upper tail order of C is  U = 1. In other words, the behavior of  (u) as u ! 0+ influences the upper tail behavior of the copula C . The tail behavior of  (u) as u ! 1, however, will affect the lower tail behavior of the copula C . When tail order of an Archimedean copula has been obtained, we can get the corresponding tail order functions usually by applying the l’Hoˆpital’s rule. Elliptical copula An elliptical random vector X has the stochastic representation X d = RAU, where A is a deterministic matrix, and R  0 is independent of U and referred to as the radial random variable, and U is uniformly distributed on the surface of a unit ball. The tail behavior of R will affect the tail behavior of corresponding elliptical copula. If R is in the Maximum Domain of Attraction (MDA) of Fre´chet, then the copula has tail dependence, i.e.,  = 1; if R is in the MDA of Gumbel, then the tail order  > 1 and the value of  relies on both the tail behavior of R and the associated correlation coefficient %. 25Tail order functions of elliptical copulas for the first case (R 2 MDA(Fre´chet)) can be calculated directly by considering the above stochastic representation, but a general form for the second case remains to be an open question. We refer to Chapter 3 for more detailed arguments about tail order and tail order function for elliptical copulas. Inference for tail probability Tail order functions can be useful for statistical inference on multivariate extremal events. Let b be the upper tail order function for a copula C, and bC be the corre- sponding survival copula, then we have bC(uw)  u ‘(u)b (w); u ! 0+. Since the term u is tractable and dominates the tail behavior, the tail order function b (w) may approximate the tail behavior of the copula C. Tail order functions are limiting functions and it would be more robust than using the copula itself for statistical inference on tail events. 1.6.5 Concordance ordering in the tail Let X and Y be d-dimensional random vectors with distribution function FX and FY such that Xi d = Yi; i = 1; : : : ; d. Then X is less concordant than Y if FX(z)  FY(z) and FX(z)  FY(z) for any z in the support of FX and FY. The concordance order is an important concept in the theory of stochastic orders (Joe, 1990). That means small values of Y are more likely to occur together than those of X, and large values of Y are also more likely to occur together than those of X. If X is less concordant than Y, then the upper and lower tail orders of the copula of X are larger than those of the copula of Y, respectively. For comparisons between tails, especially between tails in a single direction, the concordance order is a relatively strong condition. Here we actually only need the conditions such as FX(z)  FY(z) holds ultimately as z is sufficiently small for comparing the lower tail, and FX(z)  FY(z) holds ultimately as z is suffi- ciently large for comparing the upper tail. 261.6.6 Sub-extremes and second order conditions In real applications of tail modeling, one often has interests not only in the limiting properties but also in the tail part beyond a high threshold. If the limit is referred to as an extreme, then the tail part beyond the high threshold will be referred to as sub-extremes. We use limiting properties – such as tail index for margins and tail order for dependence structures – as summary quantities for the tail, in hope that the limiting quantity may provide a reasonable approximation for the extreme and sub-extremal levels as well. Apparently, the speed of decay for the convergence involved to get the limiting quantity may influence the goodness of such an approximation. We will use second order conditions as a generic term to represent those conditions that may affect rates of certain convergence. For instance, in Chapter 6, we study second order conditions in the form of the so-called second order regular variation. For example, if g(x) := x 2 + x 3, then for any given t > 0, g(xt)=g(x) ! t 2 as x ! 1. Since g(xt)=g(x)  t 2 = (x + 1) 1(t 3  t 2), the term (x + 1) 1  x 1 dominates the speed of convergence of g(xt)=g(x)! t 2. We refer to  1 here as a second order parameter. When the second order parameter is more negative, the limiting quantity t 2 becomes a better approximation to g(xt)=g(x) for large x. 1.7 Outline This thesis is organized as following: In Chapter 2, motivations, definitions and related known results for some fundamental concepts and technical tools will be presented. Chapter 3 includes the study of using tail order to quantify the degree of positive tail association. Some relevant studies on multivariate copulas such as the Archimedean copula have also been done in this chapter. In Chapter 4, tail comonotonicity, the strongest tail dependence case will be studied for its funda- mental properties, constructions and influence on risk measures. Chapter 5 will be used to report the findings of how tail behavior of copulas affects commonly-used risk measures, and in particular, of the conservativity of tail comonotonicity. In Chapter 6, we will study how 2RV affects the performance of risk measures. At last, in Chapter 7, we will conclude the thesis and propose some relevant topics for future research. 271.8 Highlights of selected new results  A new concept called tail order has been proposed in Definition 3.1, and the corresponding tail order function is defined by Definition 3.2. A formula for deriving tail order of a bivariate elliptical copula where the radial random variable belongs to MDA(Gumbel) is given in Proposition 3.1. Some fun- damental properties for these two concepts are given in Proposition 3.2 and 3.3.  In order to study the tail behavior of the Archimedean copula, we first prove a result in Proposition 3.5 that relates the tail heaviness of a positive random variable to the Taylor expansion of its LT.  For the Archimedean copula, we relate the tail heaviness of a positive ran- dom variable to the tail behavior of the Archimedean copula constructed from the LT of the random variable, and extend the results of Charpentier and Segers (2009) for the upper tail in Proposition 3.6. And the correspond- ing upper tail order function has been proved in Proposition 3.7 as well. For the lower tail, we study a more concrete form by assuming some mild con- ditions, and the relevant result is Proposition 3.8.  A new notion of tail comonotonicity is proposed in Definition 4.1, and rele- vant results about their construction include Proposition 4.3 and Proposition 4.8.  Asymptotic additivity of risk measures Value at Risk (VaR) and CTE under tail comonotonicity is reported in Propositions 4.13, 4.14, 4.15 and 4.16.  Effect of tail behavior of copulas on E[X1jX2 > t] and E[X1jX2 = t] are mainly summarized in Propositions 5.1, 5.4 and 5.9, and their corollaries.  For the study of 2RV, results for asymptotic analysis of CTE and VaR has been reported in Proposition 6.4 for the univariate case, and in Proposition 6.6 for the multivariate case. 28Chapter 2 Preliminaries 2.1 Risk measures Some statistical quantities about a random variable X are often referred to as risk measures. Among many of them, Value at Risk (VaR) and conditional tail ex- pectation (CTE) are probably the most popular risk measures. Both of them have been adopted by regulations of insurers. For example, CTE has been required for calculating the relevant risks of segregated fund in Canada (OSFI, 2011). Definition 2.1 Let be given a random variable X the amount of risk, and p 2 (0; 1) a probability level, then the corresponding VaR, denoted by VaRp(X), is defined as VaRp(X) = F  1 X (p) := inffx 2 R : FX(x)  pg: If E[X] <1, then the corresponding CTE is defined as CTEp(X) = E[XjX > VaRp(X)]: (2.1) Note that the VaR defined above is a left-continuous generalized inverse of FX , and CTE is a coherent risk measure for continuous random variables (Artzner et al., 1999), and it can better account for tail heaviness of a loss distribution than VaR does. We refer to Denuit et al. (2005) for some other relevant risk measures and their relationships. 29Risk measures are used to help people make decisions through certain decision principles. For instance, one use risk measures to determine premiums of insurance products or calculate solvency capital charges. We refer to Goovaerts et al. (2010) for a comprehensive overview of risk measures and relevant decision principles. The risk measures mentioned in Definition 2.1 are both quantile-based risk measures. In this thesis, the emphasis will be risk measures for high-risk scenar- ios, for which p is close to 1. We will conduct asymptotic comparisons of risk measures in the sense that, when p is close to 1 what will be the ordering for those risk measures. For stochastic comparisons of risks, Mu¨ller and Stoyan (2002) is popular reference. Recently, Mainik and Ru¨schendorf (2012) conduct asymptotic comparisons for risks of portfolios, where a concept of asymptotic portfolio loss order is studied. A classic question in quantitative risk management is to study the so-called diversification benefit of a portfolio of risks. Definition 2.2 Let X := (X1; : : : ; Xd)T be a d-dimensional random vector. Then risk concentration $(XjQp) of X with respect to a risk measure Qp at level p 2 (0; 1) is defined as $(XjQp) := Qp(X1 +    +Xd) Pd i=1Qp(Xi) ; where Qp : R ! R+ is a risk measure associated with a probability level p. The corresponding diversification benefit is 1 $(XjQp). Here two relevant concepts are subadditivity and superadditivity of risk mea- sures. If $(XjQp)  1, then Qp is said to be subadditive with respect to X, and $(XjQp)  1 superadditive. It is well known that CTE defined in (2.1) is subad- ditive with respect to continuous margins, but VaR is not necessarily subadditive for such a general case (e.g., Denuit et al., 2005). In this thesis, we will consider asymptotic additivity of CTE and VaR in the sense that p! 1 in the risk concen- tration $(XjQp). 302.2 Copula The study of tail behavior of random vectors has received increasing attention, es- pecially in the framework of quantitative risk management. LetX = (X1; : : : ; Xd)T be a random vector with distribution function F and continuous univariate marginal distribution functions Fi, i = 1; : : : ; d. Due to Sklar’s theorem Sklar (1959), F (x1; : : : ; xd) = C(F1(x1); : : : ; Fd(xd)); (2.2) in which the copula function C : [0; 1]d ! [0; 1] is uniquely determined by C(u1; : : : ; ud) = F (F  1 1 (u1); : : : ; F  1 d (ud)); (2.3) where F 1i is the inverse function of Fi; i = 1; : : : ; d. The corresponding survival function C is defined as C(u1; : : : ; ud) = 1 + P ;6=I f1;:::;dg( 1) jIjCI(ui; i 2 I), where CI is the I-margin of the copula C with jIj the cardinality of the set I . There exist several related methodologies. The lower tail dependence parame- ter is defined as  L = lim u!0+ u 1C(u; : : : ; u); and the upper tail dependence parameter is defined similarly with the survival func- tion C. As extensions, Juri and Wu¨thrich (2002, 2003) studied tail dependence from a distributional point of view. Klu¨ppelberg et al. (2008) defined the so-called tail dependence function of X as  X(x1; : : : ; xd) = lim t!0 t 1P[1 F1(X1)  tx1; : : : ; 1 Fd(Xd)  txd]; and Nikoloulopoulos et al. (2009); Joe et al. (2010); Li and Sun (2009) further studied the properties of the tail dependence function and their applications for multivariate t copulas, vine copulas and heavy-tailed scale mixtures of multivariate distributions, respectively. We refer to the above papers for details and properties of tail dependence functions. Multivariate Archimedean copulas are widely used in insurance and financial risk analyses as they can be used for sensitivity analyses to changes in depen- 31dence, tail asymmetry, and lower/upper tail behavior. They are not flexible enough as models for high-dimensional data but can be extended to more flexible copula families (Joe and Hu, 1996) based on mixtures of max-infinitely divisible distribu- tions. In the literature, a d-dimensional Archimedean copula C is often (e.g., Gen- est and MacKay, 1986; Nelsen, 2006) defined as C(u1; : : : ; ud) =   1( (u1) +    +  (ud)), (u1; : : : ; ud) 2 [0; 1]d. McNeil and Nesˇlehova´ (2009) showed that d-monotonicity is a sufficient and necessary condition on the Archimedean gener- ator   1 so that the above form is a copula; a real-valued function g defined on (a; b) with a; b 2 R is said to be d-monotone (d  2) if it is differentiable up to the order d  2, ( 1)kg(k)(x)  0 for any x 2 (a; b) and k = 0; : : : ; d  2, and ( 1)d 2g(d 2) is non-increasing and convex in (a; b). To get a better understanding of tail dependence (intermediate or usual tail de- pendence), we use the mixture of power or LT representation in Marshall and Olkin (1988) and Joe (1997); for mixing distribution functions [resp. survival functions], the power is called resilience [resp. frailty] in Marshall and Olkin (2007). Let C (u1; : : : ; ud) =  (  1(u1) +    +   1(ud)); (u1; : : : ; ud) 2 [0; 1] d; (2.4) where  is the LT of a positive random variable. Note that as d  3 increases, Archimedean copulas extend less into the region of negative dependence (Joe, 1997, Sections 4.4 and 5.4) and hence the restriction to LTs does not lose much generality for defining an Archimedean copula. Since a LT is completely mono- tone, it can be used to construct copulas of any dimension. In this thesis, we assume that the function  is a LT of a positive random variable; that is,  (t) := R1 0 e  xtFX(dx), where FX is the distribution function of a positive random variable X . Such choices of  cover most of the commonly used Archimedean copulas. The tail behavior of a LT function plays a critical role in analyzing the de- pendence in the tails of the Archimedean copula constructed by the LT func- tion. Here we list basic properties of a LT  : [0;1) ! [1; 0) as follows:  (0) = 1; lims!1  (s) = 0,  is continuous and strictly decreasing, and  (s) is completely monotone; that is, the derivatives  (n) exist for n = 0; 1; 2; : : : and 32( 1)n (n)(s)  0 for all s  0. If C(u1; : : : ; ud) is a copula, then the corresponding survival copula is defined as bC(u1; : : : ; ud) = 1 + X I f1;:::;dg ( 1)jIjCI(1 ui; i 2 I); where CI is the I-margin of the copula C with jIj the cardinality of the set I; in particular, for bivariate case, bC(u; v) = u+ v  1 + C(1 u; 1 v). If C is an Archimedean copula constructed by LT  , then the corresponding survival copula is referred to as a survival Archimedean copula constructed by LT  . The survival copula itself is a copula, and it should be distinguished from the survival function of a copula (denoted as C); for bivariate case, the survival function of a copula C is C(u; v) = 1 u v + C(u; v). Definition 2.3 A copula C is said to be reflection symmetric if the copula is the same as its survival copula bC; that is, C(u)  bC(u) for any u 2 [0; 1]d. Another copula family that will be studied is extreme value copula. If a copula C satisfies C(ut1; : : : ; u t d) = C t(u1; : : : ; ud) for any (u1; : : : ; ud) 2 [0; 1]d and t > 0, then we refer to C as an extreme value copula. When we compare certain properties for different copulas, we usually assume that those copulas share some common summary quantities such as the following Blomqvist’s  (Blomqvist, 1950) for the bivariate case. Definition 2.4 The Blomqvist’s  for a bivariate copula C is defined as  := 4C(1=2; 1=2) 1. So  1    1;  = 0 holds for the independent copula and a larger positive  suggests a higher positive dependence. Moreover,  (C) =  ( bC) for the bivariate case. 33Standard references for copula theory are Joe (1997) and Nelsen (2006). For applications of copulas in actuarial science and quantitative risk management, we refer to Denuit et al. (2005), McNeil et al. (2005) and Genest et al. (2009a). 2.3 Regular variation For asymptotic analysis of tail behavior of random variables or equivalently their distribution functions, the theory of regular variation provides a powerful platform. Here we only give some most fundamental concepts. More relevant concepts and results about regular variation will be used throughout the thesis, and will be in- troduced when they are needed. Standard references on regular variation are Bing- ham et al. (1987), Resnick (1987), and Geluk and de Haan (1987); Embrechts et al. (1997) and Resnick (2007) are more relevant for applications in actuarial science, quantitative finance and risk management. Definition 2.5 A measurable function g : R+ ! R+ is regularly varying at 1 with index  6= 0 (written g 2 RV ) if for any t > 0, lim x!1 g(xt) g(x) = t : (2.5) If equation (2:5) holds with  = 0 for any t > 0, then g is said to be slowly varying at1 and written as g 2 RV0. For the lower limit at 0+, if for any t > 0, limx!0+ g(xt)=g(x) = t  , then g is regularly varying at 0+ and denoted by g 2 RV (0+). Note that g(t) 2 RV () g(1=t) 2 RV  (0+). Similarly, RV0(0+) is defined. We will use ‘(x) to represent a slowly varying function, and a regularly varying function g can be written as g(x) = x ‘(x). Note that, when we say that a random variable is regularly varying, it actually means that the survival function of the random variable is regularly varying. Definition 2.6 A measurable function g : R+ ! R+ is rapidly varying at1 with 34index1 (written g 2 RV1) if for any t > 0, lim x!1 g(xt) g(x) = t1 = 8 >< >: 0; if t < 1; 1; if t = 1; 1; if t > 1: Similarly, g 2 RV 1 if for any t > 0, lim x!1 g(xt) g(x) = t 1 = 8 >< >: 1; if t < 1; 1; if t = 1; 0; if t > 1: Proposition 2.1 If U 2 RV for  2 R, then lim x!1 U(tx)=U(x) = t ; locally uniformly in t on (0;1). If  < 0, then uniform convergence holds on intervals of the form (b;1); b > 0. If  > 0, uniform convergence holds on intervals (0; b] provided U is bounded on (0; b] for all b > 0. The following result is the famous Karamata’s theorem. It tells us that if we write some regularly varying function U(x) = x ‘(x) with ‘(x) slowly varying, then U(x) behaves like x in terms of integration. Theorem 2.2 Suppose    1 and U 2 RV . Then R x 0 U(t)dt 2 RV +1 and lim x!1 xU(x) R x 0 U(t)dt =  + 1: (2.6) If  <  1 (or if  =  1 and R1 x U(s)ds < 1), then U 2 RV implies thatR1 x U(t)dt is finite, R1 x U(t)dt 2 RV +1, and lim x!1 xU(x) R1 x U(t)dt =    1: (2.7) Definition 2.7 A function g : R! (0;1) is  -varying if it is non-decreasing and right-continuous, and there exists a measurable function h : R! (0;1) such that 35for any t 2 R lim x!1 g(x+ th(x)) g(x) = et; (2.8) where h( ) is called an auxiliary function. Definition 2.8 A measurable function g : R+ ! R is  -varying if there exits a function h : R+ ! R+ such that for all t > 0, lim x!1 g(tx) g(x) h(x) = log t; (2.9) where h( ) is called an auxiliary function. The following is a definition for MRV. Due to the limitation of space, we will not give more details about MRV here. For interested readers, Resnick (2007) is a nice reference. Definition 2.9 A random vector X  0 is MRV if there exists a Radon measure  (i.e., finite on compact sets) on E := [0;1] n f0g such that, lim t!1 P[X 2 tB] P[jjXjj > t] =  (B) for any relatively compact set B  E with  (@B) = 0, where jj  jj denotes a norm on Rd. 2.4 Maximum Domain of Attraction Definition 2.10 A random variable X is said to belong to the MDA of an extreme value distribution H if there exist normalizing constants  n > 0 and  n 2 R such that (Mn   n)= n d ! H; n!1; where Mn := X[1] is the first order statistics (i.e., maximum) of a random sample of X with the sample size being n. This is written as X 2 MDA(H). 36It is well known that there are only three non-degenerate univariate extreme value distributions: Fre´chet;Gumbel and Weibull. Since MDA(Weibull) corre- sponds to bounded random variables that are irrelevant to quantitative risk man- agement, we will only consider the first two cases. The following two theorems characterize the classes of MDA(Fre´chet) and MDA(Gumbel), respectively. Theorem 2.3 A random variable X with cdf F is said to belong to the Fre´chet maximum domain of attraction if and only if F 2 RV  ;  > 0, and the corre- sponding Fre´chet cdf is exp( x  ). Theorem 2.4 A random variable X with cdf F is said to belong to the Gumbel maximum domain of attraction if and only if there exists a positive auxiliary func- tion a( ) such that lim x!1 F (x+ ta(x)) F (x) = e t; t 2 R; (2.10) where a( ) can be chosen as a(x) = R1 x F (t)=F (x)dt. The concept of MDA(Gumbel) here is closely related to  -variation: if g(x) := 1=F (x) with F satisfying (2.10), then g(x) is non-decreasing and right-continuous, and satisfies the  -variation condition (2.8) with h( ) = a( ) the auxiliary function in (2.10). Note that, Marshall and Olkin (1983) has a characterization of MDA of a mul- tivariate extreme value distribution. This paper also shows that a random vector X that follows a multivariate extreme value distribution must be associated; that is, for any real-valued increasing functions g1; g2, E[g1(X)g2(X)]  E[g1(X)]E[g2(X)], provided that the expectations exist. 37Chapter 3 Tail order and intermediate tail dependence 3.1 Introduction For statistical modeling with copulas, properties such as strengths of upper/lower tail dependence and reflection symmetry or direction of reflection asymmetry are important in deciding on appropriate copulas. For example, for the tail asymmetry phenomena of financial markets (Patton, 2006; Okimoto, 2008), copula families with a variety of tail behavior are useful for statistical modeling. Although the multivariate Gaussian and t copula families have a wide range of dependence, they are not appropriate when there is reflection or tail asymmetry. But copulas can be constructed from other methods to get different joint tail behavior. Then for use of copulas for inference for joint tail probabilities, sensitivity analysis over different families can be performed. For the study of tail dependence behavior of random vectors, we not only have interest in the cases where the random vector is asymptotically dependent, but also where asymptotic independence exhibits. Ledford and Tawn (1996) proposed the following model for a bivariate random vector (X1; X2)T, where X1 and X2 are unit Fre´chet distributed with cdf Fi(x) = e 1=x; x  0; i = 1; 2, and are non- 38negatively associated, P[X1 > r;X2 > r]  ‘(r)r 1= ; r !1; (3.1) where 1=2    1. If we let Ui = Fi(Xi); i = 1; 2, where Fi is the cdf of the unit Fre´chet and r = ( log(u)) 1, then clearly lim u!1 P[U1 > u;U2 > u] (P[U1 > u]) = lim r!1 P[X1 > r;X2 > r] (P[X1 > r]) (3.2) = lim r!1 ‘(r)r 1= [1 exp( r 1)] = lim r!1 ‘(r)r 1= r  : Thus the “tail order”  that we will introduce in Definition 3.1 corresponds to 1= of Ledford and Tawn’s representation. If  = 1; i:e:;  = 1 and ‘(r) 9 0, X1 and X2 are upper tail dependent with upper tail dependence parameter  U = limr!1 ‘(r); if 1=2 <  < 1, they are positively dependent; if  = 1=2 and ‘(r)  1, they are “tail quadrant independence”. A lot of research has been done following this direction. We refer to Ledford and Tawn (1996, 1997); Coles et al. (1999); Heffernan (2000); Ramos and Ledford (2009) for further development of this idea. The relation (3.1) tells us that the power term 1= dominates the speed of decay of the joint tail probability. We believe that the parameter 1= plays an important role in the study of tail dependence behavior, and deserves a new name “tail order” that is explained in Section 3.2.1, based on copula functions. Moreover, analo- gously to the tail dependence function, we will propose the tail order function, which includes the information of the convergence along routes other than the di- agonal. In this chapter, the emphasis is on the case where the tail order is between 1 and d for a d-dimensional random vector. We refer to this case as “intermediate tail dependence” under some positive dependence assumptions; this is explained before Example 3.1. Our main contributions span the following aspects: 1. We propose the concepts of tail order and tail order functions as an integrated way to study tail behavior of multivariate copulas. 2. We relate the tail heaviness of a positive random variable 39to the tail behavior of the Archimedean copula constructed by the LT of the random variable. In our opinion, it is an insightful way to better understand the tail behavior of Archimedean copulas. 3. Our theoretical study of tail behavior of Archimedean copulas leads to a new one-parameter Archimedean copula family, based on the LT of the inverse Gamma distribution, which shows patterns of upper and lower tails not seen in commonly used copula families. The remainder of this chapter is organized as follows. Section 3.2 introduces the concepts of tail order and tail order functions, and some properties of them. In particular, some results on relations of tail orders of marginal copulas are given. Sections 3.3 and 3.4 contain studies of intermediate tail dependence for Archimedean copulas and copulas constructed by mixture of max-id distributions, respectively. For multivariate Archimedean copulas, we have a more concrete result than Char- pentier and Segers (2009) for the lower tail, and new results for the upper tail. Asymptotic behavior of LTs of positive random variables is studied in Section 3.3.1, and the new Archimedean copula family is presented in Section 3.3.4. Fi- nally, Section 3.5 concludes with some topics of further research. The main proofs are put in Section 3.6. 3.2 Tail orders: definitions and properties In this section, we define the concepts of tail order and tail order functions, indicate their use for reflection asymmetry and derive some properties. 3.2.1 Multivariate tail order and tail order functions To avoid technicalities for tail orders, we assume conditions involving regular vari- ation of tails of copula and other functions. Definition 3.1 SupposeC is a d-dimensional copula. If there exists some  L(C) > 0 such that, with some ‘(u) 2 RV0(0+) C(u1d)  u  L(C)‘(u); u! 0+; then we refer to  L(C) as the lower tail order ofC and refer to  L(C) = limu!0+ ‘(u) as the lower tail order parameter, provided the limit exists. Similarly, the upper 40tail order is defined as  U (C) such that C((1 u)1d)  u  U (C)‘(u); u! 0+; with the upper tail order parameter  U (C) = limu!0+ ‘(u), provided the limit exists. When no confusion arises, we use the notation  to represent lower or upper tail orders, and  for tail order parameters.  L(C) = 1 [resp.  U (C) = 1] and ‘(u) 9 0 corresponds to the usual definition of upper [resp. lower] tail dependence. We will assume that lims!0+ ‘(s) = h 2 [0;1]. But h = 0 or h = 1 correspond to boundary cases, in which case more care is needed. In these boundary cases, the “speed” of decrease or increase of ‘(u) affects the tail dependence behavior. For example, if ‘(u) ! 0, then a lower speed indicates a stronger tail dependence; if ‘(u) ! +1, then a higher speed indicates a stronger tail dependence. Note that with ‘(u)! h, if  (C) = 1 then 0  h  1; if  (C) > 1 then 0  h  1. Note also that  L(C) =  U (C) = d for the d-dimensional independence copula. It is not possible for  < 1 (refer to Proposition 3.3), but it is possible for  L(C) and  U (C) to be greater than d for copulas with negative dependence. For example, as a boundary case, for the bivariate counter-monotonic copula,  L(C) and  U (C) can be considered as +1 because C(u; u) and C(1  u; 1  u) are zero for 0 < u < 1=2. The cases of  = 1 or d have been well studied in the literature, while not much research exists for 1 <  < d. For the bivariate case, 1 <  < 2 represents some level of positive dependence in the tail, but not as strong as tail dependence. For multivariate cases, without any further conditions, the meaning of 1 <  < d is complicated. We refer to the case 1 <  < d as lower [resp. upper] intermediate tail dependence only when all marginal copulas (ultimately) possess positive lower [resp. upper] orthant dependence, of which a formal definition will be given in Definition 3.3. Unless otherwise specified, when a copula is said to possess inter- mediate tail dependence, the orthant dependence condition is assumed implicitly. The following is an example of intermediate tail dependence for Gaussian copulas. 41Example 3.1 (Gaussian copula) Consider a multivariate Gaussian copula, con- structed by C d(u1; : : : ; ud) =  d(  1(u1); : : : ;  1(ud);  ); (3.3) where  d( ;  ) is the joint cdf of a standard d-variate Gaussian random vector with positive definite correlation matrix  . The multivariate Gaussian copula defined in (3:3) has intermediate tail dependence with the tail order  = 1d  11Td , the sum of all elements of   1. It can be verified by noticing that limu!0+ C d(u1d)=u  = limt! 1 d(t1d)=[ (t)] , and as t!  1,  (t)   (t)=jtj and  d(t1d) is dom- inated by the exponent term exp( t21d  11Td =2) (Hashorva and Hu¨sler, 2003, Corollary 4.1). The bivariate Gaussian copula with % > 0 has intermediate tail dependence with the tail order  = 2=(1 + %) and the slowly varying function at 0+ being ‘(u) = ( log u) %=(1+%). A related result without using copula functions has been given in Ledford and Tawn (1996). For dimension d with constant correlation %, the tail order is  = d=[1+(d 1)%]. For the trivariate case with %12 = %23 = %, we have  = [3 + %13  4%]=[1 + %13  2%2]. Gaussian copulas are reflection symmetric and have intermediate tail depen- dence when correlations are positive. They are a subfamily of the elliptical cop- ulas. Under some regularity conditions, tail orders of elliptical copulas will be determined by the tail behavior of corresponding radial random variable R. Since copula is invariant to a strict increasing transformation on margins, for the study of elliptical copula, we may omit the location and scale parameters of joint elliptical distributions, and consider the following representation: let X := (X1; X2) be an elliptical random vector such that X d = RAU; (3.4) where the radial random variable R  0 is independent of U, U is an bivariate random vector uniformly distributed on the surface of the unit hypersphere fz 2 RkjzTz = 1g, A is a 2  2 matrix such that AAT =  where  11 =  22 = 1 42and  12 =  21 = % with  1 < % < 1, eg., A =  1 0 % p 1 %2 ! . For such an elliptical distribution, the margins have the same distribution assumed to be F . The tail behavior of R may influence the tail order of elliptical copulas. For the usual tail dependence case, Schmidt (2002) proved that when the radial random variable R has a regularly varying tail then X1 and X2 are tail dependent, and thus the tail order of the corresponding elliptical copula is  = 1. Example 3.2 (Student t copula) The radial random variable R for Student t distri- butions is a generalized inverse Gamma distribution such thatR2 follows an inverse Gamma distribution with the shape and scale parameters being  =2, where  is the degree of freedom. It can be verified that FR 2 RV  (see Example 6.4). So the tail order for Student t copula is  = 1. For the case where R has lighter tails than any regularly varying tails, some asymptotic study has been conducted for elliptical distributions where R belongs to the Gumbel Maximum Domain of Attraction. We refer to Hashorva (2007), Hashorva (2010) and Hashorva (2008) for relevant references. Here we are ready to present a result that is useful to find the tail order of a bivariate elliptical copula where the radial random variable R belongs to Gumbel MDA. Proposition 3.1 LetC be the copula for an elliptical random vectorX := (X1; X2) constructed as (3.4), and b% = p 2=(1 + %). If R 2 MDA(Gumbel), then the up- per and lower tail orders of C is  = lim r!1 log (1 FR(b%r)) log (1 FR(r)) ; (3.5) provided the limit exists. This result is very convenient for us to derive the tail order if we know the tail behavior of R. Example 3.3 (Bivariate symmetric Kotz type (Fang et al., 1990) copula) The den- 43sity generator g(x) = KxN 1 expf  x g;  ;  ;N > 0; where K is a normalizing constant. By Theorem 2.9 of Fang et al. (1990), the density function of R is fR(x) = 2 xg(x 2) = 2K x2N 1 expf  x2 g: So, the survival function is 1 FR(x) = Z 1 x 2K t2N 1 expf  t2 gdt = Z 1  x2 K    N= wN=  1 expf wgdw = K    N=  (N= ;  x2 );  ( ;  ) incomplete Gamma function  K    1x2N 2 expf  x2 g; x!1: Then by (3.5), we can easily get that  = b2 % = [2=(1 + %)]  : Therefore, the tail order for the symmetric Kotz type copula is  = [2=(1 + %)] . Gaussian copula belongs to this class with  = 1, so its tail order is 2=(1 + %) which is consistent to what we have obtained. Definition 3.2 Suppose C is a d-dimensional copula and C(u1d)  u ‘(u), u! 0+ for some ‘(u) 2 RV0(0+). The lower tail order function b : Rd+ ! R+ is defined as b(w;C;  ) = lim u!0+ C(uwj ; 1  j  d) u ‘(u) ; provided the limit function exists. In parallel, if C((1 u)1d)  u ‘(u), u! 0+ for some ‘(u) 2 RV0(0+), the upper tail order function b : Rd+ ! R+ is defined 44as b (w;C;  ) = lim u!0+ C(1 uwj ; 1  j  d) u ‘(u) ; provided the limit function exists. If ‘(u) ! h 6= 0, then hb(w;C; 1) and hb (w;C; 1) become the tail dependence functions in Joe et al. (2010). Note that the copula C that satisfies the conditions of the above definition is said to be multivariate regularly varying with a limit function b or b (Resnick, 2007). Although the general theory and definitions must accommodate an arbi- trary slowly varying function ‘, in specific parametric families of copulas that have tractable forms, we find that either ‘(u) is a constant or proportional to a power of ( log u). Example 3.4 (Extreme value copula) For any multivariate extreme value copula CEV , there exists a function A : [0;1)d ! [0;1) such that CEV (u1; : : : ; ud) = expf A( log u1; : : : ; log ud)g; where A is convex, homogeneous of order 1 and satisfies max(x1; : : : ; xd)  A(x1; : : : ; xd)  x1 +    + xd: We refer to Chapter 6 of Joe (1997) for details of multivariate extreme value copu- las. Thus, CEV (u1d) = expfA(1d) log ug = u A(1d): That is, for any extreme value copula CEV , the lower tail order is  L(CEV ) = A(1d) and there is intermediate lower tail dependence except for the boundary cases such as independence copula and comonotonicity copula, where A(1d) = d and 1, respectively. In order to get the lower tail order function of extreme value copulas, first 45consider the bivariate case, for which CEV (uw1; uw2) = expf A( log uw1; log uw2)g = exp  (log u)A  1 + logw1 log u ; 1 + logw2 log u    exp  (log u)  A(1; 1) +A1(1; 1)  logw1 log u  +A2(1; 1)  logw2 log u    ; u! 0+ = uA(1;1)wA1(1;1)1 w A2(1;1) 2 ; whereAi = @A=@xi, i = 1; 2. Therefore, the lower tail order function is b(w1; w2) = wA1(1;1)1 w A2(1;1) 2 . Similarly, for a d-variate extreme value copula, b(w1; : : : ; wd) = wA1(1d)1 : : : w Ad(1d) d . By Euler’s formula for homogeneous functions, A(1d) = Pd i=1Ai(1d). Then it can be verified that b is homogeneous of order A(1d). In the bivariate case,  U = 1,  U = 2  A(1; 1), and  L = A(1; 1). That is, a larger value of the upper tail dependence parameter implies stronger lower intermediate tail dependence. Example 3.5 (Elliptical copula) Tail order functions of bivariate elliptical copulas can be derived as following: for any given 0 < w1; w2 < 1, as 0 < v < 1 is sufficiently close to 0, C(1 vw1; 1 vw2) = P[F (X1)  1 vw1; F (X2)  1 vw2] = P[X1  F 1(1 vw1); X2  F 1(1 vw2)] = P[R cos’  F 1(1 vw1); R[% cos’+ p 1 %2 sin’]  F 1(1 vw2)] = 1 2 Z  2 1 P " R  F 1(1 vw1) cos  ;R  F 1(1 vw2) % cos  + p 1 %2 sin  # d ; where’  Uniform(  ;  ) and  1 := (  =2;  =2)\f : tan    %= p 1 %2g. 46Also, letting  2 := (  =2;  =2), as 0 < v < 1 is sufficiently close to 0 v = P  R cos’  F 1(1 v)  = 1 2 Z  2 2 P  R  F 1(1 v) cos   d : If R 2 MDA(Fre´chet), i.e., R 2 RV  for some  > 0, then the margins X1; X2 2 RV  . Therefore, the map v 7! F 1(1  v) 2 RV 1= (0 +). Then, by the uniform convergence of regularly varying functions (e.g., Proposition 2.4 of Resnick (2007)) lim v!0+ C(1 vw1; 1 vw2) v = lim v!0+ R  2 1 P  R  F  1(1 vw1) cos  _ F 1(1 vw2) % cos  + p 1 %2 sin   d =P  R > F 1(1 v)  R  2 2 P h R  F  1(1 v) cos  i d =P [R > F 1(1 v)] = lim v!0+ R  2 1 P  R  w  1= 1 F  1(1 v) cos  _ w 1= 2 F  1(1 v) % cos  + p 1 %2 sin   d =P  R > F 1(1 v)  R  2 2 P h R  F  1(1 v) cos  i d =P [R > F 1(1 v)] = R  2 1 max n w1(cos  ) ; w2(% cos  + p 1 %2 sin  ) o d R  2 2 (cos  ) d : The multivariate case is similar (Klu¨ppelberg et al., 2008). However, if R 2 MDA(Gumbel), a general form of tail order functions of the whole class remains unsolved. We next mention how the upper and lower tail orders are useful to establish the direction of reflection asymmetry. Let bC be the copula of (1  U1; : : : ; 1  Ud) when the copula of (U1; : : : ; Ud) is C, where Ui’s are standard uniform variables. Reflection symmetry means that bC  C (Definition 2.3) and otherwise we say that there is reflection asymmetry. If C(u1d)  bC(u1d) for all 0 < u < u0, for some 0 < u0  1=2, then the copula has more probability in the lower tail (reflection asymmetry with skewness to lower tail). If the inequality is reversed leading to C(u1d)  bC(u1d), then the copula has more probability in the upper tail (reflec- tion asymmetry with skewness to upper tail). For most existing parametric families 47of copulas, it is difficult to analytically compare C(u1d) and bC(u1d), so the di- rection of reflection asymmetry is analytically easier via the upper and lower tail orders. For example, if  L(C) >  U (C), then C has reflection asymmetry skewed to the upper tail (smaller  means slower convergence to 0), and ifC(u1d)   Lu and bC(u1d)   Uu as u ! 0+ with  L >  U > 0, then C has reflection asym- metry skewed to the lower tail. For many parametric copula families where we have done numerical computations, u0 can be taken as 1=2. The following are some elementary properties of the lower and upper tail or- der functions b and b . Obvious properties of tail order for bC are the following:  L(C) =  U ( bC),  U (C) =  L( bC), b(w;C;  ) = b (w; bC;  ) and b (w;C;  ) = b(w; bC;  ). Proposition 3.2 A lower tail order function b(w) = b(w;C;  ) has following properties: 1. b(1)  1, and b(w) = 0 if there exists an i 2 Id with wi = 0; 2. b(w) is increasing in wi; i 2 Id; 3. for any fixed t > 0, b(tw) = lim u!0+ C(tuwj ; 1  j  d) u ‘(u) = t lim u!0+ C(tuwj ; 1  j  d) (tu) ‘(tu) = t b(w): (3.6) Thus, b(w) is homogeneous of order  . If b(w) is partially differentiable with respect to each wi on (0;+1), then by the Euler’s formula on homogeneous functions, we can write b(w) = 1  dX j=1 @b @wj wj ; 8w 2 Rd+: Remark 3.1 Since C(uw)  u ‘(u)b(w) = b(uw)‘(u), u ! 0+, the tail order function b captures the tail behavior of the copula C in different directions. 483.2.2 Further properties of tail orders In this subsection, we obtain some general properties of tail orders of multivari- ate copulas, especially on inequalities on tail orders of marginal copulas. There is an “obvious” property in terms of concordance. For two joint cdfs H;G 2 R(F1; : : : ; Fd), we say that H is less concordant than G, if H(x)  G(x) and H(x)  G(x) for any x in the support of H and G. If C1 is less concordant than C2, then  L(C1)   L(C2) and  U (C1)   U (C2). Next we introduce some concepts of positive dependence, under which multi- variate copulas may have some particular properties on tail orders. We refer to Joe (1997), Colangelo et al. (2005) for details. Definition 3.3 Suppose that F (x) is the cdf of a d-variate random vector X = (X1; : : : ; Xd)T, then X or F is said to be 1. positive lower orthant dependent (PLOD) if P[Xi  xi;8i 2 Id]  Qd i=1 P[Xi  xi] for any x 2 Rd; 2. left tail decreasing in sequence (LTDS) if P[Xi  xijX1  x1; : : : ; Xi 1  xi 1] is decreasing in x1; : : : ; xi 1 for all xi; i 2 f2; : : : ; dg; 3. multivariate left tail decreasing (MLTD) if (Xi1 ; : : : ; Xid) is LTDS for all permutation (i1; : : : ; id) of (1; : : : ; d). Proposition 3.3 Suppose a multivariate copula C(u1; : : : ; ud) has a lower tail order  L(C), then  L(C)  1. Moreover, 1. if C is (ultimately) positive lower orthant dependent (PLOD), then  L(C)  d; 2. for any S1  S2  Id with jS1j  2,  L(CS2)  L(CS1)  0. In particular, if  L(C) = 1, then for any S  Id with jSj  2,  L(CS) = 1; if C is multivariate left tail decreasing (MLTD), then  L(CS2)   L(CS1)  jS2j  jS1j. Analogous results hold with  L replaced by  U , and conditions of positive upper orthant dependence and multivariate right tail increasing. 49Remark 3.2 The above result says that when some regularity condition holds, marginality will keep the order of tail orders in the sense that margins have smaller tail orders. However, marginality does not inherit the inequality between tail or- ders of lower and upper tails. For example, take the trivariate Archimedean copula with the  function in Example 3.6 in Section 3.3.1 below. Then 3 > 1 +  for 0 <  < 1 so that  L(C) >  U (C) (see Table 3.1). But 2 < 1+ for 0 <  < 1 so that for the bivariate margins,  L(CI) <  U (CI) with jIj = 2. Sometimes partial derivatives and the density have a simpler form than the copula cdf. We hope to know what tail properties will be inherited if we take partial derivatives of the copula. For example, for the lower tail, if C(uw1; : : : ; uwd)  u  ‘(u)b(w1; :::; wd); u! 0 +; then we want to differentiate both sides of the above with respect to the wj’s to get: u @C(uw1; : : : ; uwd) @wj  u ‘(u) @b(w1; :::; wd) @wj ; u! 0+; and higher order derivatives up to: ud @dC(uw1; : : : ; uwd) @w1    @wd  u ‘(u) @db(w1; :::; wd) @w1    @wd ; u! 0+: A sufficient condition is ultimate monotonicity of partial derivatives of the copula (eg: @C=@uj is ultimately monotone in uj at 0+, and similar conditions are suf- ficient for higher orders). A proof is similar to that in Theorem 1.7.2 (Monotone density theorem) in Bingham et al. (1987). As an example of using the density to get the tail order, consider a multi- variate Gaussian copula with positive definite correlation matrix  which satis- fies C (u1d)  u ‘(u) = u ( log u) , u ! 0+. Then (as can be shown di- rectly with the monotone density theorem), this would be equivalent to c (u1d)  hu  d( log u) , u! 0+, where h is a constant. Thus, with  d for the multivari- 50ate Gaussian density, 1 = lim u!0+ c (u1d) hu  d( log u) = lim u!0+  d    1(u)1d;    d(  1(u))u  d( log u) h = lim z! 1  d (z1d;  )  d(z)[ (z)]  d[ log( (z))] h = lim z! 1  d (z1d;  )   (z)jzjd  [ log( (z)=jzj)] h (3.7) Since the exponent terms dominate the numerator and denominator of (3:7), to cancel the exponent terms, a necessary condition is that  = 1d  11Td , which turns out to be the tail order of the copula C . Also, to cancel the term of jzj in (3.7), we need that d  + 2 = 0, so  = (  d)=2. 3.3 Intermediate tail dependence : Archimedean copulas Archimedean copulas are reflection asymmetric except for the bivariate Frank cop- ula, and have a variety of tail behavior. In this section, we will study the up- per/lower tail orders and tail order functions for Archimedean copulas. A new family of one-parameter Archimedean copulas, that interpolates independence and comonotonicity, will be given. This copula possesses intermediate upper and lower tail dependence, and has patterns of tail orders different from existing parametric families. Before getting to the main results, we provide some intuition on conditions on  for intermediate upper and lower tail dependence for C . Let G1; : : : ; Gd be univariate cdfs. For  > 0, G  1; : : : ; G  d are cdfs, and  is called a resilience parameter. As the parameter  ! 0, then random vari- ables with distributions G 1; : : : ; G  d tend towards the lower endpoint of support of G1; : : : ; Gd, and as  ! 1, random variables with distributions G  1; : : : ; G  d tend towards the upper endpoint of support of G1; : : : ; Gd. There is also a parallel for survival functions and frailty, where the conclusions are reversed when the frailty parameter goes to 0 or1. In this way, an Archimedean copula C has a mixture representation with LT  . That is, C (u1; : : : ; ud) = R1 0 Qd j=1G  j (uj) dFH( ), where FH is the cdf of the resilience random variable H , G(u) = expf   1(u)g (0  u  1), and 51 (s) =  H(s) = R1 0 e  s dFH( ). The mixture means that: there are random variables X1; : : : ; Xd such that given H =  , they are conditionally independent with respective cdfs G 1; : : : ; G  d. If the random variable H has heavy tail at 1, then there is a “chance” that H =  is large and hence conditionally, X1; : : : ; Xd are all close to their upper endpoints of support (i.e., dependence in the upper tail). Hence conditions on the heaviness of the upper tail of the distribution of H lead to intermediate upper tail dependence. For the opposite tail, if the random variable H has concentration of density near 0, then there is a “chance” that H =  is near zero and hence conditionally, X1; : : : ; Xd are all close to their lower endpoints of support (i.e., dependence in the lower tail). Hence conditions on the density of the lower tail of the distribution of H lead to intermediate lower tail dependence. 3.3.1 Laplace transform and univariate tail heaviness In this subsection, we relate the asymptotic behavior of a LT to the maximal mo- ment of the positive random variable with the given LT. Definition 3.4 For a positive random variable Y with LT  , the maximal non- negative moment is MY = M = supfm  0 : E(Y m) <1g: (3.8) MY is 0 if no moments exist and MY is1 if all moments exist. A smaller value of MY means that Y has a heavier tail at1. The next lemma shows that M is related to the behavior of  at 0 when 0 < M < 1. The result for a general non-integer M such that k < M < k + 1 will be derived subsequently. Lemma 3.4 Suppose  (s) is the LT of a positive random variable Y , with 0 < MY < 1. If 1  (s) is regularly varying at 0+, then 1  (s) 2 RVMY (0 +). Remark 3.3 Even if E(Y ) = 1, we may also have MY = 1. However, Lemma 3.4 does not hold in general for this case. Remark 3.4 If we write 1  (s) = sM‘(s) and ‘(s)! h1 with 0 < h1 <1 as s! 0+, then clearly  (s) = 1 h1sM + o(sM ), s! 0+. 52Proposition 3.5 Suppose  (s) is the LT of a positive random variable Y , with k < MY < k+ 1 where k 2 f0g[N+. If    (k)(0)  (k)(s)   is regularly varying at 0+, then    (k)(0)  (k)(s)   2 RVMY  k(0 +). In particular, if the slowly varying component is ‘(s) and lims!0+ ‘(s) = h 0 k+1 with 0 < h 0 k+1 <1, then s! 0 +,  (s) = 1 h1s+ h2s 2     + ( 1)khks k + ( 1)k+1hk+1s MY + o(sMY ); (3.9) where 0 < hi <1 for i = 1; : : : ; k + 1. The above results can be summarized as follows. If M =1, the LT  (s) has an infinite Taylor expansion about s = 0. If M is finite and non-integer-valued, then with some regularity conditions,  (s) has a Taylor expansion about s = 0 up to order [M ], and the next term after this has order M . 3.3.2 Upper tail Based on the results in Section 3.3.1, we derive upper tail orders and corresponding tail order functions of multivariate Archimedean copulas; the results extend those of Charpentier and Segers (2009). Proposition 3.6 Let  be the LT of a positive random variable and assume that  satisfies the condition of Proposition 3.5. Assume that k < M < k + 1 with some k 2 f1; : : : ; d  1g, then the Archimedean copula C in (2.4) has upper intermediate tail dependence. The corresponding tail order is  U = M . If  (i)(0) is finite for all i 2 Id, then the upper tail order  U = d. If  0(0) is infinite and 0 < M < 1, then the upper tail order is  U = 1, and particularly for the bivariate case,  U = 2 2M . Remark 3.5 If we know the value of a in (3.22) in the proof of Proposition 3.6, then the tail order parameter is lim u!1 C (u; u)=(1 u) M = 2a[  0(0)] 1 M (2M  1)=(1 +M) = a[  0(0)] M (2M  2)=M : 53Example 3.6 Consider the LT of Example 4.2 in Joe and Ma (2000) with param- eter 0 <  < 1 (see Joe-Ma in Table 1). We refer to this as the normalized integral of the positive stable LT. Note that m =  0(0) =  1= (1 +   1) is finite,  00(0) = 1 and  (s)  1  s= (1 +   1) as s ! 0+. We can write  (s) = 1 +  0(0)s + o(s); s ! 0+. Let g(s) =  0(s)   0(0) = (1  expf s g)= (1 +   1)  s = (1 +   1); s ! 0+. Then clearly, g(s) 2 RV (0+) and can be written as g(s) = s ‘(s) with ‘(s) ! 1= (1 +   1) as s ! 0+. So g(s) = as + o(s ); s ! 0+, with a = 1= (1 +   1) > 0. By Proposition 3.6, the copula C has intermediate upper tail dependence when 0 <  < 1. Also,  U = 1 +  and lim u!0+ C (1 u; 1 u) u1+ = 2[ (1 +   1)] (2  1) 1 +  : It can be shown numerically that the d-variate Archimedean copula with this 1- parameter LT family is decreasing in concordance as  increases. As  ! 1 , numerically, the limit is close to the independence copula; as  ! 0+, the limit is close to the comonotonic copula. In the next proposition, we state a result for the upper tail order function of Archimedean copulas. Proposition 3.7 Let C be a multivariate Archimedean copula with  U = M being a non-integer in the interval (1; d), and suppose  satisfies the condition of Proposition 3.5. With the notation M = M  [M ] and k = [M ], the upper tail order parameter is  U (C ) = Mh [  0(0)]M Qk j=0(M  j) X ;6=I Id ( 1)jIj+k+1jIjM ; and the upper tail order function is b (w) = P ;6=I Id ( 1)jIj  P i2I wi  M P ;6=I Id ( 1)jIj   I   M ; where h = lims!0+ ‘(s) with j (k)(s)  (k)(0)j = sM‘(s) as s! 0+. 54Remark 3.6 For a d-variate Archimedean copula, the pattern of the upper tail or- der function also depends on the upper tail order  . For example, in d = 3, the homogeneous function b is positively proportional to  w 1 w  2 w  3 +(w1+w2)  +(w1+w3)  +(w2+w3)   (w1+w2+w3)  ; 1 <  < 2; w 1 +w  2 +w  3 (w1+w2)   (w1+w3)   (w2+w3)  +(w1+w2+w3)  ; 2 <  < 3: The signs of all terms depend on whether 1 <  < 2 or 2 <  < 3. The pattern of alternating signs extends to d > 3. This pattern, together with Lemma 3.12, also shows why we don’t have a general form of the tail order function when M is a positive integer. Recently, we have noticed that Larsson and Nesˇlehova´ (2011) uses survival copulas for a random vector X := (X1; : : : ; Xd) d = RSd as another representation for Archimedean copulas, where R is a radial random variable, the random vector Sd is uniformly distributed on the simplex f(x1; : : : ; xd) 2 Rd+ : Pd i=1 xi = 1g, and R and Sd are independent. In parallel to elliptical copulas that are constructed based on random vectors that have a similar scale mixture representation, the au- thors find that the tail behavior of the radial random variable R will affect the tail dependence patterns of corresponding Archimedean copulas. This representation provides an alternative way to study the dependence in the tails of Archimedean copulas. 3.3.3 Lower tail For intermediate lower tail dependence of Archimedean copulas, a general result has been obtained in Theorem 3.3 of Charpentier and Segers (2009). We will derive a more concrete and usable result that involves the slowly varying function ‘, and give an interpretation in terms of the (resilience) random variable H which has LT  . The condition below on the LT  (s) as s ! 1 covers almost all of the LT families in the Appendix of Joe (1997), as well as other LT families that can be 55obtained by integration or differentiation. Suppose  (s)  T (s) = a1s q expf a2s rg and  0(s)  T 0(s); s!1; with a1 > 0; a2  0; (3.10) where r = 0 implies a2 = 0 and q < 0, and r > 0 implies r  1 and q can be 0, negative or positive. Note that r > 1 is not possible because of the complete monotonicity property of a LT. The condition can be interpreted as follows. As  (s) decreases to 0 more slowly as s ! 1, then the random variable H with LT  has a heavier “tail” at 0. Let z = lim !0 fH( ) 2 [0;1], where fH is the density of H and is assumed well-behaved near 0. As z increases, then the “tail” at 0 is heavier. If z = 0, then the tail is lighter as the rate of decrease to 0 is faster. If z = 1, then the tail is heavier as the rate of increase to1 is faster. In terms of the LT and the condition in (3.10), as r increases (with fixed q), the tail of H at 0 gets lighter, and as q increases (with fixed r), the tail of H at 0 gets heavier. The next proposition shows that lower tail dependence behavior is influenced by r. Proposition 3.8 Suppose a LT  satisfies the condition in (3.10) with 0  r  1. If r = 0, then C has lower tail dependence or lower tail order is 1. If r = 1, then  L(C ) = d. If 0 < r < 1, then C has intermediate lower tail dependence with 1 <  L(C ) = dr < d, ‘(u) = dqa 1  1 a   2 ( log u)  with  = (q=r)(1  dr), and the tail order function is b(w) = Qd i=1w dr 1 i . Remark 3.7 Condition (3.10) does not cover all possibilities. It is possible that as s ! 1,  (s) goes to 0 slower than anything of form (3.10). Examples are given by LT families LTF and LTG in Joe (1997), leading to Archimedean families such that limu!0+ C (u1d)=u = 1 (for the bivariate case, see families BB2 and BB3 in Joe and Hu (1996), and Joe (1997)). Note that, for LTF,  (s) = [1 +   1 log(1 + s)] 1= with  > 0 and  > 0 and as s!1,  (s)   1= (log s) 1= ; for LTG,  (s) = expf [  1 log(1 + s)]1= g with  > 0,  > 1 and as s ! 1,  (s)  expf   1= (log s)1= g. We refer to Chapters 4 and 5 for relevant research on LTF and LTG. 56Remark 3.8 Consider the pair ( ;  ) of LTs where (a)  0(0) is finite and  (s) =  0(s)= 0(0) or (b) R1 0  (v)dv is finite and  (s) = R1 s  (v)dv= R1 0  (v)dv. For the upper tail, we get M = M  1 so that LT  has a heavier tail and  U (C ) is smaller (stronger intermediate tail dependence) if  U (C ) < d. Proposition 3.8 implies that  L(C ) =  L(C ). But the second level of tail dependence strength comes from the slowly varying function ‘(u) = dqa1  1 a   2 ( log u)  . Since C (u1d)  u ‘(u), u! 0+, a smaller  means stronger intermediate lower tail dependence at the first level, and a faster ‘(u) ! +1 or a slower ‘(u) ! 0+ means stronger intermediate lower tail dependence at the second level. For the LT tail, a1sq exp( a2sr), a smaller r means slower decrease to 0 as s ! +1 and the resilience random variable has more probability near 0 and C has more de- pendence in the lower tail. This can be shown by a smaller tail order dr. A larger q means slower decrease to 0 as s ! +1, which also implies more lower tail dependence. This is seen from a faster increase of ‘(u) ! +1 as u ! 0+ when q < 0 and increases, or a slower decrease of ‘(u) ! 0+ as u ! 0+ when q > 0 and increases. Note that when u is small enough, ( log u) dominates ‘(u). 3.3.4 A new parametric Archimedean copula By applying the LT of the inverse Gamma distribution, we present a new one- parameter Archimedean copula that exhibits intermediate upper and lower tail de- pendence, and have essentially a full range of positive dependence from indepen- dence to comonotonicity. Example 3.7 (Archimedean Copula based on the Laplace Transform of Inverse Gamma (ACIG)) Let Y = X 1 have the inverse Gamma (I ) distribution, where X  Gamma( ; 1) for  > 0. Then it is straightforward to derive that MY =  . The LT of the inverse Gamma distribution:  (s; ) = 2  ( ) s =2K (2 p s); s  0;  > 0; (3.11) where K is the modified Bessel function of the second kind. (Please see Section 3.6 for the derivation of (3.11).) It can be shown numerically that the d-variate Archimedean copula with this 1-parameter LT family is decreasing in concordance 57as  increases, with limits of the independence copula as  !1 and the comono- tonic copula as  ! 0. Proposition 3.9 Let C be an Archimedean copula constructed by (3.11). If  2 (0;+1) is not an integer, then the upper tail order is maxf1;minf ; dgg. The lower tail order is p d. Remark 3.9 For the bivariate case,  U = maxf1;minf ; 2gg and  L = p 2 . Hence there is reflection asymmetry with skewness to the upper tail for 0 <  < p 2 and skewness to the lower tail for  > p 2 . Contour plots of this new Archimedean copula are given in Figure 3.1. Figure 3.1: Contour plots: ACIG copula + standard normal margins α = 4  0.02  0.04  0.06  0.08  0.1   0.12 −2 −1 0 1 2 − 2 − 1 0 1 2 α = 2  0.02  0.04  0.06  0.08  0.1   0.12   0.14  −2 −1 0 1 2 − 2 − 1 0 1 2 α = 1.414214  0.02  0.04  0.06  0.08  0.1  0.12   0.14   0.16 −2 −1 0 1 2 − 2 − 1 0 1 2 α = 1  0.02  0.04  0.06  0.08  0.1   0.12  0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 α = 0.8  0.02  0.04  0.06  0.08   0.1  0.12   0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 α = 0.5  0.02  0.04  0.06  0.08   0.1  0.12   0.14   0.16  −2 −1 0 1 2 − 2 − 1 0 1 2 To conclude this subsection, we list the tail orders for some Archimedean cop- ulas that interpolate independence and comonotonicity in Table 3.1. A variety of tail behavior obtains from known parametric Archimedean families and the new Archimedean family. Note that the bivariate Frank copula is reflection symmet- ric. But for d-dimensional Frank copula with d  3, it can be shown numerically 58that C (121d) > C ( 1 21d) for parameters  > 0, although the lower tail order and upper tail order are the same. Some of the results in this table can be found in Char- pentier and Segers (2009) and Heffernan (2000). For all of the examples in Table 3.1, the upper and lower tail orders decrease or remain constant as the dependence parameter(s) leads to increased dependence/concordance. Table 3.1: Tail order of some Archimedean copulas that interpolate indepen- dence and comonotonicity Copula/LT family  L  U Frank; log-series LT    1 log[1 (1 e  )e s] ( > 0) d d 1MTCJ; gamma LT (1 + s) 1= ( > 0) 1 d Joe; Sibuya LT 1 (1 e s)1= ( > 1) d 1 Gumbel; positive stable LT expf s1= g ( > 1) d1= 1 Joe-Hu; BB1 extension; (1 + s1= ) 1= ( > 0;  > 1) 1 1 Joe-Hu; BB7 extension; 1 [1 (1 + s) 1= ]1= ( > 1;  > 0) 1 1 Crowder; BB9 extension; expf (  + s)1= +  g ( > 1) d1= d Joe-Ma; R1 s e  v dv= (1 +   1) (0 <  < 1) d 1 +  ACIG; LT of inverse gamma 2  1( )s =2K (2 p s ) ( > 0) d1=2 (d ^  ) _ 1 1. Mardia-Takahasi-Cook-Johnson, (see Cook and Johnson, 1981). 3.4 Intermediate tail dependence: Mixture of max-id copulas As an extension of Archimedean copulas, we study in this section the tail orders for copulas that are constructed with mixtures of max-id copulas. Some results studied in Joe (1997) are extended to intermediate tail dependence. Let F be a d-variate cdf. If F t is also a cdf function for all t > 0, then F is max-id (Joe and Hu, 1996). The class of copulas based on mixture of max-id distributions has led to interesting classes of bivariate two-parameter copula families with both upper and lower tail dependence (e.g., labeled as BB1, BB4, BB7 in Joe (1997)). As well, other forms of intermediate tail dependence behavior are possible. These types of copulas will give us more flexibility in choices of bivariate linking copulas in vines (Aas et al., 2009; Joe et al., 2010). 59Here we generalize Theorems 4.13 and 4.16 in Joe (1997) to multivariate ver- sions and intermediate tail dependence. In the earlier research on copulas, the analyses determined when tail dependence (tail order  = 1) can occur for dif- ferent copula families; in that setting, the tail order occurred within the sufficient condition in Theorem 4.16 of Joe (1997). Let K be a multivariate max-id copula and  be a LT of a positive random variable, and consider the copulas that are of the following form C(u1; : : : ; ud) =    logK  e   1(u1); : : : ; e   1(ud)   (3.12) Proposition 3.10 Suppose that a copula C be constructed by (3.12). 1. If  satisfies the condition of Proposition 3.5 with some k 2 f1; : : : ; d 1g and  U (KI) > 1 for any marginal copula KI , then C has upper intermedi- ate tail dependence and  U (C) =  U (C ). 2. If 1   (s) 2 RV (0+),  U (K) = 1 with marginal copula KI(u1jIj)  u‘I(u); u ! 0+ such that limu!0+ ‘I(u) = hI 2 (0; 1], 0 < h  I = P ;6=J I( 1) jJ j 1hJ  1 and 0 < P ;6=I Id ( 1)jIj 1(h I)   1, then  U (C) = 1 with  U (C) = P ;6=I Id ( 1)jIj 1(h I)  . Proposition 3.11 Suppose that a copula C be constructed by (3.12) with 1   =  L(K)  d. If   (s)= 0(s) 2 RV with 0 <   1, and 1 <  1  < d, then the copula C has lower intermediate tail dependence  L(C) =  1  , and  L(C) =  ( ;  )   L(C ) with  ( ;  ) = ( =d)1  2 (0; 1]. Also,  L(K) = 1 implies that  L(C) = 1. Remark 3.10 Note that  L(C) is less than or equal to both  L(K) and  L(C ). K can be the independence copula or have intermediate lower tail dependence. The lower tail order of the copula C is increasing in  L(K). One consequence of Propositions 3.10 and 3.11 is that if  U (K) = d and  L(K) = d then  U (C) =  U (C ) and  L(C) =  L(C ). Hence if K is chosen as the parametric Frank copula family with parameter   0, then C(u1; :::; ud;  ) as given in (3.12) will be increasing in concordance as  increases. The parameter  affects dependence only, while the LT  controls the upper and lower tail orders. 60When we take K as the independence copula or the Frank copula with positive dependence, and the LT has tail of the form  (s)  a1sq expf a2srg, s ! 1, 0  r < 1, where a1; a2 are some positive constants, then we can construct a new family of Archimedean copulas that satisfies the condition of Proposition 3.11. In dimensions d  3, Archimedean and mixture of max-id copula families cannot achieve the range of dependence available from vine copulas (Bedford and Cooke, 2001; Aas et al., 2009; Joe et al., 2010). But for d = 2, the mixture of max- id approach can lead to more candidates, with a variety of upper and lower tail behavior, to be used as bivariate linking copulas in vines. For instance, from Table 3.1, the preceding subsections and propositions, the Joe-Ma  function, which is the normalized integral of the positive stable LT, combined with the bivariate Gaussian copula with %  0 can lead to a two-parameter family with more flexible upper and lower tail orders. Note that, the bivariate Gaussian density is Totally Positive of order 2 if %  0, and hence max-id (Joe, 1997, Theorem 2.6). 3.5 Discussion We have shown how the concept of tail order is useful to quantify the strength of dependence in the upper and lower tails, as well as the direction of reflection asym- metry. One- and two-parameter families that are Archimedean copulas and mixture of max-id copulas together can cover a wide range of tail orders. The interpretation through the latent resilience variable shows why Archimedean copulas can obtain a full range of tail orders by varying the density of the resilience at 0 and1. In order to get our results for Archimedean copulas, we needed Proposition 3.5 which, on its own, contributes knowledge about LTs. Archimedean copulas only have exchangeable dependence but their bivariate versions can be used within vines. Vine copulas (Bedford and Cooke, 2001; Aas et al., 2009; Joe et al., 2010) in dimension d, which include multivariate Gaussian and t copulas as special cases, are built from d(d 1)=2 bivariate linking copulas, of which d  1 are bivariate marginal copulas and the remainder are conditional bivariate copulas with the number of conditioning variables between 1 to d  2. By choosing bivariate linking copulas with flexible tail orders and reflecting sym- metry/asymmetry, we can get vine copulas to cover a wide range of tail behavior, 61as well as dependence structures. The study of tail orders of vine copulas in terms of the tail orders of the bivariate linking copulas will be studied in future research. For vine copulas, we are also interested in conditions that retain consistent relation of upper and lower tail orders for all margins. 3.6 Proofs Derivation of LT of the inverse Gamma distribution: With Y = X 1 and X  Gamma( ; 1), the LT is derived as  (s) =  (s; ) = E(e sY ) = E(e s=X) = [ ( )] 1 Z 1 0 e s=xx  1e xdx: From the GIG( ;  ; ’) density (McNeil et al., 2005), Z 1 0 w  1 expf 12( w  1 + ’w)g dw = 2( =’) =2K ( p  ’ ): Note that K = K  . Hence with  = 2s, ’ = 2,  =   (s; ) = 2  1( )(2s=2) =2K ( p 2s  2 ) = 2  1( )s =2K (2 p s ):  Proof of Proposition 3.1: Letting r := F 1(1 u) and b% = p 2=(1 + %), then by Example 6.2 (i) of Hashorva (2007), as u! 0+ and thus r !1, C(1 u; 1 u) = P  X1 > F  1(1 u); X2 > F  1(1 u)  = P[X1 > r;X2 > r] = (1 + o(1)) (1 %2)3=2 2 (1 %)2 [a(b%r)=r][1 FR(b%r)]; (3.13) where FR is the cdf of R and a( ) is an auxiliary function of R with respect to the Gumbel Maximum Domain of Attraction in the sense of (2.10). As u ! 0+, i.e., r ! 1, both a(b%r)=r ! 0 (see Theorems 3.3.26 and 3.3.27 of Embrechts et al. (1997)) and 1  FR(b%r) ! 0. Let G(x) := 1=[1  FR(x)], then G : R ! R+ is increasing and the condition of (2.10) is equivalent to that G 2  -varying with 62auxiliary function a( ) (de Haan, 1970, Definition 1.5.1). The inverse function of a  -varying function is a  -varying function (de Haan, 1974, Corollary 1.10). Therefore, G 1 2  -varying. Assuming that an auxiliary function ofG 1 is a0( ), by Lemma 1.2.9 of de Haan and Ferreira (2006), the auxiliary function a0( ) of the  -varying function G 1 is slowly varying at 1. Moreover, a0(t) = a(G 1(t)) (de Haan, 1974, Corollary 1.10). So, a(x) = a0(G(x)). Then in (3.13), a(b%r)=r = a0(G(b%r))=r = a0(1=[1 FR(b%r)])=r; while 1 FR(b%r) is rapidly varying in r at1 due to the fact that G is  -varying and any  -varying function is rapidly varying (de Haan, 1970, Theorem 1.5.1). Therefore, 1 FR(b%r) = 1 FR  p 2=(1 + %)F 1(1 u)  dominates the tail behavior of (3.13) as u! 0, and thus determines the correspond- ing tail order of the elliptical copula. By the definition of tail order in Definition 3.1, we may also obtain the upper tail order by the following  = lim u!0+ logC(1 u; 1 u) log u = lim r!1 log (1 FR(b%r)) log (1 F (r)) : By Example 6.2 (iii) of Hashorva (2007), as r !1, P[X1 > r] = (1 + o(1))(2 ) 1=2[a(r)=r]1=2[1 FR(r)]: Due to the similar argument as before, 1  FR(r) dominates the tail behavior of P[X1 > r], as r !1. Therefore, we may write  = lim r!1 log (1 FR(b%r)) log (1 FR(r)) ; which completes the proof.  Proof of Proposition 3.3: Assuming C(u1d)  u L(C)‘(u), u! 0+, with ‘(u) 2 RV0(0+), for any copula C and 0  u  1, C(u1d)  u. Therefore,  L(C)  1. 63To prove (1), by the condition of PLOD, we have C(u1d)  ud for any 0  u  1 and thus,  L(C)  d. To prove (2), choosing S1  S2 with jS2j  jS1j = j 2 N+. Let us consider the case where j = 1 first, for some l 2 f1; : : : ; jS2jg and any 0  u  1, CS2(u1jS2j) = P[Ul  ujU1  u; : : : ; Ul 1  u; Ul+1  u; : : : ; UjS2j  u]  P[U1  u; : : : ; Ul 1  u; Ul+1  u; : : : ; UjS2j  u]  P[Ul  u] P[U1  u; : : : ; Ul 1  u; Ul+1  u; : : : ; UjS2j  u] = uCS1(u1jS1j): The inequality is due to the MLTD of C. Clearly,  L(CS2)  L(CS1)  1. Since P[Ul  ujU1  u; : : : ; Ul 1  u; Ul+1  u; : : : ; UjS2j  u]  1, CS2(u1jS2j)  CS1(u1jS1j) and thus,  L(CS2)   L(CS1)  0. An iterated argument will prove the case for a general j: 0   L(CS2)   L(CS1)  jS2j  jS1j. If  L(C) = 1, then for any S  Id with jSj  2, we have 1   L(CS)   L(C) = 1, which completes the proof. For  L(C) = 1, note that the MLTD condition is not needed.  Proof of Lemma 3.4: Let Z be an exponential random variable, independent of Y , with mean 1. Choose any fixed m with 0 < m < 1. Then E(Z m) =  (1  m), and if we define Wm = (Y=Z)m, then for any w > 0, P [Wm  w] = P[Z  Y w 1=m] = Z 1 0  1 expf yw 1=mg  FY (dy) = 1  (w  1=m); where FY is the cdf of Y . Therefore, E(Y m) < 1 implies E(Wm) < 1 and limw!1w[1  (w 1=m)] = 0, i.e., lim s!0+ [1  (s)]=sm = 0: (3.14) If 1   (s) is regularly varying at 0+, then we can write 1   (s) = s ‘(s) with  6= 0, where ‘(s) 2 RV0(0+). Then, (3.14) implies that lims!0+ s   m‘(s) = 0. Let  > 0 be arbitrarily small. If m = MY   , then we have E(YMY   ) <1 and 64thus lims!0+ s   MY + ‘(s) = 0. Therefore,   MY   . Also by a result on page 49 of Chung (1974), (3:14) implies that for any 0 <  < 1, E  Y m(1  )  < 1. If we assume that there exists an  > 0 with m = MY +  such that, lims!0+ s   MY   ‘(s) = 0, then for any small  > 0, E  Y (MY + )(1  )  < 1. Then we may choose some   <  =( + MY ), and get E  Y (MY + )(1   )  < 1 with (MY +  )(1    ) > MY , which gives rise to a contradiction. Thus, for any  > 0, we must have lims!0+ s   MY   ‘(s) 6= 0, and hence,   MY    0. So, MY      MY +  ; which completes the proof.  Proof of Proposition 3.5: This proof extends that in Lemma 3.4, which corresponds to the case where k = 0. For a positive integer j, let Zj  Gamma(j + 1; 1) so that E(Z mj ) =  (j + 1 m)= (j + 1) if 0 < m < j + 1. Let Wm;j = (Y=Zj) m where Y is independent of Zj . Then for 0 < m < j + 1, E(Wm;j) < 1 if and only if E(Y m) < 1. Next, similar to the proof of Lemma 3.4, if Y has LT  and moments up to order k, for j 2 f0; 1; : : : ; kg and 0 < m < j + 1, Pr  Wm;j = (Y=Zj) m  w  = Pr(Zj  Y w  1=m) = Z 1 0 FZj (yw  1=m) dFY (y) = Z 1 0 h 1 jX i=0 yiw i=m i! expf yw 1=mg i dFY (y) = 1 jX i=0 w i=m i! ( 1)i (i)(w 1=m): Suppose 0 < m < minfj + 1;MY g. Then E(Y m) <1 implies that w h 1 jX i=0 w i=m i! ( 1)i (i)(w 1=m) i ! 0; w !1; 65i.e., s m h 1 jX i=0 si i! ( 1)i (i)(s) i ! 0; s! 0+: (3.15) Assuming  has derivatives at zero up to kth order, then for positive integer j  k, the main term in (3.15) is 1 jX i=0 si i! ( 1)i (i)(s) = 1 j 1X i=0 si i! ( 1)i h j iX l=0 sl (i+l)(0)=l! + o(sj i) i  sj j! ( 1)j (j)(s) = 1 j 1X i=0 si i! ( 1)i jX l=i sl i (l)(0)=(l  i)! sj j! ( 1)j (j)(s) + o(sj) = 1 jX l=0  (l)(0) sl l! nl^(j 1)X i=0 ( 1)i l! i!(l  i)! o  sj j! ( 1)j (j)(s) + o(sj) = 1  (0)  (j)(0) sj j!   ( 1)j   sj j! ( 1)j (j)(s) + o(sj) = ( 1)j 1 sj j!   (j)(s)  (j)(0)  + o(sj): (3.16) Hence (3.15) implies that sj m   (j)(s)  (j)(0)  ! 0; s! 0+; if j is a non-negative integer less than MY and m < MY . In particular, if k is a non-negative integer such that k < m < MY < k + 1, then sk m   (k)(s)  (k)(0)  ! 0; s! 0+: If    (k)(0)   (k)(s)   is regularly varying at 0+, we write    (k)(0)   (k)(s)   = s ‘(s). For any  > 0, a similar argument in the proof of Lemma 3.4 will prove that   MY  k   . Now we prove the other direction. We assume that there 66exists an  > 0 with m = MY +  such that, lim s!0+ s +k MY   ‘(s) = 0; (3.17) that is, sk MY     (k)(s)   (k)(0)  ! 0 as s ! 0. Since  is completely monotonic,  (k)(0)   (k)(s) is either negative or positive as s ! 0+. That is, ( 1)k[ (k)(0)   (k)(s)] > 0. The following argument is for an even k, and similar when k is odd. Then by the Karamata’s theorem (Resnick, 2007), regular variation of    (k)(0)  (k)(s)   implies that   (k 1)(x) +  (k 1)(0) + x (k)(0) = Z x 0   (k)(0)  (k)(s)  ds  ( + 1) 1x +1‘(x); x! 0+: (3.18) Since R x 0 [ (k)(0)  (k)(s)]ds is again regularly varying, we can take the integra- tion on both sides repeatedly and obtain for j = 0; 1; : : : ; k,   (k j)(x) + jX i=0 xj i (j  i)!  (k i)(0)    jY i=0 1  + i ! x +j‘(x); x! 0+: (3.19) Multiplying both sides of (3.19) by x k j (k j)!( 1) k j leads to LHSj =:  xk j (k  j)! ( 1)k j (k j)(x) + jX i=0 ( 1)k j (k  j)!(j  i)! xk i (k i)(0)  ( 1)k j (k  j)!   jY i=0 1  + i ! xk+ ‘(x); x! 0+: (3.20) Then we add the left-hand side of (3.20) for j = 0; : : : ; k, and after rearranging the summand, we have kX j=0 LHSj =  kX i=0 xi i! ( 1)i (i)(x) + 1 + k 1X i=0 kX j=i ( 1)k j (k  j)!(j  i)! xk i (k i)(0): (3.21) 67By the binomial theorem, for each given i 2 (0; : : : ; k 1), Pk j=i ( 1)k j (k j)!(j i)!  0. Then, from (3.20) and (3.21) we can conclude that kX j=0 LHSj = 1 kX i=0 xi i! ( 1)i (i)(x) = O  xk+ ‘(x)  : Therefore, multiplying both sides of the above by s MY   and using (3.17), s MY   h 1 kX i=0 si i! ( 1)i (i)(s) i ! 0; s! 0+: Then for any small  > 0, E  Y (MY + )(1  )  < 1. Then we may choose some   <  =( +MY ), and get E  Y (MY + )(1   )  <1 with (MY +  )(1   ) > MY , which gives rise to a contradiction to the fact that MY is the maximal moment. Thus, for any  > 0, we must have lims!0+ s  +k MY   ‘(s) 6= 0, and hence,   MY  k +  . Thus,  = MY  k. To prove the last statement of the proposition, since 1  (s) = 1  (k)(s)= (k)(0) =   (k)(0)  (k)(s)  = (k)(0) 2 RVMY  k(0 +); by Remark 3:4, we have  (s) =  (k)(s)= (k)(0) = 1 h0k+1s MY  k + o(sMY  k): Then, by integration, we will have  (s) = 1 +  (1)(0)s+ 12 (2)(0)s2 +    + ( 1)k+1hk+1s k+MY  k + o(sk+MY  k) = 1 h1s+ h2s 2     + ( 1)k+1hk+1s MY + o(sMY ) s! 0+; where 0 < hi <1. The integration is due to Lemma 31 of Breitung (1994).  Proof of Proposition 3.6: We provide the proof only for the bivariate case. For d  3, the intermediate upper tail dependence can be studied analogously, and the (omitted) proof is similar but with more complicated notation. Let  0(0) = m with  1 < m < 0, then by Proposition 3.5, as s ! 0+, 68letting M = M  1, g(s) =  0(s) m = asM + o(sM ); (0 < a <1; 0 < M < 1): Since g0(s) =  00(s) is increasing as s ! 0+, if we write g(s)  asM‘(s); s ! 0+, where ‘(s) 2 RV0(0+) and ‘(s)! 1 as s! 0+. Note that g(s) =  0(s) m = Z s 0  00(x)dx = Z s 0 g0(x)dx: By the Monotone Density Theorem (Bingham et al., 1987, Theorem 1.7.2),  00(s) = g0(s)  aMsM 1‘(s); s! 0+: (3.22) Thus, lims!0+  00(2s)= 00(s) = 2M 1. Observe that for 0 <  < 1, lim u!1 C (u; u) (1 u)1+ = lim u!1 1 2u+  (2  1(u)) (1 u)1+ = lim u!1  2 + 2 0(2  1(u))= 0(  1(u))  (1 +  )(1 u) = lim u!1 4 00(2  1(u))=[ 0(  1(u))]2  2 00(  1(u)) 0(2  1(u))=[ 0(  1(u))]3  (1 +  )(1 u)  1 = lim s!0+ 4 00(2s)=[ 0(s)]2  2 00(s) 0(2s)=[ 0(s)]3  (1 +  )(1  (s))  1 (letting s =   1(u)) = lim s!0+ 4m 2 00(2s) 2m 2 00(s)  (1 +  )(1  (s))  1 = lim s!0+ 4m 2  00(2s)  00(s)  2m  2  (1 +  ) (1  (s))   1  00(s) = lim s!0+ 2m 2(2M  1)  (1 +  ) (1  (s))   1  00(s) : By Proposition 3.5, there is a constant h > 0 such that 1  (s) =  ms hsM+1 + o(sM+1); s! 0+: 69Then, as s! 0+, [1  (s)]  1  ( m)  1s  1: In addition, it has been shown that 00(s)  aMsM 1‘(s) as s! 0+ and 2m 2(2M 1) is finite. Hence, the intermediate tail dependence exists if and only if  = M and  U = 1 +M = M . The proof for the case of k = 0 is similar, by applying Proposition 3.5.  Lemma 3.12 Let d  2 be a positive integer and let j be a positive integer that is less than d. Let Sdj(w1; : : : ; wd) = X ;6=I Id ( 1)jIj 1  X i2I wi  j : (3.23) Then Sdj  0. Proof of Lemma 3.12: When j = 1, by the binomial theorem, for any n 2 N+, Pn i=0( 1) i  n i  = 0, so that Sd1  0 for d  2. For 1 < j < d, Sdj is a symmetric homogeneous function of order j, and its first order partial derivatives are homogeneous of order j  1. By recursion with Euler’s formula for homogeneous functions to the jth order partial derivatives Sdj(w) = 1 j dX i=1 @Sdj(w) @wi wi = 1 j(j  1) dX i1=1 dX i2=1 @2Sdj(w) @wi1@wi2 wi1wi2 = 1 j! dX i1=1    dX ij=1 @jSdj(w) @wi1    @wij wi1    wij : (3.24) We will show that all the jth order partial derivatives are 0. Because of symmetry, we consider only terms for whichwi1 : : : wij = w n1 1    w np p where 1  p  j < d, 70n1 > 0, . . . , np > 0 and n1 +    + np = j. Then @jSdj(w) @n1w1    @npwp = j!( 1)p 1 + X ;6=J fp+1;:::;dg ( 1)p+jJ j 1j! = j!( 1)p 1  1 + X ;6=J fp+1;:::;dg ( 1)jJ j  = j!( 1)p 1 d pX i=0 ( 1)i  d p i  = 0; which completes the proof.  Note that (3.23) is not zero for j = d because (3.24) would include a non-zero term such as @dSdj=@w1    @wd = ( 1)d 1d!. In fact, there are d! non-zero terms in (3.24) when (i1; : : : ; id) is a permutation of (1; : : : ; d), and Sdd(w1; : : : ; wd) = ( 1)d 1d! Qd i=1wi. Proof of Proposition 3.7: Consider lim u!0+ P hT i2Id fUi  1 uwig i uk+M = X ;6=I Id ( 1)jIj 1 lim u!0+ P  S i2IfUi  1 uwig  uk+M : By Proposition 3.5, since the function w 7! 1   (w) 2 RV1(0+), then we have 71w 7!   1(1 w) 2 RV1(0+). Thus, lim u!0+ P  S i2IfUi  1 uwig  uk+M = lim u!0+ 1  [  1(1 uw1) +    +   1(1 uwd)] uk+M = lim u!0+ 1  [  1(1 uw1) +    +   1(1 uwd)] f1  [  1(1 u)]gk+M = lim u!0+ 1  h   1(1 u)    1(1 uw1)   1(1 u) +    +   1(1 uwd)   1(1 u)  i f1  [  1(1 u)]gk+M = lim u!0+ 1     1(1 u)  P i2I wi   f1  [  1(1 u)]gk+M : Let s =   1(1 u) and Q(w) = X ;6=I Id ( 1)jIj 1 lim s!0+ 1   s  P i2I wi   f1  (s)gk+M : (3.25) To obtain the limit in (3.25), we may use the l’Hoˆpital’s rule. For the first k deriva- tives of the numerator, for fixed w and  (j)(0) finite for j = 1; : : : ; k, lim s!0 X ;6=I Id ( 1)jIj 1 (j) h s  X i2I wi  i X i2I wi  j = 0; j 2 f1; : : : ; kg; because by Lemma 3.12, X ;6=I Id ( 1)jIj 1  X i2I wi  j = 0; j 2 f1; : : : ; kg; 1  k < d: Then by the l’Hoˆpital’s rule (k + 1 applications) Q(w) = X ;6=I Id ( 1)jIj 1  lim s!0+   (k+1)[s  P i2I wi  ]  P i2I wi  k+1 = (k+1)(s) [  (1)(s)]k+1f Qk j=0(k +M  j)g (1  (s)) M 1= (k+1)(s) : 72Since g(s) = j (k)(s)   (k)(0)j = hsM + o(sM ) with h > 0, we can write g(s)  sM‘(s); s! 0+ with a slowly varying function ‘(s)! h as s! 0+. Note that, g(s) = R s 0 j (k+1)(x)jdx = R s 0 g 0(x)dx, and g0(s) is monotonic as s ! 0+, then by the Monotone density theorem, j (k+1)(s)j = g0(s)  MsM 1‘(s) and  (k+1)(s)  ( 1)k+1MsM 1‘(s). Therefore, Q(w) = Mh [  (1)(0)]k+M Qk j=0(k +M  j) X ;6=I Id ( 1)jIj+k+1  X i2I wi  k+M : Then, the upper tail order function is b (w) = Q(w) Q(1d) = P ;6=I Id ( 1)jIj  P i2I wi  k+M P ;6=I Id ( 1)jIj   I   k+M : Note that this is a homogeneous function in w of order  U = M = k +M . This completes the proof .  Proof of Proposition 3.8: If r = 0, then   1(t)  (t=a1)1=q as t ! 0+ (where q < 0). If r > 0, then for large s and small t, in log (s) = log t  log a1 + q log s a2s r; s!1; the third term dominates, so that   1(t)  [( log t)=a2] 1=r; t! 0+: Next, consider C (u1d) =  (d  1(u)). For r = 0, one gets  (d  1(u))   (da 1=q1 u 1=q)  dqu, as u ! 0+, with dq 2 (0; 1), so that  L = 1. For 0 < r < 1, suppose  L; = lim u!0+   d  1(u)  u ( log u) > 0: 73Then by l’Hoˆpital’s rule,  L; = lim u!0+ d 0  d  1(u)  = 0    1(u)   u  1( log u) = lim s!1 d 0(ds)= 0(s)  [ (s)]  1[ log (s)] : (3.26) By condition (3.10), the dominating term of  0(s) or T 0(s) is  0(s)   a1a2rs q+r 1 expf a2s rg; s!1: Consider the limit of the right-hand side of (3.26) without the factor d= :  0(ds)= 0(s)  dq+r 1 expf a2(d r  1)srg; s!1; [ (s)]  1[ log (s)]  [a1s q]  1 expf a2(  1)s rg  [a2s r  q log s log a1]   a  11 a  2s q(  1)+r expf a2(  1)s rg; s!1: Hence  = dr, q(  1) + r = 0 or  = (q=r)(1  ) = (q=r)(1 dr) and  L; = ddq+r 1  a  11 a  2 = dq a  11 a  2 : So ‘(u) =  L;  ( log u) = dqa 1  1 a   2 ( log u)  . Under the condition in (3.10), it can be verified that   (s)= 0(s) 2 RV1 r, which satisfies the condition in Theorem 3.3 of Charpentier and Segers (2009). So the tail order function is obtained.  Proof of Proposition 3.9: When 0 <  < 1, K (s)  12   ( )  s=2    +  (  )  s=2    : (3.27) We refer to the website of Wolfram Research Bes for asymptotic behavior of mod- ified Bessel function of the second kind. For 0 <  < 1,  (s; )  1 +  (  )  ( ) s ; s! 0+; 74and 1  (s; )   s  (  )= ( ) 2 RV (0+). This is consistent with Lemma 3.4. Now, let us consider the case where  is non-integer with  > 1. For an integer j with 0 < j <  and X 0  Gamma(  j; 1),  (j)(s; ) = [ ( )] 1( 1)j Z 1 0 e s=xx  j 1e x dx = ( 1)j  (  j)  ( ) E(e s=X 0 ) = ( 1)j2  1( )s(  j)=2K  j(2 p s ): So,  (s; ; j) =  (j)(s; )= (j)(0; ) is the LT of Z = 1=X 0  I (  j; 1) with MZ =   j. When  is non-integer with j j > 1, the behavior near 0 of K is K (x)  x  j j2j j 1 (j j) h 1 + x2 4(1 j j) i : So, for integer j <   1 ( 1)j (j)(s; ) = 2  1( )s(  j)=2K  j(2 p s )   (  j)  ( )  1 + s 1  + j  ; s! 0; for k <  < k + 1, where k 2 N+, then by (3.27) ( 1)k (k)(s; ) = 2  1( )s(  k)=2K  k(2 p s )   (  k)  ( ) +  (  + k)  ( ) s  k; s! 0: Therefore, j (k)(s)  (k)(0)j 2 RV  k(0+), which is consistent with Proposition 3.5. Then, by Proposition 3.5, there is a positive constant hk+1 such that  (s) =1 +  (1)(0)s+ 12 (2)(0)s2 +    + ( 1)k (k)(0)sk=k!+ + ( 1)k+1hk+1s  + o(s ): The upper tail order of the d-variate Archimedean copula C follows from Propo- sitions 3.6 and 3.7. Therefore, if  2 (0;+1) is not an integer, the upper tail order is maxf1;minf ; dgg. 75Next we investigate the lower tail. From Abramowitz and Stegun (1964), p. 378: for large z, K (z)  r  2z e z n 1 + 4 2  1 8z +O(z 2) o : Hence,  (s; ) = 2  1( )s =2K (2 p s )  2  1( )s =2 r  4s1=2 e 2s 1=2 =  1=2  1( )s =2 1=4e 2s 1=2 ; s!1: (3.28) Also, as s!1,  (1)(s; ) =  2  1( )s(  1)=2K  1(2 p s )    1=2  1( )s =2 3=4e 2s 1=2 : For the d-dimensional Archimedean copula, then by Proposition 3.8 with a1 =  1=2  1( ), q =  =2 1=4; a2 = 2 and r = 1=2 in (3.10), as u! 0,   d  1(u)   d =2 1=4 a  11 2  ( log u) u p d ; a1 =  1=2  1( );  = (  1=2)(1 p d ): Thus  L(C ) = p d.  Proof of Proposition 3.10: Suppose that K is a multivariate max-id copula such that, for any index set ; 6= I  Id, KI((1  s)1jIj)  s aI ‘I(s), s ! 0+, with 1 < aI and ‘I(s) 2 RV0(0+). Note that K((1 s)1d) = 1 + X ;6=I Id ( 1)jIjKI  (1 s)1jIj  ; (3.29) whereKI is the survival function of the I-margin copulaKI and letKfig(1 s) = s for any i 2 Id. Letting s = 1 expf   1(u)g, as u! 1 , i.e., s! 0+, since aI > 1 for any ; 6= I  Id, 1  ds dominates the right-hand side of (3.29), and 76thus,  logK  e   1(u)1d  =  logK((1 s)1d)   log (1 ds)  ds = d(1 expf   1(u)g)  d  1(u): Therefore, C(u1d)    d  1(u)  = C (u1d); u! 1  : By Proposition 3.6, we know that C has intermediate upper tail dependence, and moreover,  U (C) =  U (C ). This proves (a). To prove (b), note from Proposition 3.3 that  U (KI) = 1 for any marginal copula KI . Assuming ‘I(s)! hI 2 (0; 1] as s! 0+ and hfig = 1 for all i,  logKI  e   1(u)1jIj  =  logKI((1 s)1jIj)   log (1 h  Is)  h  I  1(u); where h I = P ;6=J I( 1) jJ j 1hJ . By the construction of (3.12), for any I-margin copula of C, CI(u1jIj) =    logKI(e    1(u)1jIj)     h I  1(u)  : Thus, as s! 0+, i.e., u! 1 , C(u1d) = 1 + X ;6=I Id ( 1)jIjCI(u1jIj)  1 + X ;6=I Id ( 1)jIj  h I  1(u)  = X ;6=I Id ( 1)jIj 1  1   h I  1(u)   : If 1  (x) 2 RV (0+), then clearly, C((1 u)1d)  u X ;6=I Id ( 1)jIj 1(h I)  ; u! 0+: So,  U (C) = 1 and  U (C) = P ;6=I Id ( 1)jIj 1(h I)  .  77Proof of Proposition 3.11: Suppose that a d-variate max-id copula K(s1d)  s ‘(s) as s! 0+ and let s = expf   1(u)g. As u! 0+, thus s! 0+,  logK  e   1(u)1d  =  logK(s1d)   log(s  ‘(s))    log s =    1(u): Therefore, C(u1d) =    logK  e   1(u)1d         1(u)  ; u! 0+: With some modification of the proof of Theorem 3.3 of Charpentier and Segers (2009), we can prove the rest. For purpose of notational convenience, we include the modification in the following. Letting  = ( + d )=(1 +  ) and !(s) =   (s)= 0(s), then we know that lim s!0+   1(sx)   1(s) !(  1(s)) =  log(x); and if y(t)! y 2 R as t!1, then lim t!1  (t+ y(t)!(t))  (t) = exp( y): For any t > 0, write  (   1(ut)) =      1(u) + y(u; t)!(   1(u))  , where y(u; t) =   [  1(ut)   1(u)] !(  1(u))  !(  1(u)) !(   1(u)) : As u! 0+, y(u; t)!   log(t)   =   1  log(t). Therefore, lim u!0+   1(   1(ut))   1(   1(u)) = exp( 1  log(t)) = t 1  ; and thus C(u1d) 2 RV 1  (0 +). We have also known from Theorem 3.3 of Char- pentier and Segers (2009) that  L(C ) = d1  . This completes the proof.  78Chapter 4 Tail comonotonicity 4.1 Introduction Suppose we have bivariate loss data and hope to estimate some high-risk scenarios, say by VaR or CTE. A widely used method is to fit some parametric models based on the bivariate Student t or other parametric copula families. Then risk measures or tail dependence can be derived from these fitted models. Due to model uncer- tainty, all of these methods are not conservative from the viewpoint of an actuary. The middle part might influence our estimation more than the important tail part does. These traditional methods are too sensitive to the middle part which contains most of the data (Nikoloulopoulos et al., 2012). Given that we do not have enough information for the joint tail of losses, we conservatively assume that it is upper tail comonotonic (as defined in Section 4.2), and the conservativity can be justified in Chapter 5. In this way, we actually give up estimating the “first order tail parameter” (i.e., the usual tail dependence parameter  ) by assuming a conservative one (i.e.,  = 1), and let the likelihood of data contribute to the estimation of the “second order tail parameter”. Since the first order parameter is only for an asymptotic property, the conservative assumption on it would not put too much constraint on the model. This approach will give us a more robust method of measuring risks. In the literature on actuarial science and quantitative finance, many efforts have been done to seek finer upper bounds for dependence structures. The concepts of 79comonotonicity, conditional comonotonicity, and most recently, the upper comono- tonicity have been studied to provide theoretically tractable bounds. However, these conditions are either too strong or not tailored for the tail, and could lead to an over-conservative risk measure. Moreover, those dependence structures lack flex- ible distribution families that can be used to model real data. We refer to Dhaene et al. (2002a,b); Cheung (2007, 2009) for the reference of these concepts. Tail comonotonicity, on the other hand, needs a weaker condition and requires that the degree of positive dependence approaches its maximum only when all the marginal losses go to infinity. The degree of dependence at the sub-extremal level can still be estimated from the data. This approach should better balance the requirements of safety and accuracy for risk management. Tail comonotonicity is also referred to as asymptotic full dependence or a de- pendence structure where the tail dependence parameter satisfies  = 1. Although such a dependence structure is not new, we find that it has interesting properties, such as asymptotic additivity of VaR and CTE, that are analogous to the usual comonotonicity. Moreover, some parametric families illustrate its suitability for modeling loss data that may appear to have tail dependence. Among many copula families, Archimedean copulas and copulas constructed from a scale mixture of a non-negative random vector can be used to provide a tail comonotonic dependence structure. This chapter is organized as follows. In Section 4.2, the concept of tail comono- tonicity, its properties and some parametric examples will be studied. Methods of constructing copulas with tail comonotonicity or the strongest tail dependence are given in Section 4.3. Asymptotic additivity of VaR and CTE under the assumption of tail comonotonicity is shown in Section 4.4. Finally, in Section 4.5, we conclude the chapter and propose some directions for future research. 4.2 Definitions of tail comonotonicity and properties Let X = (X1; : : : ; Xd)T be a non-negative random vector, representing amounts of d losses, and the univariate marginal cdf’s are all continuous and denoted as F1; : : : ; Fd. The Fre´chet space containing all the random vectors with these uni- variate margins is denoted as R(F1; : : : ; Fd). For risk management, the joint tail 80probability P[X1 > x1; : : : ; Xd > xd] is relevant, especially when x1; : : : ; xd are large values. Cheung (2009) studied an upper comonotonicity structure: roughly speaking, beyond a finite threshold a , the dependence structure becomes comono- tonicity, while keeping the dependence structure below the threshold flexible; this means that the distribution is not absolutely continuous. Using the Florida flooding dataset mentioned in Figure 1.2, the idea of comonotonicity and upper comono- tonicity can be illustrated by Figure 4.1. Figure 4.1: Florida flood data - Comonotonicity vs Upper comonotonicity Florida flood aggregate loss (1977~2006) Normal score of aggregate loss of 30 counties No rmal score of aggregate loss of other 30 countie s −2 −1 0 1 2 −2 −1 0 1 2 G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G GG G G G G G G G G Florida flood aggregate loss (1977~2006) Normal score of aggregate loss of 30 counties No rmal score of aggregate loss of other 30 countie s −2 −1 0 1 2 −2 −1 0 1 2 G G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G Under this dependence structure, if x  a , then clearly P[X1 > x1; : : : ; Xd > xd] = minfF 1(x1); : : : ; F d(xd)g = 1 maxfF1(x1); : : : ; Fd(xd)g; which is the upper bound for such a tail probability of any X 2 R(F1; : : : ; Fd). The upper bound coincides with the tail probability for the usual comonotonicity. Letting bC be the survival copula of X, P[X1 > x1; : : : ; Xd > xd] = bC(F 1(x1); : : : ; F d(xd)): A proper threshold a might not exist in real applications. However, note that our aim of proposing a conservative dependence structure will be met if the tail 81probability can be well approximated by the upper bound when x is sufficiently large; that is, bC(F 1(x1); : : : ; F d(xd))  minfF 1(x1); : : : ; F d(xd)g; xi’s are sufficiently large: When x is sufficiently large, F i(xi)’s become sufficiently close to 0. But how xi’s converge to the right end points of their supports will affect the joint tail prob- ability. Considering that the usual comonotonicity is a copula property and does not depend on the margins, here we want to define a dependence concept which will not rely on margins as well. Therefore, letting ui := F i(xi), it suffices to have that bC(u1; : : : ; ud)  minfu1; : : : ; udg; ui’s are sufficiently small. To be convenient in real applications, this condition should also be satisfied with copulas bC that are absolutely continuous. Of course, how ui’s approach 0 will affect the above approximation. However, as long as the rates of convergence of ui’s to 0 are comparable (in the sense that ui = uwi for any given 0 < wi < +1), the above approximation should be good. Then bC would be what we want if it satisfies bC(uw1; : : : ; uwd)  minfuw1; : : : ; uwdg; u! 0 +; wi; : : : ; wd 2 [0;+1); i.e., lim u!0+ bC(uw1; : : : ; uwd) u = minfw1; : : : ; wdg; wi; : : : ; wd 2 [0;+1); (4.1) where the trivial case of wi = 0 for some i is also included. Definition 4.1 A random vector X is said to be upper tail comonotonic if X has a copula C and its survival copula bC satisfies (4.1); the copula C is said to be an upper tail comonotonic copula. X is said to be lower tail comonotonic if X has a copula C that satisfies limu!0+ C(uw1; : : : ; uwd)=u = minfw1; : : : ; wdg; wi 2 [0;+1); the copula C is said to be a lower tail comonotonic copula. 82Remark 4.1 Based on the above definition, tail comonotonicity is a concept about copula and does not rely on the marginal distributions as long as the conditions hold. In Definition 4.1, the random vector X is not necessarily continuous and the right end points of each univariate margin can be finite or infinite. Remark 4.2 In the framework of multivariate regular variation (MRV), the con- cept of upper tail comonotonicity is represented by the limit Radon measure (Resnick 2007 Resnick (2007))  ([0;x]c) = (minfx1; : : : ; xdg)   : So asymptotic full dependence for MRV is a case of upper tail comonotonicity with the univariate margins being power laws. Recall from Joe et al. (2010) that, when C is a d-variate copula with the sur- vival function C, then lower tail dependence means that there exists a non-zero homogeneous function b of order 1 such that lim u!0+ C(uw1; : : : ; uwd)=u = b(w1; : : : ; wd); wi 2 [0;+1); (4.2) and upper tail dependence means that there exists a non-zero homogeneous func- tion b of order 1 such that lim u!0+ C(1 uw1; : : : ; 1 uwd)=u = b  (w1; : : : ; wd); wi 2 [0;+1): (4.3) So, tail comonotonicity simply means that the lower and/or upper tail dependence functions are minfw1; : : : ; wdg. Also, note that for any tail dependence function b(w), we must have b(w)  min(w1; : : : ; wd). Some relevant properties for upper/lower tail comonotonicity are mentioned in Beirlant et al. (2004), we give some alternative results in the following two propositions. Proposition 4.1 Suppose C is a copula. Then  U (C) = 1 if and only if the up- per tail dependence function exists and b (w1; : : : ; wd) = min(w1; : : : ; wd). In 83parallel,  L(C) = 1 if and only if the lower tail dependence function exists and b(w1; : : : ; wd) = min(w1; : : : ; wd). Remark 4.3 From Proposition 4.1, we can conclude that the concept of upper/lower tail comonotonicity is nothing but the tail dependence parameter  = 1. The des- ignation of this concept is just consistent with the names of upper/lower tail depen- dence. If the tail dependence function exists, then it must have the unique form. It is well known that all pairs of a random vector are comonotonic is equiv- alent to that the random vector is comonotonic. A parallel result also holds for upper/lower tail comonotonicity. Proposition 4.2 Suppose C is a d-variate copula, then any bivariate marginal copula Cfijg; i 6= j, is upper [resp. lower] tail comonotonic if and only if C is upper [resp. lower] tail comonotonic. Next, we give two examples of parametric tail comonotonic copulas that are two-parameter Archimedean copulas. Example 4.1 (BB2 in Joe and Hu (1996), Joe (1997)) With LT  (s) = [1 +   1 log(1 + s)] 1= ; the bivariate Archimedean copula is C (u; v) = h 1 +   1 log  e (u    1) + e (v    1)  1  i 1= ;  > 0;  > 0: Then as u! 0+, C (uw1; uw2) u  h 1 +   1 maxf ((uw1)    1);  ((uw2)    1)g i 1= u 1 = min(w1; w2): Thus C is lower tail comonotonic. Scatter plots of the BB2 copula with parame- ters  = 0:2 and  = 0:4 or 0:2 are in Figure 4.2 (N = 2000); there is no upper tail dependence for this copula. 84Figure 4.2: Simulation of BB2 with/without the univariate margins being transformed to the standard Normal; in the left and middle plots  = 0:2,  = 0:4 and in the right plot  = 0:2,  = 0:2 BB2 d= 0.2 th= 0.4  (Uniform margin) 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 G G G G G G G G G G G G G G G G G G G GG G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GGG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG BB2 d= 0.2 th= 0.4  (Normal margin) −2 0 2 −2 0 2 G G G G G G G G GG G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GGG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G G G G G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G GG G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G GG G G G G G GG G G GG G GG G G G G G G G G G G G G G G G G GGG G G G G G G G G GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G BB2 d= 0.2 th= 0.2  (Normal margin) −2 0 2 −2 0 2 G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GGG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G GG G G G G GG G GG G G G G G G G G G G GG G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G GG G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G GG G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G Example 4.2 (BB3 in Joe and Hu (1996), Joe (1997)) With LT  (s) = exp n  [  1 log(1 + s)]1= o ; the bivariate Archimedean copula is C (u; v) = exp n  [  1 log(e ~u  + e ~v   1)]1= o ;  > 1;  > 0; where ~u =  log u; ~v =  log v. Then as u! 0+, C (uw1; uw2) u  exp n  [  1 log(e ( log[u min(w1;w2)])  )]1= o u 1 = min(w1; w2): Thus C is also lower tail comonotonic. Scatter plots of the BB3 copula with parameters  = 0:2 and  = 1:7 or 1.3 are in Figure 4.3 (N = 2000); there is also upper tail dependence for this copula and  U = 2 21= . Remark 4.4 Looking at the plots for the lower comonotonic copulas, they appear suitable as survival copulas to be used to get conservative dependence structure for joint large losses. Although they are both lower tail comonotonic, there is not much constraint on the sub-extremal level. 85Figure 4.3: Simulation of BB3 with/without the univariate margins being transformed to the standard Normal; in the left and middle plots  = 0:2,  = 1:7 and in the right plot  = 0:2,  = 1:3 BB3 d= 0.2 th= 1.7  (Uniform margin) 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GGG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G GG G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G BB3 d= 0.2 th= 1.7  (Normal margin) −2 0 2 −2 0 2 G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G GG G G GG G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G GG G G GG G G G GG G G G G G G G G G G G G GG G G G G G G G G G G GG G G G G G G G G G GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G BB3 d= 0.2 th= 1.3  (Normal margin) −2 0 2 −2 0 2 G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G GGG G G G G G GG G G G G GG G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G GGG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G GG G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G GG GG G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G GG GG G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G GG G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G GG G G GG G G G G GG G G G G G G G G G G G G G G G GG G G G G G G G GG G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G GG G G G G G G G G G G G G G G 4.3 Construction of tail comonotonic copulas In this section, we propose methods to construct tail comonotonic copulas based on mixing distributions with very heavy tails; “very heavy” means there are no moments of any positive/negative orders or the corresponding survival function is slowly varying. Precise conditions will be presented in the following subsections. 4.3.1 Archimedean copulas In this subsection, we will relate the tail behavior of LTs to upper/lower tail comono- tonicity of corresponding Archimedean copulas. The next result says that if the LT  is slowly varying at the tails, then C must have the tail dependence function min(w1; : : : ; wd). Proposition 4.3 Let the Archimedean copula C be based on the LT  satisfying  (1) = 0. If  (s) 2 RV0 then the lower tail dependence function exists and C is lower tail comonotonic; if 1  (s) 2 RV0(0+) then the upper tail dependence function exists and C is upper tail comonotonic. Remark 4.5 Write the Archimedean copula as the mixture C (u1; : : : ; ud) = Z 1 0 dY i=1 G (ui)dFH( ); 86where G(u) = expf   1(u)g is a cdf on [0; 1] and H is a resilience random variable with LT  . The condition 1  2 RV0(0+) means that the density fH( ) has a very heavy tail as  !1 so that E[Hm] =1 for all m > 0. The condition  2 RV0 means that the density fH( ) has a very heavy tail as  ! 0 so that E[H m] =1 for allm > 0. The implication of the condition of 1  at 0 follows by the proof of Lemma 3.4. Similarly, we can show the implication of the condition of  at1 as follows. Suppose to the contrary that E[H m] <1 for some m > 0. Let Wm = (Z=H)m, where Z  Exponential(1), independent of H , and H has the LT  . Then P[Wm  w] = P[Z  Hw1=m] = R1 0 exp( yw 1=m)FH(dy) =  (w1=m). Then E[H m] < 1 and Z having all positive moments implies that E[Wm] < 1 and thus wP[Wm  w] ! 0, as w ! 1; that is, w (w1=m) ! 0, or equivalently sm (s) ! 0, as s ! 1. It is well known that if  (s) 2 RV0 and m > 0, we must have sm (s)!1. The contradiction implies that E[H m] =1 for all m > 0. It can be verified that the LTs for BB2 and BB3 are both slowly varying at +1, except in the lower boundary case (i.e.,  = 1 for BB3); that is,  BB2(s) = [1 +   1 log(1 + s)] 1= 2 RV0;  > 0;  > 0;  BB3(s) = exp n  [  1 log(1 + s)]1= o 2 RV0;  > 1;  > 0: Now, consider the tail behavior of the LT studied in (3.10); that is, assume that a LT  satisfies  (s)  T (s) = a1s q expf a2s rg and  0(s)  T 0(s); s!1; with a1 > 0; a2  0; (4.4) where r = 0 implies a2 = 0 and q < 0, and r > 0 implies r  1 and q can be 0, negative or positive. Note that r > 1 or r < 0 is not possible because of the complete monotonicity property of a LT. This condition covers almost all of the LT families in the Appendix of Joe (1997), as well as other LT families that can be obtained by integration or differentiation. The next result is contained in the Appendix of Joe (1997), where L +1 is the class of infinitely differentiable increasing functions of [0;1) onto [0;1), with 87alternating signs for derivatives. This combines some results from pages 441 and 450 of Feller (1971); the condition on  implies that it is the LT of an infinitely divisible random variable. Lemma 4.4 If  is a LT such that  log  2 L +1 and  is another LT, then  (s) =  ( log  (s)) is a LT. Proposition 4.5 Suppose the LT  (t) satisfies condition (4.4), and take LT of Gamma  (s) = (1 + s) 1= , thus  (s) :=  ( log( (s))) is also a LT. Then 0  r < 1 implies that  (s) 2 RV0. In the literature, risks are compared with respect to some stochastic orders, say, the usual stochastic order and the increasing convex order (Mu¨ller and Stoyan, 2002). In our opinion, the comparisons of risks are more meaningful for high-risk scenarios. However, the conditions these stochastic orders need to satisfy are too strong to be flexible for comparing tail risks. To this end, we may define some new concepts of stochastic order that are particularly used to compare the tails of univariate/multivariate cdf’s. For example, we may define a stochastic order based on asymptotic properties of distribution functions as what follows. Definition 4.2 (Ultimate usual stochastic orders) Let X , Y be random variables, X is said to be greater than Y in the upper ultimate usual stochastic order if there exists a finite q such that FX(t)  F Y (t) for all t  q, written as X &st Y . Let X and Y be random vectors, then X is said to be greater than Y in the upper ultimate usual stochastic order if there exists a finite threshold q such that FX(t)  FY(t) for all t  q, written as X &st Y. For lower ultimate usual stochastic order, the notation is st&. Then X is said to be greater than Y in the lower ultimate usual stochastic order (written as Xst&Y ) if there exists a finite q such that FX(t)  FY (t) for all t  q, and X is said to be greater than Y in the lower ultimate usual stochastic order if there exists a finite threshold q such that FX(t)  FY(t) for all t  q, written as Xst&Y. The relationships can also be symbolized by using corresponding cdfs, such as, FXst&FY for Xst&Y. If X;Y represent amounts of losses, then X &st Y implies that X is riskier than Y . If the right tail of X is heavier than that of Y , then this is a sufficient condition for X &st Y . 88Proposition 4.6 Let  1;  2 be given LTs. If   11   2(s) is superadditive for suf- ficiently large s, then there exists a finite threshold q such that for any u  q, C 1(u)  C 2(u), i.e., C 1st&C 2 . Note: using notation of Archimedean copula in Nelsen (2006), the generator should be strict in order to apply Proposition 4.6. Remark 4.6 From Proposition 4.6, we expect that BB2 is more lower tail positive dependent than BB3 below a sufficiently low threshold, although they are both lower tail comonotonic. Let  1(s) :=  BB2(s) = [1 +   1 1 log(1 + s)]  1= 1 and  2(s) :=  BB3(s) = exp   [  12 log(1 + s)] 1= 2  , then g(s) :=   11   2(s) = expf 1(e  1(log(1+s)= 2)1= 2  1)g  1: Letting h(s) := log(1 + s)= 2, g0(s) =  1 1h(s) 1= 2e 1h(s) 1= 2e  1  e 1h(s) 1= 2 1  =( 2(1 + s) log(1 + s)): By observing g0(s), we know that g(s) is strictly increasing and ultimately strictly convex. Assume that g(s) is strictly convex as s  s0, and let y0 := g(s0) and z(s) := g(s + s0)  y0, then z(s) is superadditive for s 2 [0;1). Therefore, for x; y  0, z(x+ y)  z(x) + z(y); that is, g(x+ y + s0) + g(s0)  g(x+ s0) + g(y + s0). Since g(s) is strictly increasing and strictly convex as s  s0, we must have, when x; y are sufficiently large, g(x + y + 2s0)  g(x + y + s0) + g(s0). Therefore,   11   2(s) is ultimately superadditive. 4.3.2 Heavy tail mixtures Consider the heavy tail scale mixture X = (RT1; : : : ; RTd); (4.5) where R and Ti’s are all non-negative random variables, R is independent of the Ti’s, and the dependence structure between Ti’s is not specified. This form cov- ers truncated elliptical distributions. The CTE for such random vectors has been 89studied in Zhu and Li (2012), and more relevant study is in Chapter 6, where R is assumed to be regularly varying and second order regularly varying in the right tail, respectively. The following lemma is often referred to as the Breiman’s Theorem (Breiman (1965)). Although the proof in Breiman (1965) is not for a general  , it can be adapted for proving a more general case where 0   < +1. To the best of our knowledge, we have not found a complete and detailed proof for this case. So we include a proof in Section 4.6, and emphasize that the result for  = 0 corresponding to slowly variation of Y also holds. Lemma 4.7 Suppose a random variable R  0 with FR(y) 2 RV  ; (0   < +1). T  0 is independent of R and E[T + ] <1 for some  > 0. Then lim y!1 P[RT > y] P[R > y] = E[T ]: (4.6) Proposition 4.8 Let X = (RT1; : : : ; RTd) be defined as in (4.5). If R 2 RV0, E[T  ii ] <1; i = 1; : : : ; d, for some  i > 0, then X is upper tail comonotonic. The above scale mixture can be used to construct upper tail comonotonic cop- ulas. Note that, the margins Ti’s are not necessarily identical and their dependence structure is not specified. An elliptical random vector X can be written as X d =  +RAU  Ed( ; ;  ); (4.7) where the radial random variableR  0 is independent ofU, U is an k-dimensional random vector uniformly distributed on the surface of the unit hypersphere Sk 12 = fz 2 RkjzTz = 1g, A is a d  k matrix with rank(A) = k and AAT =  that is positive semidefinite,  is the characteristic generator. When  is positive def- inite, write  ij :=  ij= p  ii jj , and  ij 6=  1 in the following to avoid trivial cases. We refer to Fang et al. (1990) for a comprehensive reference for elliptical distributions. Now, we are studying whether there exist tail comonotonic elliptical distribu- tions. Note that the Ti’s in Proposition 4.8 are all non-negative, so it does not cover 90the usual elliptical distributions. For tail dependence of elliptical distributions, Schmidt (2002), and Hult and Lindskog (2002) study the case where the radial random variable R has certain tail patterns such as regularly varying right tails. Frahm et al. (2003) shows that the two different representations of tail dependence parameters in Schmidt (2002) and Hult and Lindskog (2002) for the regularly vary- ing case are equivalent. From their results, we know that, with a regularly varying radial random variable R (tail index   < 0), the non-degenerate elliptical dis- tributions have tail dependence parameters that are strictly less than 1. We now show that, even when P[R > s] is slowly varying in s, tail comonotonicity still does not hold. The intuitive reasoning for this result is that for bivariate elliptical distributions that have usual tail dependence, there is tail dependence in all cor- ners/quadrants, so that the tail dependence in the upper positive quadrant is not 1. Proposition 4.9 Suppose X is an elliptical random vector as defined in (4.7) with  ii > 0 for i = 1; : : : ; d, if P[R > s] 2 RV0, then any bivariate margins of X is upper (and lower) tail dependent, and the tail dependence parameter  ij = 1=2 + (1= ) arcsin  ij . Remark 4.7 In the proof of Theorem 4.3 in Hult and Lindskog (2002), it is claimed that X d = RAU implies that  Xi Xj ! d = R 0 @ p  ii 0 p  jj jj p  jj q 1  2ij 1 A  cos’ sin’ ! ; where ’  Uniform(  ;  ). Here we need to make a note that the above claim is true only when X is bivariate. For a general d-dimensional X (d > 2), radial random variables for margins will not be the same as the original R for the whole distribution. We make a more detailed argument about this in what follows. Due to the Corollary in page 43 of Fang et al. (1990), margins of elliptical distributions are still elliptical with the same characteristic generator  . Moreover, due to Lemma 5.3 of Schmidt (2002), the radial random variable R depends on the dimension d of the elliptical distribution, and there exists a constant k > 0 such 91that Rs d = kRd p B; B  Beta(s=2; (d s)=2); s < d; and B is independent of Rd. Since a Beta distribution has moments of all orders, by Lemma 4.7, Rs inherits the tail behavior of Rd when Rd is regularly varying or slowly varying, which is why the original proof in Hult and Lindskog (2002) can still yield a correct conclusion. Let (U1; U2) follows a bivariate copula, where Ui  Uniform(0; 1); i = 1; 2. From Embrechts et al. (2009a), the tail dependence parameters in the “North East (NE)” and “South East (SE)” for the bivariate Student t copula are the following:  NE := lim u!1 P[U2 > u; jU1 > u] = R  =2 ( =2 arcsin  )=2(cos t)  dt R  =2 0 (cos t)  dt =:  ( ;  ); (4.8)  SE := lim u!1 P[U2  1 u; jU1 > u] = R  =2 ( =2+arcsin  )=2(cos t)  dt R  =2 0 (cos t)  dt =:  ( ;  ): (4.9) Note that  NE + SE is still a conditional probability and thus 0   NE + SE  1; in fact, for the above 0 <  ( ;  ) +  ( ;  ) < 1 for  > 0. Moreover, both  NE and  SE are decreasing in  : for a bivariate t copula (Embrechts et al. (2002), Joe (2011)),  NE = 2T +1   p ( + 1)(1  )=(1 +  )  ; (4.10)  SE = 2T +1   p ( + 1)(1 +  )=(1  )  ; (4.11) and it can be verified that (4.8) and (4.10) are equivalent, and (4.9) and (4.11) are equivalent (see Section 4.6 for a direct derivation). Since T +1(x) is decreasing in  for a fixed x  0, and T +1(x) is increasing in x for a fixed  , both  NE and  SE are decreasing in  . From Theorem 5.2 of Schmidt (2002), we know that if there exists a bivariate 92tail dependent margin, then we must have 0 < lim inf x!1 F (tx) F (x)  lim sup x!1 F (tx) F (x)  1; t  1; where F ( ) is the survival function of the radial random variable R. If R is slowly varying, then the upper bound in the above necessary condition is reached. Although we have not proved it for now, we conjecture that there are no tail comonotonic non-degenerate elliptical distributions and the maximum tail depen- dence parameter for a bivariate elliptical copula is  = 1=2 + (1= ) arcsin  . As  ! 0+ in (4.8) and (4.9), the sum converges to [1=2 + (1= ) arcsin  ] + [1=2 + (1= ) arcsin(  )] = 1 and this is the maximum possible value of limx!1 P(jX2j > xjX1 > x) when X1; X2 have a common distribution. Hence when all corners are considered, elliptical distributions with a slowly variable radial random variable have the strongest possible tail dependence. In contrast, note that Archimedean copulas based on LTs do not have tail dependence on quadrants that have different signs. 4.3.3 Extreme value copulas We now show that for extreme value copulas, tail comonotonicity is equivalent to comonotonicity. Proposition 4.10 Suppose C is an extreme value copula, then  U = 1 () C is a comonotonic copula ()  L = 1: 4.4 Asymptotic additivity of risk measures As a dependence structure, tail comonotonicity may affect the risk measures of aggregated losses. In this section, we mainly study impacts of tail comonotonicity on additivity of commonly used risk measure such as VaR and CTE. It is well known that VaR and CTE are additive when the loss random variables are comonotonic (see Dhaene et al. (2006)); that is, if (X1; : : : ; Xd) is comono- 93tonic, then for all p 2 (0; 1), VaRp  dX i=1 Xi ! = dX i=1 VaRp(Xi); CTEp  dX i=1 Xi ! = dX i=1 CTEp(Xi): The additivity property also holds for the upper comonotonicity in the sense of Cheung (2009) when the probability level p associated with the risk measures is larger than a threshold specified by the upper comonotonicity structure. A natural question is whether such an additivity property can be kept asymptotically as p! 1 . Asymptotic super and/or sub additivity of risk measures has been studied ex- plicitly or implicitly in several papers. For example, Embrechts et al. (2009b) stud- ied asymptotic additivity properties of VaR for multivariate (d > 2) dependent loss random variables that have regularly varying survival functions (with index   ) and Archimedean dependence structures. It is shown that for a probability level p < 1 and sufficiently close to 1, whether strict super or sub additivity depends on whether  < 1 or  > 1. The Archimedean copula has upper tail depen- dence but the upper tail dependence parameter  < 1 (since the generator of the Archimedean copula is assumed to be regularly varying). In Section 3.3 of Alink et al. (2007), an example of lower tail comonotonic copula has been shown together with Corollary 2.4 of Embrechts et al. (2009b) that VaR is asymptotically additive for exchangeable regularly varying dependent random variables that have an upper tail comonotonic copula. Analogous to the additivity property of VaR and CTE for a comonotonic ran- dom vector, we find that, asymptotic additivity of VaR and CTE still holds for a large class of random vectors that are relevant for quantitative risk management. We now prove some results for asymptotic additivity of VaR and CTE for ran- dom variables that are in the maximum domain of attraction (MDA) of Fre´chet and Gumbel, respectively. The case for MDA of Weibull corresponds to loss random variables that are bounded above, and is not very relevant for actuarial applications, so we do not consider it here. Only upper tails of losses are to be studied as they are more relevant for risk measures; analogous results for lower tails also hold but are omitted here. It suffices 94to consider non-negative random variables to study upper tails, so in the following the random vector X is assumed to be non-negative. However, for lower tails, we have simpler notation for tail comonotonicity. Therefore, instead of studying X directly, in what follows, we always define Y :=  X and prove the results based on Y. The following corresponding assumptions are also assigned on Y, which have corresponding natural meanings for the non-negative random vector X. For study of tail behavior of random variables, we always assume that the dis- tribution function is continuous (or at least continuous in the tail regions). So P[X < x] coincides with P[X  x] in what follows. Assumption 4.1 Let Y be a non-positive continuous random vector with marginal distributions F1; : : : ; Fd defined on ( 1; 0] such that Fi( t) 2 RV  with  > 0, and lim t!1 Fi( t)=F1( t) = ki; 0 < ki <1; for any i = 1; : : : ; d. Remark 4.8 If Y :=  X satisfies Assumption 4.1, thus P[Xi > t] 2 RV  , then each univariate margin of X is in the MDA of Fre´chet (Theorem 3.3.7 of Embrechts et al. (1997)). The following lemmas are analogous to Lemma 6.1 of Resnick (2007). They are useful in proving Propositions 4.13 and 4.14. For E  R d , denote M+(E) := f : Radon, non-negative measures on the Borel  -algebra E of Eg; and let the lower boundary LB := f(y1; : : : ; yd) : some yi = 0g and the upper boundary UB := f(y1; : : : ; yd) : some yi =1g, then define E1 := [0;1] n LB; E2 := [ 1;1] n UB: 95Lemma 4.11 Suppose  n;  2M+(E1). Then, as n!1  n v !  inM+(E1) ()  n([y;1])!  ([y;1]) for any y 2 [0;1) n LB such that  (@[y;1]) = 0. Remark 4.9 The set E1 excludes the lower boundary, since otherwise rectangles of the form [y;1] can not determine the vague convergence. If  puts mass on axes such as the usual normalization for asymptotic independence in Section 6.5.1 of Resnick (2007), then  =2 M+(E1). The condition  (@[y;1]) = 0 also re- quires that  does not put mass on the upper boundary. Lemma 4.12 Suppose  n;  2M+(E2). Then, as n!1  n v !  inM+(E2) ()  n([ 1;y])!  ([ 1;y]) for any y 2 ( 1;1] n UB such that  (@[ 1;y]) = 0. Proposition 4.13 (Asymptotic additivity of VaR: Fre´chet case) Suppose X is non- negative and upper tail comonotonic, and Y :=  X satisfies Assumption 4.1. If S = X1 +    +Xd, then VaRp(S)  dX i=1 VaRp(Xi); p! 1  : Assumption 4.2 Let Y be a non-positive continuous random vector with marginal distributions F1; : : : ; Fd defined on ( 1; 0] and there exists a positive measurable function a( ) such that lim t!1 Fi( t+ a(t)s) Fi( t) = es; 8s 2 R; (4.12) and limt!1 Fi( t)=F1( t) = ki with 0 < ki <1 for any i = 1; : : : ; d. Remark 4.10 Note that the condition Fi( t)=F1( t) ! ki implies that we can take ai(t) = a(t) for all i without loss of generality, and a(t) = o(t); t ! 1. If Y :=  X satisfies Assumption 4.2, then due to Theorem 3.3.27 of Embrechts 96et al. (1997), each univariate margin of X is in the MDA of Gumbel; that is FXi(t+ a(t)s)=FXi(t)! e  s; s 2 R. Remark 4.11 A mixture distribution can satisfy the tail equivalence condition FXi(t)=FX1(t) ! ki. Without loss of generality, we can let 0 < ki  1. For example, let random variables X1  Exponential(1) and X2 = ( Exponential(1); with probability k2 0; with probability 1 k2: Then FX2(x)=FX1(x)! k2. Proposition 4.14 (Asymptotic additivity of VaR: Gumbel case) Suppose X is non- negative and upper tail comonotonic, and Y :=  X satisfies Assumption 4.2. If S = X1 +    +Xd, then VaRp(S)  dX i=1 VaRp(Xi); p! 1  : The conditions that are studied for asymptotic additivity of VaR satisfy assump- tions considered in Asimit et al. (2011), which investigates asymptotic proportion- ality between CTE and VaR. With the help of their results, we may conclude the asymptotic additivity of CTE in what follows. Proposition 4.15 (Asymptotic additivity of CTE: Fre´chet case) Suppose X is non- negative and upper tail comonotonic, and Y :=  X satisfies Assumption 4.1 with  > 1. If S = X1 +    +Xd, then CTEp(S)  dX i=1 CTEp(Xi); p! 1  : Proposition 4.16 (Asymptotic additivity of CTE: Gumbel case) Suppose X is non- negative and upper tail comonotonic, and Y :=  X satisfies Assumption 4.2. If 97S = X1 +    +Xd, then CTEp(S)  dX i=1 CTEp(Xi); p! 1  : The above asymptotic additivity results suggest that when we use tail comono- tonicity as the dependence structure for such marginal distributions, we should expect that the diversification benefit (see Definition 2.2) will decrease to 0 as p ! 1 . However, the speed of decay is unknown by observing the asymptotic relationship only; that is, we do not know when p is sufficiently close 1 so that the additivity relationship of the risk measures is sufficiently good. The relevant study will involve a second order approximation, and it is out of the scope of the thesis. 4.5 Concluding remarks and future research Tail comonotonicity, like comonotonicity and upper comonotonicity, provides a bound-like dependence structure, and it is more reasonable to be used to capture in- formation from data. Tail comonotonicity has some parallel properties of the usual comonotonicity, such as asymptotic additivity of VaR and CTE. Among many dif- ferent copula families, Archimedean copulas with a mixing distribution that has no moments of any positive orders (for upper tail comonotonicity) or no moments of any negative orders (for lower tail comonotonicity), and copulas based on scale mixtures with a slowly varying non-negative random variable can be used to con- struct tail comonotonic copulas. However, elliptical and extreme value copulas cannot provide useful tail comonotonic copulas. Since tail comonotonicity is only an asymptotic property, it may or may not provide a conservative dependence structure for sub-extremal levels of risks. In Chapter 5, we will study how conservative are risk measures, under the tail comono- tonic dependence structure. Although asymptotic additivity properties of VaR and CTE we have proved has already covered a wide range of random vectors, an elegant proof for a most general case (if exists; note that additivity of VaR and CTE for the usual comono- tonicity does not depend on margins) must be welcome. Many other topics, such as optimal portfolio design, could be studied under the assumption of tail comono- 98tonicity. We think that tail comonotonicity is particularly useful for dealing with high dimensional data for high-risk scenarios. Due to the curse of dimensionality, it is very hard (or impossible) to accurately model the tail behavior of aggregate high dimensional risks. For dependence modeling of multivariate random vari- ables, vine copula is promising (Bedford and Cooke, 2002; Aas et al., 2009; Joe et al., 2010). By Proposition 4.2, it is feasible to use bivariate tail comonotonic cop- ulas to build up a vine copula that is still tail comonotonic. The vine copula with tail comonotonicity may provide a reasonable cushion for the overall dependence structure. 4.6 Proofs Proof of Proposition 4.1: If we take (w1; : : : ; wd) = (1; : : : ; 1), then it is obvious that  = 1. For the other direction, letting w = min(w1; : : : ; wd) 6= 0, then C(uw ; : : : ; uw ) u  C(uw1; : : : ; uwd) u  min(uw1; : : : ; uwd) u = w ) w = lim uw !0+ C(uw ; : : : ; uw ) uw w  lim inf u!0+ C(uw1; : : : ; uwd) u  lim sup u!0+ C(uw1; : : : ; uwd) u  w : Thus, b(w1; : : : ; wd) = min(w1; : : : ; wd). Similar for the upper tail.  Proof of Proposition 4.2: By Proposition 4.1, it suffices to prove that  = 1. We only state the proof for lower tail comonotonicity here. SupposeUi  Uniform(0; 1) with joint distribution C. Since P[U1  u] Pd i=2 P[U1  u < Ui] u  C(u1d) u  1; and pairwise lower tail comonotonicity implies that limu!0+ Pd i=2 P[U1  u < Ui]=u = 0, which implies that C is lower tail comonotonic. The other direction is due to the fact that if  L(C) = 1, then  L(CI) = 1 for every marginal copula CI ; 1 < jIj < d.  Proof of Proposition 4.3: By Proposition 4.1, we only need to prove the tail de- 99pendence parameter  = 1. For the lower tail, letting s :=   1(u), then because  (s) 2 RV0,  L = lim u!0+ C (u1d) u = lim u!0+  (d  1(u)) u = lim s!+1  (ds)  (s) = 1: For the upper tail, by Proposition 4.2, it suffices to prove the bivariate case. Let s :=   1(1 u), then  U = lim u!0+ bC (u; u) u = lim u!0+  2 +  (2  1(1 u)) 1 u  = lim s!0+  2 1  (2s) 1  (s)  = 1; which finishes the proof for the upper tail.  Proof of Proposition 4.5: Clearly,  log( (s)) = (1= ) log(1 + s) 2 L +1, by Lemma 4.4,  (s) is a LT. Then the conclusion is straightforward by plugging  log( (s)) into  ( ) of (4.4) since exp( [log(1 + s)]r) 2 RV0 as 0 < r < 1.  Proof of Proposition 4.6: Superadditivity of   11   2(s) for sufficiently large s implies that there exists sufficiently large s0 such that s1  s0 and s2  s0 lead to   11   2(s1 + s2)    1 1   2(s1) +   1 1   2(s2): Let s1 :=   1 2 (u) and s2 :=   1 2 (v), then u; v   2(s0) implies that   11   2(  1 2 (u) +   1 2 (v))    1 1   2(  1 2 (u)) +   1 1   2(  1 2 (v)); that is,  2(  1 2 (u) +   1 2 (v))   1(  1 1 (u) +   1 1 (v)), which completes the proof.  Proof of Lemma 4.7: Choose some b(y), such as y‘0(y) for a suitably chosen 100slowly varying function ‘0(y), satisfying that as y !1, b(y)!1, and b + (y)FR(y)!1; 8 > 0 (4.13) b(y)=y ! 0: (4.14) Then, lim y!1 P[RT > y] P[R > y] = lim y!1 R1 0 FR(y=t)FT (dt) FR(y) = lim y!1 R b(y) 0 FR(y=t)FT (dt) FR(y) + lim y!1 R1 b(y) FR(y=t)FT (dt) FR(y) = lim y!1 Z 1 0 1(0;b(y)](t) FR(y=t) FR(y) FT (dt) + lim y!1 Z 1 b(y) FR(y=t) FR(y) FT (dt) =: lim y!1 I1 + lim y!1 I2: We know 1(0;b(y)](t) FR(y=t) FR(y)  1(0;1](t) + 1(1;b(y)](t) FR(y=t) FR(y) , and also, when t 2 (1; b(y)], (4.14) implies that as y !1, y=t!1. Then as y is sufficiently large, by the Karamata’s representation of regularly varying function, when t 2 (1; b(y)], FR(y=t) FR(y)  t + : Since E[T + ] <1, by the dominated convergence theorem, lim y!1 I1 = Z 1 0 lim y!1 1(0;b(y)](t) FR(y=t) FR(y) FT (dt) = Z 1 0 t FT (dt) = E[T ]: For I2, we have I2  Z 1 b(y) 1 FR(y) FT (dt)  Z 1 b(y) t + FR(y)b + (y) FT (dt)! 0; y !1; and the convergence to 0 is due to E[T + ] <1 and (4.13).  101Proof of Proposition 4.8: Due to Lemma 4.7, FX1(s)  FXi(s); s! +1 for i = 2; : : : ; d, thus, s = F  1 Xi (FXi(s))  F  1 Xi (FX1(s)). Since F  1 X1(t) = F  1 X1 (1 t), clearly, F 1Xi (FX1(s))  s; s! +1; i = 2; : : : ; d: (4.15) Assuming F is the joint cdf of (T1; : : : ; Td), and hi(s) := F  1 Xi (FX1(s))=s; i = 1; : : : ; d, the upper tail dependence parameter  U = lim t!1 P h X1 > F  1 X1 (t); : : : ; Xd > F  1 Xd (t) i P h X1 > F  1 X1 (t) i = lim s!+1 P h RT1 > s; : : : ; RTd > F  1 Xd (FX1(s)) i =P[R > s] P[RT1 > s]=P[R > s] = lim s!+1 R R d + P [R > s(maxfh1(s)=t1; : : : ; hd(s)=tdg)]F (dt1; : : : ; dtd)=P[R > s] P[RT1 > s]=P[R > s] : (4.16) Due to Lemma 4.7, lims!+1 P[RT1 > s]=P[R > s] = 1. For the numerator of (4.16), choose some b(s) such as s‘0(s) with a proper slowly varying function ‘0 such that, s!1 implies b(s)!1, and b (s)FR(s)!1; 8 > 0 (4.17) b(s)=s! 0: (4.18) 102Denote (b(s)1d;+1]c := R d + n (b(s)1d;+1], then lim s!+1 Z R d + P [R > s(maxfh1(s)=t1; : : : ; hd(s)=tdg)] P[R > s] F (dt1; : : : ; dtd) = lim s!+1 Z R d + 1(b(s)1d;+1]c(t1; : : : ; td)  P [R > s(maxfh1(s)=t1; : : : ; hd(s)=tdg)] P[R > s] F (dt1; : : : ; dtd) + lim s!+1 Z R d + 1(b(s)1d;+1](t1; : : : ; td)  P [R > s(maxfh1(s)=t1; : : : ; hd(s)=tdg)] P[R > s] F (dt1; : : : ; dtd) =: lim s!+1 I1 + lim s!+1 I2: Due to (4.15), for any 0 <  < 1, there exists an s0 <1 such that, s > s0 implies that hi(s) >  for all i. Then s > s0 implies that 1(b(s)1d;+1]c(t1; : : : ; td) P [R > s(maxfh1(s)=t1; : : : ; hd(s)=tdg)] P[R > s]  1[0; 1d)(t1; : : : ; td) + 1(b(s)1d;+1]cn[0; 1d)(t1; : : : ; td)  P [R > s (maxf1=t1; : : : ; 1=tdg)] P[R > s] : It is well known that P[R > s] is slowly varying implies P[R > st]=P[R > s] converges uniformly in t 2 [a; b] as s ! 1, where 0 < a; b < 1 (see Bingham et al., 1987). Then P[R > s (maxf1=t1; : : : ; 1=tdg)]=P[R > s] converges uniformly in t 2 (b(s)1d;+1]c n [0;  1d) to 1. Moreover, t 2 (b(s)1d;+1] c n [0;  1d) and (4.18) implies that, as s! +1, s maxf1=t1; : : : ; 1=tdg ! +1. Then slow 103variation of P[R > s] and dominated convergence theorem implies that lim s!+1 I1 = Z R d + lim s!+1 1(b(s)1d;+1]c(t1; : : : ; td)  P [R > s(maxfh1(s)=t1; : : : ; hd(s)=tdg)] P[R > s] F (dt1; : : : ; dtd) =: Z R d + lim s!+1 1(b(s)1d;+1]c(t1; : : : ; td) (s; t1; : : : ; td)F (dt1; : : : ; dtd): Since (4.15), for any 0 <  1;  2 < 1, there exists an s1 such that s > s1 im- plies that 1   1  hi(s)  1 +  2 for all i. Thus, for any t1; : : : ; td such that minft1; : : : ; tdg > 0, 1 = lim s!1 P[R > s(1 +  2)=minft1; : : : ; tdg] P[R > s]  lim sup s1<s!1  (s; t1; : : : ; td)  lim s!1 P[R > s(1  1)= minft1; : : : ; tdg] P[R > s] = 1: Therefore, lim s!1 I1 = 1: For I2, 1(b(s)1d;+1](t1; : : : ; td) P [R > s(maxfh1(s)=t1; : : : ; hd(s)=tdg)] P[R > s]  1(b(s)1d;+1](t1; : : : ; td) 1 P[R > s]  1(b(s)1d;+1](t1; : : : ; td) (minft1; : : : ; tdg) P[R > s]b (s) : Note that, E[T  ii ] < 1 implies that E[(minfT1; : : : ; Tdg)  ] < 1 for some  > 0, because E[(minfT1; : : : ; Tdg) i ]  R R d + t ii F (dt1; : : : ; dtd) = E[T  i i ] < 1. Since E[(minfT1; : : : ; Tdg) ] < 1, and P[R > s]b (s) ! +1 (due to (4.17)), by dominated convergence theorem, lim s!+1 I2 = 0: Thus, the claim is proved.  104Proof of Proposition 4.9: The proof in Theorem 4.3 of Hult and Lindskog (2002) remains valid for this case where R is slowly varying, although in their proof, R is required to be regularly varying with a tail index   < 0.  Proof of equivalence between (4.8) and (4.10): The cdf of t +1 can be written as a regularized Beta function as follows: (Press, 2007, page 323) T +1(t) = 1 2  sign(t) " 1 2 B  x;  +12 ; 1 2  B   +1 2 ; 1 2   1 2 # ;  > 0; 0 < x :=  + 1 t2 +  + 1 < 1: whereB(x; :; :) andB(:; :) are incomplete Beta and Beta functions. Using trigono- metric function representation of Beta functions, we have with t = sin2  , B   + 1 2 ; 1 2  = Z 1 0 t( +1)=2 1(1 t)1=2 1dt = 2 Z  =2 0 (sin  ) d = 2 Z  =2 0 (cos  ) d : Next, withB  x;  +12 ; 1 2  , where t =  q ( +1)(1 %) 1+% and x =  +1 t2+ +1 = (1+%)=2, then B  1 + % 2 ;  + 1 2 ; 1 2  = Z (1+%)=2 0 s(v 1)=2(1 s) 1=2ds = 2 Z arcsin p (1+%)=2 0 (sin  ) d = 2 Z  =2  =2 arcsin p (1+%)=2 (cos  ) d : Now using the identity cos(2 ) = cos2   sin2  , it can be verified that 2( =2  arcsin p (1 + %)=2) =  =2  arcsin % by taking “cos” on both sides and getting %. Therefore, the claim is proved.  Proof of Proposition 4.10: By Proposition 4.2, it suffices to prove the bivariate case. For any multivariate extreme value copula C, there exists a function A : [0;1)d ! [0;1) such that C(u1; : : : ; ud) = expf A( log u1; : : : ; log ud)g, where A is convex, homogeneous of order 1 and satisfies that maxfx1; : : : ; xdg  A(x1; : : : ; xd)  x1 +    + xd. For a bivariate extreme value copula, the function A(x; y) can be written as A(x; y) = (x + y)B( xx+y ), where B( ) is convex and 105maxfw; 1 wg  B(w)  1 for 0  w  1 (see Joe, 1997, Theorem 6.4). The upper tail dependence parameter is  U = 2  A(1; 1). If we let  U = 1, then B(1=2) = 1=2, also B(w) must be convex, so we have B(w) = maxfw; 1 wg; that is, A(x; y) = maxfx; yg and thus C(u1; u2) = expf A( log u1; log u2)g = minfu1; u2g: The other direction is straightforward. The lower tail order for a bivariate extreme value copula is  = A(1; 1) (see Example 3.4). If  L = 1, we must have the lower tail order A(1; 1) = 1. Then the subsequent argument is the same as for the upper tail.  Proof of Lemma 4.11: The proof is the same as Lemma 6.1 of Resnick (2007). We rewrite the proof here for completeness. ()) is due to Theorem 3.2 of Resnick (2007). For ((), let g 2 C+K(E1), where C+K(E1) := fg : E1 ! R+; continuous with compact supportg; then the support of g must be contained in some [y;1] such that  (@[y;1]) = 0. Since convergence on this set holds, supn  n([y;1]) <1 and thus, sup n  n(g)  sup x2E1 g(x)  sup n  n([y;1]) <1: This is true for any g 2 C+K(E1), so f ng is relatively compact due to (3.16) of Resnick (2007). If  and  0 are two subsequential limits, then  and  0 agree on the continuity sets [y;1]. Additionally, the rectangles of those continuity sets [y;1] constitute the  -system which generates B(Ko) the Borel  -algebra of Ko, where Ko := fB  E1 : B is relatively compact ;  (@B) = 0g. Then  0 =  on E1 by Theorem 3.2 of Resnick (2007) again.  Proof of Lemma 4.12: The proof here is similar to the proof of Lemma 4.11 by replacing [y;1] by [ 1;y].  Proof of Proposition 4.13: Let W =  S and Yi =  Xi; i = 1; : : : ; d, then (Y1; : : : ; Yd) is lower tail comonotonic, and the map y 7! P[Yi   y] 2 RV  . 106Moreover, since limt!1 Fi( t)=F1( t) = ki for any i = 1; : : : ; d, clearly, F 1i (p)  k 1= i F  1 1 (p) as p ! 0 + (Lemma 2.1 of Asimit et al. (2011), or Propo- sition 0.8(vi) of Resnick (1987)). It suffices to prove that, F 1W (p)  F  1 1 (p) dX i=1 k1= i ; p! 0 +: Also, P[W   t] 2 RV  (Proposition 7.3 of Resnick (2007)) and apply Propo- sition 0.8(vi) again. Then it suffices to show as t!1, P[W   t]  P[Y1   t]  dX i=1 k1= i ! : (4.19) Under Assumption 4.1, if Y is also lower tail comonotonic, and C is the copula, then we have for any y 2 [0;1) n LB, lim t!1 P[Y1   ty1; : : : ; Yd   tyd] P[Y1   t] = lim t!1 C  F1( ty1) F1( t) F1( t); : : : ; Fd( tyd) F1( t) F1( t)  F1( t) = minfk1y   1 ; : : : ; kdy   d g; (4.20) where the last equality is due to lower tail comonotonicity of C and uniform con- tinuity of copula functions (Nelsen, 2006, see). Define a measure  on E1 as  ([y1;1]     [yd;1]) := minfk1y   1 ; : : : ; kdy   d g: This measure  only puts mass on the line f(y1; : : : ; yd) : k1y   1 =    = kdy   d g. If k1y   1 =    = kdy   d =: h, then  ([y1;1]     [yd;1]) = h. Clearly,  is Radon and every set [y;1] is a continuity set. Define  t( ) := P [( Y1=t; : : : ; Yd=t) 2  ] P[Y1   t] ; 107then by Lemma 4.12 and (4.20),  t( ) v !  ( ); t!1: where v ! is vague convergence. Define a set H :=  y 2 [0;1) n LB : dX i=1 yi  1 ! : Then clearly,  (@H) = 0 and H is relatively compact. Thus by Theorem 3.2 of Resnick (2007),  t(H)!  (H); t!1: That is, letting zi := kiy   i , as t!1, P[W   t] P[Y1   t] =  t(H)! min n k1y   1 ; : : : ; kdy   d : dX i=1 yi  1 o =  Lebesgue  z : dX i=1 (z=ki)  1=  1 ! =  dX i=1 k1= i ! ; which justifies (4.19), thus finishing the proof.  Proof of Proposition 4.14: Let W =  S and Yi =  Xi; i = 1; : : : ; d, then (Y1; : : : ; Yd) is lower tail comonotonic. Moreover, since lim t!1 Fi( t+ a(t)s)=F1( t) = kie s for any s 2 R and i = 1; : : : ; d, taking s =  log ki leads to VaRp(Yi)  VaRp(Y1) a( VaRp(Y1)) log ki; p! 0 + (4.21) 108Then it suffices to show as t!1, P " W   dt a(t) dX i=1 log ki #  P[Y1   t]: (4.22) Under Assumption 4.2, if Y is also lower tail comonotonic, and C is the copula, then we have for any y 2 ( 1;1] n UB, lim t!1 P[Yi   t a(t) log ki + a(t)yi; i = 1; : : : ; d] P[Y1   t] = lim t!1 C  Fi( t a(t) log ki+a(t)yi) F1( t)  F1( t); i = 1; : : : ; d  F1( t) = lim t!1 C  kieyi log kiF1( t); i = 1; : : : ; d  F1( t) = minfey1 ; : : : ; eydg; (4.23) where the last equality is due to lower tail comonotonicity of C and uniform con- tinuity of copula functions (Nelsen, 2006). Define a measure  on E2 as  ([ 1; y1]     [ 1; yd]) := minfe y1 ; : : : ; eydg: This measure  only puts mass on the line f(y1; : : : ; yd) : ey1 =    = eydg. If ey1 =    = eyd =: h, then  ([ 1; y1]      [ 1; yd]) = h. Clearly,  is Radon and every set [ 1;y] is a continuity set. Define  t( ) := P [((Y1 + t)=a(t) + log k1; : : : ; (Yd + t)=a(t) + log kd) 2  ] P[Y1   t] ; then by Lemma 4.12 and (4.23),  t( ) v !  ( ); t!1: where v ! is vague convergence. Define a set H :=  y 2 ( 1;1] n UB : dX i=1 yi  0 ! : 109Then clearly,  (@H) = 0 and H is relatively compact. Thus by Theorem 3.2 of Resnick (2007),  t(H)!  (H); t!1: That is, letting zi := eyi , as t!1, P[W   dt a(t) Pd i=1 log ki] P[Y1   t] =  t(H) ! min n ey1 ; : : : ; eyd : dX i=1 yi  0 o =  Lebesgue  z > 0 : dX i=1 log z  0 ! = 1: which justifies (4.22) and asymptotic additivity holds.  Proof of Proposition 4.15: It is well known that under Assumption 4.1 with  > 1, CTEp(Xi)     1VaRp(Xi); p ! 1  , for i = 1; : : : ; d (e.g., Zhu and Li, 2012). By Theorem 2.1 in Asimit et al. (2011), and Proposition 4.13, dX i=1 CTEp(Xi)     1 dX i=1 VaRp(Xi)     1 VaRp(S)  CTEp(S); p! 1  ; which completes the proof.  Proof of Proposition 4.16: It is well known that under Assumption 4.2, CTEp(Xi)  VaRp(Xi); p! 1 (e.g., Section 3.3.3 of Embrechts et al. (1997)). Also, CTEp(S)  VaRp(S); p ! 1 (see (2.15) of Asimit et al. (2011)). Therefore, by Proposition 4.14, CTEp(S)  VaRp(S)  dX i=1 VaRp(Xi)  dX i=1 CTEp(Xi); p! 1  ; which completes the proof.  110Chapter 5 Tail dependence and conservativity 5.1 Introduction Dependence modeling with copulas for multivariate random losses plays an im- portant role in accounting for a nonlinear dependence structure in insurance and financial data. For example, the dependence between losses and associated ex- penses often appears to be asymmetric in upper and lower tails for auto insurance claim data (Frees and Valdez, 1998). There are several important statistical issues for dependence modeling with copulas, such as statistical inference, goodness-of-fit testing and model selection. Among many others, we refer to Joe (1997), Genest and Favre (2007), Genest et al. (2009b) and Brechmann et al. (2012) for statistical issues in copula modeling. When introducing various statistical methodologies into the actuarial commu- nity, an important question that should not be avoided is the sensitivity of com- monly used risk measures to statistical modeling. As we know, nice properties of statistical methodologies are often derived based on certain nice assumptions. Moreover, there are not sufficient approaches that are tailored for tail risks. It is very challenging for a statistical method to accurately account for tail risks, espe- cially when data in the tail are sparse and rare events leading to large losses happen unexpectedly. Due to the nature of the insurance industry, financial security is par- 111ticularly important. Therefore, in practice, actuaries often need to consider more conservative modeling when there is less certainty, and conduct scenario testing accordingly. In Chapter 4, we have studied a concept of tail comonotonicity, which can be a property for absolutely continuous copulas so it is not as restrictive as the comono- tonic copula. Upper tail comonotonic copulas have an upper tail dependence pa- rameter of 1 in the sense of the usual tail dependence (Joe, 1997, section 2.1.10), and include the comonotonic copula as well as copulas satisfying the properties of upper comonotonicity (Cheung, 2009) as special cases; we refer to Dhaene et al. (2002a,b) for the former concept, and Dong et al. (2010) and Nam et al. (2011) for further study of the latter concept. For upper comonotonicity, there is a con- straint of comonotonicity beyond a certain threshold. But with tail comonotonicity, the constraint applies only for the limit and the data can contribute to the likeli- hood for the whole support of the copula. For joint non-functional-related losses, tail comonotonicity is a reasonably conservative assumption. The benefit of tail comonotonicity is that it introduces a balance between accuracy and security for assessing dependent tail risks; the less information we have and the larger losses we care about, the more conservative assumptions are adopted. We refer to Chapter 4 for more relevant research on tail comonotonicity. If X1 and X2 are dependent losses, two fundamental conditional specifications E[X1jX2 > t] and E[X1jX2 = t] that are relevant to quantitative risk manage- ment are the emphasis of this chapter. As t ! 1, for fixed margins, dependence structures will play a role in ordering the above conditional expectations. Sufficient conditions that lead to such a comparison have been derived for bivariate copulas; in particular, for Archimedean copula families, tail behavior of their generators may affect the asymptotic orders, and tail comonotonicity is proved to be conser- vative in term of the two conditional specifications. For tail comonotonic copulas, second order conditions determine the degree of conservativity, and we propose a way to compare the second order conditions. Other than the two conditional specifications, tail comonotonicity is also conservative in terms of diversification effects, thus we refer to tail comonotonicity as an asymptotically worst dependence structure. Simulations are conducted to further understand the conditions and their influ- 112ence on risk measures; tail comonotonicity is shown to be conservative as high-risk scenarios are considered. Finally, an absolutely continuous copula model with tail comonotonicity is applied to an auto insurance claim data; it was used to assess the magnitude of associated expenses based on the information of loss. This chapter is organized as following: Section 5.2 reports results on the two conditional specifications and second order properties of tail comonotonicity. The asymptotically worst dependence structure is briefly introduced in Section 5.3. Some findings of the simulation study are presented in Section 5.4, and a data anal- ysis with tail comonotonicity is conducted in Section 5.5. Section 5.6 concludes with discussions, and the proofs are collected in Section 5.7. 5.2 Conditional specifications For risk management in actuarial science or quantitative finance, one often con- cerns the influence of risks from an individual asset over the whole portfolio. If (X1; : : : ; Xd) are unbounded dependent losses, then conditional tail expectation of the forms E[ Pd i=1XijXk > t] or E[ Pd i=1XijXk = t] can be used as relevant risk measures, where t is usually a high quantile of Xk. Due to linearity of proba- bility expectations, it suffices to study the conditional tail expectation of the forms E[XijXk > t] or E[XijXk = t] for a given pair of i; k 2 f1; : : : ; dg. For notational convenience, they are written as the following two forms: E[X1jX2 > t]; (5.1) E[X1jX2 = t]: (5.2) Moreover, these forms of conditional specifications may also be used to model a univariate time series, such as, E[Xi+1jXi > t] where copulas can be used to model the serial dependence between Xi and Xi+1 (Patton, 2009). The study of these two forms in the literature has been mainly on the influence of dependence and asymptotic analysis for the threshold t in (5.1) or (5.2) goes to infinity. For example, Landsman and Valdez (2003) has a formula for (5.1) when (X1; X2) follows a bivariate elliptical distribution; Zhu and Li (2012), and Chap- ter 6 conduct asymptotic analysis under the condition of (second order) regular 113variation. In this section, we will investigate the effects of dependence structures between X1 andX2 on these two conditional specifications: E[X1jX2 > t] and E[X1jX2 = t] as t ! +1. In particular, we will study how conservative tail comonotonicity is for modeling dependent risks. From Chapters 3 and 4, we know conditions for Archimedean copulas to be tail comonotonic or satisfy other upper/lower tail prop- erties. Therefore, we illustrate general results in the special case of Archimedean copulas. In order to illustrate the conservativity of tail comonotonicity, we will focus on the bivariate case although tail comonotonicity is a multivariate concept. For multivariate extensions, the conditional specification can be of the following forms: E[X1jX2 > t1; : : : ; Xd > td] or E[X1jX2 = t1; : : : ; Xd = td]. These two forms may be useful in quantitative risk management for portfolios or constructing models for multiple regression analysis. However, these multivariate extensions are harder to analyze and are left for future study. In this section, X1; X2; Y1 and Y2 are assumed to be absolutely continuous random variables defined on [0;1), Xi; Yi  Fi; i = 1; 2, written as X;Y 2 R(F1; F2), the Fre´chet space of all bivariate random vectors whose marginal dis- tributions are F1 and F2, respectively, and F1; F2 are absolutely continuous and defined on [0;1). Furthermore, E[Xi] = E[Yi] < 1; i = 1; 2. Then sufficient conditions that lead to the following two asymptotic inequalities will be studied. Relation (I): E[X1jX2 > t] % E[Y1jY2 > t]; t!1; (5.3) Relation (II): E[X1jX2 = t] % E[Y1jY2 = t]; t!1: (5.4) In our analysis of the above two conditions, the limits of the conditional dis- tributions of bivariate copulas will appear, specifically C1j2(uj1) and C1j2(uj0) for 0 < u < 1. If (U; V )  C, these are the limiting distributions of [U jV = v] as v ! 1 or v ! 0+. These limiting conditional distributions appear in the tail dependence results in Cooke et al. (2011). With this form of dependence in the tails, C1j2(uj1) = 0 for 0  u < 1 or [U jV = 1] degenerates at 1 is the strongest upper tail dependence and C1j2(uj0) = 1 for 0 < u  1 or [U jV = 0] degenerates at 0 is the strongest lower tail depen- dence. Note that, this tail dependence form is different from the widely-used form 114of tail dependence defined in Joe (1997, section 2.1.10); we refer to the latter as “usual tail dependence” in this chapter. Next consider the special case of a bivari- ate Archimedean copula constructed by a LT  . The condition C1j2(uj1) = 0 for 0  u < 1 occurs when  0(0) =  1; this is also the condition to achieve the usual upper tail dependence. The condition C1j2(uj0) = 1 for 0 < u  1 occurs as 0 < r < 1 under the mild assumption  (s)  T (s) = a1sq expf a2srg with a1 > 0, a2  0, and  0(s) = T 0(s), as s!1; this matches conditions to achieve either lower tail dependence or intermediate lower tail dependence, i.e., a lower tail order of 1   < 2. 5.2.1 The case: E[X1jX2 > t] Proposition 5.1 Suppose X := (X1; X2), Y := (Y1; Y2) and X;Y 2 R(F1; F2). (a) If X  C (F1; F2), Y  C(F1; F2) and limv!1 [C  1j2(ujv)=C1j2(ujv)] < 1 for all 0 < u < 1, then Relation (I) holds. (b) If bC and bC are survival copulas of C and C, X  bC (F1; F2), Y  bC(F1; F2), and limv!0+ [1  C  1j2(ujv)]=[1  C1j2(ujv)] < 1 for all 0 < u < 1, then Relation (I) holds. For constructing Relations (I) and (II) for Archimedean copulas, the upper and lower tail behavior of the following function is critical:  (u) := ’0(’ 1(u))  0(  1(u)) ; u 2 [0; 1]; (5.5) where  and ’ are LTs for the copulas or survival copulas of X and Y. Note that  (0) and  (1) are defined as the corresponding limits, provided that the limits exist. Corollary 5.2 Suppose X := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2), and their dependence structures are C and C that are bivariate Archimedean copulas constructed as in (2.4) with LTs  and ’, respectively. If  (1) exists and  (u) >  (1) for any 0 < u < 1, then Relation (I) holds. Remark 5.1 The condition (5.12) in the proof of Proposition 5.1 holds if C is larger than C in the PQD or concordance ordering (Joe, 1997; Shaked and Shan- thikumar, 2007). The concordance ordering holds for copulas from a single com- 115monly used 1-parameter bivariate copula family, but Proposition 5.1 and Corollary 5.2 can also be applied to two copulas from two different parametric families, as shown in Example 5.1. Remark 5.2 The condition  (u) >  (1) is the same as  0(s) >  0(0), where  =   1  ’. For concordance ordering of the two Archimedean copulas, a sufficient condition is  convex or  0(s) increasing in s (Joe, 1997, Corollary 4.2). So the tail comparison here is satisfied with the weaker condition. Example 5.1 (a) (bivariate MTCJ vs Gumbel). The MTCJ copula is based on the gamma LT (1 + s) 1= M for  M > 0 and the Gumbel copula is based on the positive stable LT expf s 1= Gg for  G > 1. Suppose the parameters for MTCJ and Gumbel copulas are  M and  G, respectively, and the LTs are ’ and  , respectively. Then  (u) = ( G= M )u M ( log u) G 1, and  (u) >  (1) = 0 for any 0 < u < 1. So if (X1; X2) has an arbitrary Gumbel copula and (Y1; Y2) has an arbitrary MTCJ copula, and Xi; Yi  Fi, i = 1; 2, Relation (I) must hold. This case is not surprising as the Gumbel copula has upper tail dependence for any  G > 1 and the MTCJ has upper tail order of 2 (upper tail independence) for any  M > 0. That is, the Gumbel copula has more probability in the upper tail and C  (u; v;  G)  C(u; v;  M ) or equivalently C (u; v;  G)  C(u; v;  M ) for (u; v) in an upper set S G; M that can include most of [0; 1] [v0; 1] for some v0 near 1. (b) (Joe/B5 vs Gumbel). The Joe/B5 copula is based on the Sibuya LT 1 (1 e s)1= J for  J > 1. This also has upper tail dependence so the comparison with Gumbel requires a careful check of the conditions in Proposition 5.1. Let CJoe and CGum denote these two copula families. Using the conditional distributions on page 147 of Joe (1997), as v ! 1 , CJoe1j2 (ujv;  J) CGum1j2 (ujv;  G)  (1 u)  J+1f1 (1 u) Jg u( log u)  G+1  (1 v) G  J : The limit is 0 if  G >  J and 1 if  G <  J and finite if  G =  J . In the latter case, the ratio is shown numerically to be > 1 for 0 < u < 1. Therefore if  G =  J =  > 1 and C = CGum(;  ), C = CJoe(;  ), (X1; X2)  C (F1; F2), (Y1; Y2)  C(F1; F2), then Relation (I) must hold. 116For applications in insurance and finance, we often need to consider a copula that has upper tail dependence. In Table 5.1, we summarize the comparison in the sense of Corollary 5.2 for some commonly used upper tail dependent copulas. Table 5.1: Comparisons between Gumbel, Joe, BB1 and BB7 copula families Copula Parameter(s)  0 (  1  (u)) Domination term as u! 1  Gumbel 1    1 u( log u) 1  ( log u)1   (1 u)1  Joe 1    1 (1 u) 1  [1 (1 u) ] (1 u)1  BB1 1   ; 0 <   1  u  +1(u   1)1  (u   1)1    1  (1 u)1  1 BB7 1   ; 0 <   1  (1 u) 1  [1 (1 u) ]1+ (1 u)1  1. The notation of  ;  is exchanged from their original form in Joe (1997). Note that the upper tail dependence parameters for all of the copulas in Table 5.1 are 2 21= . The following result tells us how to use Table 5.1. It includes part (b) of Example 5.1 as a special case. Corollary 5.3 Suppose X := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2), and their dependence structures are copulas C ; C 2 fGumbel; Joe;BB1; BB7g. If   >  , that is, the upper tail dependence parameters   >  , then Relation (I) holds. For modeling dependent risks, we often need to reflect a copula to get its sur- vival copula. Now we consider the condition in the sense of Corollary 5.2 when survival Archimedean copulas are used to model the risks. Proposition 5.4 SupposeX := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2);X  bC (F1; F2) and Y  bC(F1; F2), where C and C are Archimedean copulas con- structed as in (2.4) with LTs  and ’, respectively. If   1  ’(s) is strictly convex for large s, and in particular, if  (u) is strictly decreasing in u near 0, then Rela- tion (I) holds. Remark 5.3 The above result is on the ordering in the lower tail of two copulas. For the concordance ordering of Archimedean copulas C and C’, a sufficient condition is that   1  ’ is superadditive (Joe, 1997, Theorem 4.1), which holds if   1  ’ is convex due to   1(’(0)) = 0 (Marshall and Olkin, 2007, Proposition 117A.11). Obviously, when   1  ’ is superadditive, Relation (I) must hold. But the condition in Proposition 5.4 only requires the superadditive inequality where one argument is large and the other is arbitrary, and this can be checked via the behavior of  (u) as u! 0+. From Corollary 5.2 and Proposition 5.4, the tail behavior of  0(  1(u)) for u ! 1 and u ! 0+ determine the upper and lower tail association in the sense of Relation (I). So we list in Table 5.2 the conditions for commonly used 1- or 2-parameter Archimedean copulas that appear in Joe (1997). By looking at the conditions listed in Table 5.2, we can easily compare the conditional tail expecta- tions in the sense of Proposition 5.4 (or Corollary 5.2). Those conditions will also be used in the next subsection for establishing Relation (II). Example 5.2 (BB3 vs BB2) As u ! 0+, observed from Table 5.2, the term e  (u    1) for BB2 and the term e  ( log u)  for BB3 dominate the condition in Proposition 5.4 for comparing these two copulas. It is clear that the former term goes to 0 much faster than the latter as u ! 0+. Therefore, if C is BB2 and C is BB3, and bC and bC are copulas for X and Y respectively, then Relation (I) holds. It is also consistent to the fact that BB2 is more lower tail positive depen- dent than BB3 below a sufficiently low threshold, although they are both lower tail comonotonic (see Chapter 4). Table 5.2 shows that  0(  1(0))  0 for all of the Archimedean copula fam- ilies listed, which reminds us of the following fact: suppose  (s) is the LT of a positive random variable, then  (n)(s)! 0 as s!1 for n = 1; 2; : : : . The following lemmas are the Monotone Density Theorem (Bingham et al., 1987; Embrechts et al., 1997) applied to slowly varying functions. Lemma 5.5 Let ‘ 2 RV0 and ‘(s) = R s 0 ‘ 0(u)du (or ‘(s) = R1 s ‘ 0(u)du). If ‘0(s) is ultimately monotone as s!1, then ‘0(s) = o(s 1‘(s)); s!1. Lemma 5.6 Let ‘ 2 RV0(0+) and ‘(s) = R s 0 ‘ 0(u)du. If ‘0(s) is ultimately mono- tone as s! 0+, then ‘0(s) = o(s 1‘(s)); s! 0+. 118Table 5.2: Condition  0(  1(u)) for parametric Archimedean copula fami- lies Copula Parameter(s)  0(  1(u)) Frank(B3) 0   (1= )(1 e u) MTCJ(B4) 0    (1= )u1+ Joe(B5) 1    (1= )(1 u)1  [1 (1 u) ] Gumbel(B6) 1    (1= )u( log u)1  BB1 1   ; 0 <   (1=(  ))u +1(u   1)1  BB2 0 <  ;   (1=(  ))u1+ e  (u    1) BB3 0 <  ; 1    (1=(  ))u( log u)1  e  ( log u)  BB6 1   ;   (1=(  ))(1 u)1  [1 (1 u) ][ log[1 (1 u) ]]1  1 BB7 1   ; 0 <   (1=(  ))(1 u)1  [1 (1 u) ]1+ BB8 0 <   1; 1    (1=(  ))(1  u)1  + (1=(  ))(1  u) BB9 0   ; 1    (1= )u(  log u)1  BB10 0 <  ; 0    1   u   (1  ) 1u1+1= 1. The notation of  ;  is exchanged from their original form in Joe (1997). The following two results establish that tail comonotonicity is conservative in the sense of Relation (I). By Proposition 4.3, the condition 1   2 RV0(0+) in Corollary 5.7 implies that the Archimedean copula C is upper tail comonotonic, and the condition  2 RV0 in Corollary 5.8 implies that the Archimedean copula C is lower tail comonotonic. Corollary 5.7 Suppose X := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2), and their dependence structures are C and C that are Archimedean copulas con- structed as in (2.4) with LTs  and ’, respectively. If 1   2 RV0(0+) and lim infs!0+( s’ 0(s))=(1 ’(s)) > 0, then Relation (I) holds. Corollary 5.8 Suppose X := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2);X  bC (F1; F2) and Y  bC(F1; F2), where C and C are Archimedean copulas con- structed as in (2.4) with LTs  and ’, respectively. If  (u) is ultimately monotone as u ! 0+,  2 RV0 and lim infs!1( s’0(s))=(’(s)) > 0, then Relation (I) holds. 119Remark 5.4 The conditions lim inf s!0+ ( s’0(s))=(1 ’(s)) > 0 (for copula upper tail); lim inf s!1 ( s’0(s))=’(s) > 0 (for copula lower tail) cover tail dependence, intermediate tail dependence and tail orthant independence cases. For instance, for the case of lower tail, let  :=  lims!1(s’0(s))=’(s), then  = 0 is tail comonotonicity, 0 <  <1 corresponds to tail dependence and  =1 leads to intermediate tail dependence or near independence (tail order  = d). We refer to Charpentier and Segers (2009) and Chapter 3 for a comprehensive study of tail behavior of Archimedean copulas. 5.2.2 The case: E[X1jX2 = t] In this subsection, we have some parallel results to the previous subsection by conditioning on X2 = t instead of X2 > t. Proposition 5.9 Suppose X := (X1; X2), Y := (Y1; Y2) and X;Y 2 R(F1; F2). (a) If X  C (F1; F2), Y  C(F1; F2) and limv!1 [C  1j2(ujv)=C1j2(ujv)] < 1 for all 0 < u < 1, then Relation (II) holds. (b) If bC and bC are survival copulas of C and C, X  bC (F1; F2), Y  bC(F1; F2) and limv!0+ [1  C  1j2(ujv)]=[1  C1j2(ujv)] < 1 for all 0 < u < 1, then Relation (II) holds. Corollary 5.10 Suppose X := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2), and their dependence structures are C and C that are Archimedean copulas con- structed as in (2.4) with LTs  and ’, respectively. If  (1) exists and  (u) >  (1) for any 0 < u < 1, then Relation (II) holds. Corollary 5.11 SupposeX := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2);X  bC (F1; F2) and Y  bC(F1; F2), where C and C are Archimedean copulas con- structed as in (2.4) with LTs  and ’, respectively. If   1  ’(s) is strictly convex for large s, and in particular if for u sufficiently small,  (u) is strictly decreasing in u, then Relation (II) holds. 120Corollary 5.12 Suppose X := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2), and their dependence structures are C and C that are Archimedean copulas con- structed as in (2.4) with LTs  and ’, respectively. If 1   2 RV0(0+) and lim infs!0+( s’ 0(s))=(1 ’(s)) > 0, then Relation (II) holds. Corollary 5.13 SupposeX := (X1; X2), Y := (Y1; Y2), X;Y 2 R(F1; F2);X  bC (F1; F2) and Y  bC(F1; F2), where C and C are Archimedean copulas con- structed as in (2.4) with LTs  and ’, respectively. If  (u) is ultimately monotone,  2 RV0 and lim infs!1( s’0(s))=(’(s)) > 0, then Relation (II) holds. 5.2.3 Second order conditions and conservativity From Example 5.2, we notice that BB2 in general has a stronger positive associa- tion in the lower tail than BB3, although BB2 and BB3 are both lower tail comono- tonic. In this subsection, we will study second order conditions under which tail comonotonicity may appear various strength of lower tail dependence. Suppose that copula C is lower tail comonotonic, if there exist auxiliary func- tions A(t); B(t) such that lim u!0+ B  C(uw1;:::;uwd) u  min(w1; : : : ; wd)  A(u) = H(w1; : : : ; wd) 6 0; w > 0; (5.6) then the speed of convergence can be characterized by the auxiliary functions A(t) and B(t). Second order conditions may play an important role in modeling high-risk sce- narios. We refer to Degen et al. (2010) and Chapter 6 for relevant studies under the condition of second order regular variation (de Haan and Stadtmu¨ller, 1996). The idea here is similar to second order regular variation, but the auxiliary func- tions A(t) and B(t) may have various forms other than a power function as in the second order regular variation. Lemma 5.14 Let C be a d-dimensional Archimedean copula constructed with LT  2 RV0 as in (2.4). Letting w := minfw1; : : : ; wdg, and w is unique in w, 121then as u! 0+, C(uw1; : : : ; uwd)  uw  +  0(  1(uw ))  X i2fj:wj 6=w g    1(uwi)  =: uw +  (u;w); where the second order term  (u;w) = o(u); u! 0+. Based on Lemma 5.14, one can obtain concrete forms of second order terms. The following are examples for the bivariate case of BB2 and BB3, for which the auxiliary functions have simple forms. The derivation is straightforward by using Lemma 5.14, thus omitted. Example 5.3 (BB2) The auxiliary functions for BB2 copula are A(t) = t  ;  > 0; B(t) = log jtj; that is, lim u!0+ log    C (uw1;uw2) u  min(w1; w2)    u  =: H(w1; w2) =   jw   1  w   2 j: Therefore,  is the second order parameter that dominates the speed of conver- gence. Example 5.4 (BB3) The auxiliary functions for BB3 copula are A(t) = ( log t)  1;  > 1; B(t) = log jtj; that is, lim u!0+ log    C (uw1;uw2) u  min(w1; w2)    ( log u)  1 =: H(w1; w2) =    j logw1  logw2j: 122So  is the second order parameter. For statistical inference with BB2 or BB3 in the lower tail, if we want to reduce the number of the parameters we can also consider to simply assume  to be a fixed number, say  = 0:01 or  = 0:1. From computational experiments, we find that there are some redundancies when we involve the parameter  . For both BB2 and BB3, when  is larger, both u  and ( log u)  1 converges to +1 faster as u! 0+ and the copula has more positive dependence at the lower sub-extreme. By looking at the auxiliary functions of BB2 and BB3, we may also conclude that BB2 is more lower tail positive dependent than BB3 below a sufficiently low threshold, which is consistent to Example 5.2. The reason is due to u  BB2 goes to +1 faster than ( log u) BB3 1 as u ! 0+, and this just implies that the con- vergence of CBB2(uw1; uw2)=u ! minfw1; w2g is faster than the convergence of CBB3(uw1; uw2)=u! minfw1; w2g, as u! 0+. 5.3 Asymptotically worst dependence structures As we have shown in Section 5.2, tail comonotonicity affects the conditional spec- ifications. Moreover, tail comonotonicity may also influence risk measures for aggregate losses. For instance, if Xi d = Yi; i = 1; : : : ; d, and the dependence struc- tures of (X1; : : : ; Xd) and (Y1; : : : ; Yd) are different, then risk measures on P Xi and P Yi may be different, although they have the same marginal distributions. In this section, we will briefly study an asymptotically worst dependence concept brought about by tail comonotonicity. Let X = (X1; : : : ; Xd) be a d-dimensional real-valued random vector, with univariate cdf’s F1; : : : ; Fd. The Fre´chet space containing all the random vectors that possess these margins is denoted as R(F1; : : : ; Fd). Suppose g : Rd ! R is an aggregate function, and Qp : R ! R+ is a quantile based risk measure, where p is the probability level and is usually close to 1. Then X is said to have an asymptotically worst dependence structure in terms of the risk measure Qp and the aggregate function g, if for any X0 2 R(F1; : : : ; Fd), Qp[g(X)] & Qp[g(X0)]; p! 1 : (5.7) 123Note that, in Mainik and Ru¨schendorf (2012), an asymptotic ordering called asymp- totic portfolio loss order has been used to compare the tail probabilities of two portfolios. In this section, we will study situations where tail comonotonicity becomes an asymptotically worst dependence structure. For the choice of risk measures, we restrict ourself to VaR and CTE. For aggregate functions, we only consider the case where g(x1; : : : ; xd) = Pd i=1 xi. Then this concept is also relevant to conservative assessments for diver- sification effects of risk aggregation. 5.3.1 Conditional tail expectation Assumption 5.1 Let X be an upper tail comonotonic non-negative random vector with continuous marginal distributions F1; : : : ; Fd defined on [0;1) such that 1 Fi(t) 2 RV  with  > 1, and lim t!1 [1 Fi(t)]=[1 F1(t)] = ki; 0 < ki <1 for any i = 1; : : : ; d. The next result shows that tail comonotonicity is an asymptotically worst depen- dence structure for tail equivalent random variables that have regularly varying upper tails. Proposition 5.15 If X satisfies Assumption 5.1, then for any X0 2 R(F1; : : : ; Fd), CTEp(X1 +    +Xd) & CTEp(X 0 1 +    +X 0 d); p! 1  : In this sense, upper tail comonotonicity provides a conservative dependence structure. Assumption 5.2 Let X be an upper tail comonotonic non-negative random vec- tor with continuous marginal distributions F1; : : : ; Fd defined on [0;1) and there 124exists a positive measurable function a( ) such that lim t!1 1 Fi(t+ a(t)s) 1 Fi(t) = es; 8s 2 R; (5.8) and limt!1[1 Fi(t)]=[1 F1(t)] = ki with 0 < ki <1 for any i = 1; : : : ; d. Due to Proposition 4.16, we also have the following result. Proposition 5.16 If X satisfies Assumption 5.2, then for any X0 2 R(F1; : : : ; Fd), CTEp(X1 +    +Xd) & CTEp(X 0 1 +    +X 0 d); p! 1  : 5.3.2 Value at risk Based on Propositions 4.13 and 4.14, it suffices to find conditions on the depen- dence structure for X, where the margins are specified by either Assumptions A or B, so that VaRp becomes subadditive as p is in a left-neighbor of 1. It is well known that, in general VaR is not subadditive. However, with certain regularity conditions, VaR may also be subadditive; the study of the regularity conditions has been an important topic in the literature but is out of the scope of this chapter. In what follows, some results for asymptotically worst dependence structure in terms of VaR will be given. Those are based on known facts of situations where VaR becomes (asymptotically) subadditive. Proposition 5.17 Let X0 = (X 01; : : : ; X 0 d) 2 MRVd(  );  > 1, with identically distributed margins on [0;1), and X is upper tail comonotonic withXi d = X 0i; i = 1; : : : ; d. Then VaRp(X1 +    +Xd) & VaRp(X 0 1 +    +X 0 d); p! 1  : For marginal distributions that are in the Maximum Domain of Attraction of Gumbel, subadditivity of VaR may hold for multivariate elliptical distributions (McNeil et al., 2005, Theorem 6.8). So the following result holds. 125Proposition 5.18 Let X0 = (X 01; : : : ; X 0 d) be a d-dimensional elliptical distribu- tion where the margins satisfying (5.8) with support ( 1;1), and X is upper tail comonotonic with Xi d = X 0i; i = 1; : : : ; d. Then VaRp(X1 +    +Xd) & VaRp(X 0 1 +    +X 0 d); p! 1  : 5.4 Simulation study In this section, we report on simulations to illustrate the conservativity of tail comonotonicity in terms of E[X1jX2 > VaRp(X2)], E[X1jX2 = VaRp(X2)], VaRp(X1 +X2) and CTEp(X1 +X2) as p! 1 , respectively. 5.4.1 Conditional specifications Now we compare the effects onE[X1jX2 > VaRp(X2)] andE[X1jX2 = VaRp(X2)] for different Archimedean copulas. Comonotonicity and the survival copulas of BB2, MTCJ, Gumbel and Frank (denoted as s.BB2, s.MTCJ, s.Gumbel and s.Frank) are used to model the dependence structures of two marginsX1 andX2; that is, the strength of upper tail positive dependence are different. From Chapter 3, consider- ing both tail order and tail dependence parameters, the strength of dependence in the upper tail can be ordered as: Comonotonicity  s.BB2  s.MTCJ  s.Gumbel  s.Frank: (5.9) Choosing parameters for these Archimedean copulas to have the same Blomqvist’s  (since MLEs for different copula families often lead to a similar Blomqvist’s  ), we can compare the values of E[X1jX2 > VaRp(X2)] or E[X1jX2 = VaRp(X2)] for different Archimedean copulas. Figures 5.1 and 5.2 contain the plots for the two conditional specifications, respectively, with p ranging from 80% to 99%; this range is most relevant for applications. The calculations were based on numerical integration through Monte Carlo simulations. For both figures, the copula param- eters used in the simulation were the following: BB2:  = 2;  = 0:4; MTCJ:  = 1:424; Gumbel:  = 1:729 and Frank:  = 3:844. The common Blomqvist’s  was 0:421. 126From Figure 5.1, it is interesting that, when p is sufficiently large (say, p  80% for the plots), the order of the values of E[X1jX2 > VaRp(X2)] is the same as the order in (5.9). Moreover, the same pattern can be observed regardless of the mar- gins. The pattern for Figure 5.2 is different than that for E[X1jX2 > VaRp(X2)]. But when p is sufficiently large (say, p  95% in this example), the rank of E[X1jX2 = VaRp(X2)] is still kept as the same as (5.9). Based on these comparisons, the BB2 tail comonotonic copula is reasonable and better than the usual comonotonicity when considering a relatively lower prob- ability level p where more data are available. However, as less data are available when considering a higher probability level, the tail comonotonic copula becomes more conservative and approaches the upper bound provided by the usual comono- tonicity. This kind of conservatism makes more sense for quantitative risk man- agement. 5.4.2 Asymptotically worst dependence structures To illustrate the idea of applying tail comonotonicity for a reasonably conservative modeling of high-risk scenarios, we generated a bivariate data with s.BB1 as the copula and standard Pareto as the margins. We transformed the data to rank scores and fitted these bivariate rank scores by copulas that possess upper tail dependence, such as t, Gumbel, s.BB2 and s.BB3 copulas. The estimation was based on max- imum quasi-likelihood estimation and the margins are assumed to be given. The Monte Carlo method was then used to approximate the corresponding VaRs and CTEs, based on the fitted copulas and the known margins. Table 5.3 and 5.4 present comparisons between different copulas, for the cases of more dependence and less dependence, respectively. The margins were standard Pareto distributions with cdf F (x) = 1  x 3. For each run, the sample size was 105 and “se”, the simulation error for the Monte Carlo method was based on 1; 000 runs. In the tables, “ind” is for the independent case, “s.BB1.o” means s.BB1 is the true model, and  is the corresponding upper tail dependence parameter. Table 5.3 shows that: (1) Overlooking the dependence between margins will seriously underestimate the aggregate risks. (2) The Gumbel copula has the small- est AIC, but it underestimates the risks. (3) The BB2 and BB3 copulas overesti- 127Figure 5.1: The value of y-axis is E[X1jX2 > VaRp(X2)]. In the first plot, X1; X2 have Exponential distributions with cdf F (x) = 1  Exp( x= ). In the second plot, X1; X2 have Pareto distributions with cdf F (x) = 1 (1 + x= )  . Exponential margins with σ = 10 p E(  X 1 | X 2  > t ) 20 30 40 50 0.80 0.85 0.90 0.95 Comonotonicity s.BB2 s.MTCJ s.Gumbel s.Frank Pareto margins with θ = 3, σ = 20 p E(  X 1 | X 2  > t ) 20 40 60 80 100 120 0.80 0.85 0.90 0.95 Comonotonicity s.BB2 s.MTCJ s.Gumbel s.Frank 128Figure 5.2: The value of y-axis is E[X1jX2 = VaRp(X2)]. In the first plot, X1; X2 have Exponential distributions with cdf F (x) = 1  Exp( x= ). In the second plot, X1; X2 have Pareto distributions with cdf F (x) = 1 (1 + x= )  . Exponential margins with σ = 10 p E(  X 1 | X 2  = t ) 20 30 40 0.80 0.85 0.90 0.95 Comonotonicity s.BB2 s.MTCJ s.Gumbel s.Frank Pareto margins with θ = 3, σ = 20 p E(  X 1 | X 2  = t ) 20 40 60 0.80 0.85 0.90 0.95 Comonotonicity s.BB2 s.MTCJ s.Gumbel s.Frank 129mate the risk a little bit and could provide a reasonable conservative estimate for this case, especially when the probability level p is very high. (4) The tail depen- dence parameters seem relevant to the magnitude of the aggregate risks: a higher tail dependence parameter tends to lead to a larger aggregate risk, especially when the probability level is quite high. This also suggests that tail comonotonicity is conservative under some conditions. Comparing Tables 5.3 and 5.4, we can also find that overestimation arising from tail comonotonicity is milder for the case of stronger tail dependence than for the case of less tail dependence. Table 5.3: (More dependence) VaR and CTE for X1 + X2. The MLEs were based on the whole sample generated from s.BB1 ( = 1:57;  = 1:68;  = 0:77, more dependence) with a sample size of 2000. The bold AIC value is the smallest. VaR90 VaR995 CTE90 CTE995 AIC  ind 4.084 8.949 5.532 12.631 - - se <0.001 0.003 0.001 0.010 s.BB1.o 4.270 11.573 6.400 17.369 - 0.77 se <0.001 0.005 0.001 0.017 t 4.269 11.275 6.315 16.842 -2678.4 0.53 se <0.001 0.005 0.001 0.016 Gumbel 4.272 11.537 6.389 17.289 -2887.7 0.73 se <0.001 0.005 0.001 0.016 s.BB2 4.279 11.616 6.419 17.411 -2737.6 1 se <0.001 0.005 0.001 0.017 s.BB3 4.263 11.643 6.416 17.440 -2782.7 1 se <0.001 0.005 0.001 0.016 With the same simulation settings, we repeated the experiment for 40 times. Table 5.5 is a summary of these simulations. The approach is conservative only when the probability level is very high (say, > 99%). With a moderately high probability level, say, 90%, this approach would be not conservative. Moreover, tail comonotonicity worked better for the case where the original dependence is actually stronger. 130Table 5.4: (Less dependence) VaR and CTE for X1 + X2. The MLEs were based on the whole sample generated from s.BB1 ( = 2;  = 0:4;  = 0:42, less dependence) with a sample size of 2000. The bold AIC value is the smallest. VaR90 VaR995 CTE90 CTE995 AIC  ind 4.083 8.951 5.531 12.641 - - se <0.001 0.004 0.001 0.010 s.BB1.o 4.252 11.094 6.249 16.515 - 0.42 se <0.001 0.005 0.001 0.015 t 4.262 11.100 6.261 16.465 -2099.0 0.39 se <0.001 0.005 0.001 0.015 Gumbel 4.245 11.417 6.331 17.105 -1946.5 0.65 se <0.001 0.006 0.001 0.015 s.BB2 4.234 11.480 6.345 17.225 -1574.3 1 se <0.001 0.005 0.001 0.016 s.BB3 4.217 11.192 6.220 16.994 -2054.5 1 se <0.001 0.005 0.001 0.017 5.5 Application on a claim dataset We now illustrate how tail comonotonicity can be used in real applications with the dataset of Loss (X2) versus Allocated Loss Adjustment Expense (ALAE) (X1) that has been studied in the literature such as Frees and Valdez (1998), and Klugman and Parsa (1999). The margins will be fitted by a Pareto distribution with the following distribution function (same as in Frees and Valdez (1998)) F (x) = 1  (1 + x= )  . Its density and inverse functions are f(x) = ( = ) (1 + x= )   1 and F 1(x) =  (1 x) 1=   . In addition to the Gumbel copula fitted in Frees and Valdez (1998), we use s.BB2 as a conservative model to fit the dependence structure as well. ALAE for auto insurance claims often involves legal expenses that could be very large. Just like the current dataset, for a relatively small amount of loss, the ALAE can be very large and there are usually no limits that can be set for the expense. If one wants to assess the risk of future total loss and expense, one way is to think that the historical dataset reflects the future claims. However, this method may be too optimistic and does not account for the uncertainty beyond the historical dataset. 131Table 5.5: VaR and CTE for X1 + X2. The first 4 columns of values are the means of corresponding quantities calculated from 40 random samples generated from the same settings as before. The rest are frequency of those quantities being greater than those for s.BB1.o. (A: more depen- dent, large sample size; B: less dependent, large sample size; C: more dependent, small sample size; D: less dependent, small sample size. More dependence:  = 1:57;  = 1:68;  = 0:77; less dependence:  = 2;  = 0:4;  = 0:42. Large sample: N = 2000; small sample: N = 50.) VaR90 VaR995 CTE90 CTE995 VaR90 VaR995 CTE90 CTE995 A s.BB1.o 4.270 11.574 6.400 17.365 t 4.266 11.281 6.313 16.845 2 0 0 0 Gumbel 4.270 11.536 6.387 17.303 15 0 0 0 s.BB2 4.277 11.612 6.417 17.423 40 40 40 40 s.BB3 4.249 11.643 6.408 17.490 0 40 33 40 B s.BB1.o 4.251 11.090 6.247 16.510 t 4.257 11.093 6.255 16.488 38 21 35 13 Gumbel 4.243 11.397 6.324 17.085 0 40 40 40 s.BB2 4.230 11.462 6.337 17.198 0 40 40 40 s.BB3 4.219 11.168 6.218 16.960 0 31 1 40 C s.BB1.o 4.270 11.573 6.399 17.355 t 4.263 11.279 6.310 16.818 18 0 0 0 Gumbel 4.270 11.530 6.385 17.293 18 8 10 9 s.BB2 4.274 11.624 6.421 17.439 23 34 34 34 s.BB3 4.253 11.620 6.398 17.459 15 35 24 36 D s.BB1.o 4.251 11.087 6.247 16.499 t 4.255 11.129 6.262 16.527 22 26 26 22 Gumbel 4.248 11.420 6.335 17.120 15 40 40 40 s.BB2 4.236 11.529 6.359 17.298 9 40 40 40 s.BB3 4.228 11.325 6.279 17.062 7 32 25 36 We now remove the data where ALAE is among the 1% largest, pretending that we have not observed those large values yet. Then we use a Pareto dis- tribution to fit the margins for the rest of the dataset, and use the Gumbel and s.BB2 copulas to fit the dependence structure, respectively. Then simulations based on the two fitted models will be used to assess high senario risks in terms of E[ALAEjLOSS  VaRp(LOSS)]; the results for the Gumbel, s.BB2 copulas, and empirical assessement based on the data will be compared. See Figure 5.3 for a comparison, where calculations were based on the mean of 40 simulations, and 132for each simulation a random sample of size 106 was used. When the number of simulations was about 40, the mean became stable. First, we fit the two margins separately and get the estimates (Table 5.6). Com- pared to the estimates for the original data (replicated from Frees and Valdez (1998)), the upper tails of both X1 and X2 become a bit lighter (because of larger  ^). Table 5.6: Estimates for margins with/without 1% largest ALAE removed LOSS(X2) ALAE(X1) Data Parameters Estimate Standard error Estimate Standard error 1% removed  17189 1714 21686 2964  1.310 0.082 3.045 0.323 original  14453 1397 15133 1633  1.135 0.066 2.223 0.175 Then we assume that the margins are known as what we have estimated in Table 5.6 and plug them into likelihood functions. Considering there are 31 (originally 34) right-censored loss data (X2), the likelihood contributed by a right-censored data value (x1; x2) is f1(x1) (@1F )(x1; x2) = f1(x1)  1 C2j1(F2(x2)jF1(x1))  : The estimation is reported in Table 5.7, and a comparison for different models is illustrated in Figure 5.3. From this example, we can observe that, as the prob- ability level p is very large (eg, 99:5%), “empirical” is not able to extrapolate the increasing trend from the original data and even gives a counter-intuitive decreas- ing trend, but s.BB2 leads to a reasonable extrapolation beyond the original data although s.BB2 was fitted based on the data with the 1% largest removed. The Gumbel copula seems to be reasonably good as well but has a limited ability in extrapolation compared with s.BB2. In this particular example, s.BB2 outperforms the other models and provides reasonable extrapolation into high-risk scenarios. Furthermore, the choice of  controls the degree of conservativity of s.BB2. 133Table 5.7: Comparison between the s.BB2 and Gumbel copulas: from the second order condition of BB2, we know that  is the second order pa- rameter that dominates the tail behavior of the copula. If we fit  ;  of s.BB2 simultaneously for the data, then the MLE of  tends to be very close to 0; that is, s.MTCJ could better fit the data than s.BB2 (since as  ! 0, BB2 becomes MTCJ). But s.MTCJ will lose the asymptotic full dependence structure. The aim of this comparison is not to find the best fitting copula but to study how conservative s.BB2 is. So we fix two val- ues for the parameter  for s.BB2 ( = 0:01; 0:1), and then obtain the MLEs of  . Model Parameters Estimate Standard error Blomqvist’s  s.BB2( = 0:01)  0.679 0.041 0.254 s.BB2( = 0:10)  0.520 0.026 0.226 Gumbel  1.399 0.028 0.283 5.6 Discussion “Essentially, all models are wrong, but some are useful.” (Box and Draper, 1987). By showing the conservativity of tail comonotonicity and its usage in accounting for unexpected rare events (especially with the promising result illustrated in Fig- ure 5.3), we are not conveying the information that tail comonotonicity is better than other candidate models. Through this study, we hope to be able to raise an issue that has not been attracted enough attention, but may be useful in dependence modeling when one has to assess dependent large risks relying on insufficient in- formation. Since there are very few insurance claim datasets available to the aca- demic researchers and actuaries, more empirical evidence for the usefulness of tail comonotonicity must be welcome. An absolutely continuous copula that is tail comonotonic can provide more re- alistic conservative bounds on risk measures than the comonotonic copula. Of the simple 2-parameter bivariate copula families, the BB2 family is the most tail asym- metric with lower tail comonotonicity and upper tail independence (upper tail order 2). The BB3 copula is also lower tail comonotonic but has upper tail dependence. As shown in Example 5.2 and Section 5.2.3, BB3 has a slower rate of convergence to the limiting comonotonic tail dependence function. It is shown that the survival 134Figure 5.3: The value of y-axis is E[ALAEjLOSS  VaRp(LOSS)]. “Empir- ical” is based on the data with the 1% largest removed; “Empirical0” is based on the original data. p 40000 60000 80000 100000 120000 90% 95% 99% 99.5% G G G G G G G G G G G G G G G G G G Empirical Empirical0 s.BB2(δ= 0.01) s.BB2(δ= 0.1) Gumbel BB2 copula family provides conservative bounds as a model for bivariate loss data if in fact there is upper tail dependence but not tail comonotonicity. The survival BB3 copula family may also provide bounds if one does not want to use a copula as conservative as BB2. When a tail comonotonic copula is used to provide a data-driven conservative dependence structure, it is suggested to first fit the margins separately then fit the copula based on the uniform scores derived from the fitted margins. If one wants to fit the copula and margins simultaneously, misspecification of the copula model may affect the estimation for margins severely. Moreover, under such a situation, computation may become more complicated. For multivariate loss data, one can use multivariate extensions of the BB2 and 135BB3 copulas. The BB2 and BB3 families are bivariate Archimedean copulas based on LT families given in Examples 4.1 and 4.2, so there is an obvious extension to exchangeable multivariate Archimedean copulas by the definition. Because the two LT families belong to RV0, they can be used to get multivariate tail comonotonic copulas with more flexible non-extremal dependence via the mixture of max-id copula families (Joe and Hu, 1996). Although the influence is not large, the choice of the BB2 dependence parame- ter  in the data analysis example was arbitrary. The  can be thought as a parameter to control the degree of conservativity. For future research, we expect that there are certain criteria due to statistical and/or economic reasons to get the parameter  ap- propriately tuned. Moreover, we will also look more closely at the tail expansions of E[X1jX2 > t] and E[X1jX2 = t] for different marginal distributions of X1 and X2. The strength of the dependence in the tail can affect whether the conditional tail expectation behaves like O(t), O(t ) for some 0   < 1 as t!1. 5.7 Proofs Proof of Proposition 5.1: (a) Write E[X1jX2 > t;C ] = Z +1 0 P[X1 > xjX2 > t]dx = Z +1 0 P[X1 > x;X2 > t] P[X2 > t] dx = Z +1 0 1 F1(x) F2(t) + C (F1(x); F2(t)) F 2(t) dx = (F 2(t))  1  E[X1] + Z 1 0 f F2(t) + C  (F1(x); F2(t))gdx  : (5.10) SinceE[X1] <1, and P[X1 > x;X2 > t]  P[X1 > x], the integral R1 0 f F2(t)+ C (F1(x); F2(t))gdx is finite. The conclusion follows if for all v close to 1 from 136below, Z 1 0 f v + C (F1(x); v)gdx = Z 1 0 f[ v + C (u; v)]  [f1(F  1 1 (u))]  1gdu  Z 1 0 f[ v + C(u; v)]  [f1(F  1 1 (u))]  1gdu = Z 1 0 f v + C(F1(x); v)gdx: (5.11) The above follows if for any given 0 < u < 1, there exists a v0 = v0(u) such that 1  v > v0 implies that C (u; v)  C(u; v): (5.12) Taking a Taylor expansion for g(v) := C (u; v) C(u; v) about v0 with v < v0 < 1 leads to C (u; v) C(u; v)  (v  v0)  C 1j2(ujv 0)] C1j2(ujv 0)  = (v  v0)C1j2(ujv 0) ( C 1j2(ujv 0) C1j2(ujv0)  1 ) : Then (5.12) holds if limv0!1C 1j2(ujv 0)=C1j2(ujv 0) < 1. (If the limit of the ratio is 1, then possibly a non-standard second order expansion is needed.) (b) The copulas for F1; F2 are bC (u; v) = u + v  1 + C (1 u; 1 v) and bC(u; v) = u+ v 1 +C(1 u; 1 v), so that bC 1j2(ujv) = 1 C  1j2(1 uj1 v) and bC1j2(ujv) = 1 C1j2(1 uj1 v). Now part (a) applies to bC  1j2 and bC1j2.  Proof of Corollary 5.2: For any given 0 < u < 1, write C 1j2(ujv) C1j2(ujv) =  0(  1(u) +   1(v))= 0(  1(v)) ’0(’ 1(u) + ’ 1(v))=’0(’ 1(v)) ; so limv!1 C  1j2(ujv)=C1j2(ujv) < 1 if lim v!1 ’0(’ 1(v))  0(  1(v) < ’0(’ 1(u))  0(  1(u) ; or  (u) >  (1). The conclusion now follows from Proposition 5.1.  137Proof of Corollary 5.3: Let  =    and ’ = ’ be the LTs for C and C respectively. By considering the domination term as u! 1 in Table 5.1,  (1) = k limu!1 (1  u)     = 0 where k is a positive constant. Hence  (u) >  (1) for 0 < u < 1 and Corollary 5.2 applies.  Proof of Proposition 5.4: Rather than applying part (b) of Proposition 5.1, we provide a more direct proof. Because we are using survival copulas, rather than (5.12), we want to show that for any given 0 < u < 1, there is a v0 such that bC (u; v)  bC(u; v) or C (1 u; 1 v) = C (1 u; 1 v)  C’(1 u; 1 v) = C(1 u; 1 v) for 1  v > v0. The inequality is the same as  (  1(u1) +   1(v1))  ’(’  1(u1) + ’  1(v1)); (5.13) with u1 = 1  u and v1 = 1  v for v1 near 0. Let u1 = ’(su) and v1 = ’(sv). Since  is decreasing, inequality (5.13) is the same as   1(u1) +   1(v1)  (  1  ’)(’ 1(u1) + ’  1(v1)) or (  1  ’)(su) + (  1  ’)(sv)  (  1  ’)(su + sv): For s sufficiently large, if  (s) :=   1 ’(s) is strictly convex, then for anyw > 0,  (s+w)  (s) is strictly increasing in s with limit of1, since  (s) is also a strictly increasing function. Therefore, for sv large enough,  (su + sv)  (sv) >  (su). If  (u) is strictly decreasing in u near 0, that is,  0(s) = ’0(s)= 0( (s)) is strictly increasing for large s by letting s = ’ 1(u), then  (s) is strictly convex for large s, which completes the proof.  Proof of Corollary 5.7: Since 1   (s) = R s 0   0(t)dt and   0(t) is strictly increasing in t as t! 0+, by Lemma 5.6,   0(s)=(1  (s)) = o(s 1), s! 0+. Therefore, by L’Hoˆpital’s rule, lim s!0+ log(1  (s)) log(1 ’(s)) = lim s!0+ [log(1  (s))]0 [log(1 ’(s))]0 = lim s!0+  0(s)=[1  (s)] ’0(s)=[1 ’(s)] = 0; which implies ’(s) approaches 1 faster than  (s) as s ! 0+, and lims!0+ [1  138 (s)]=[1 ’(s)] =1. Thus, limu!1   1(u)=’ 1(u) = 0, and lim u!1  (u) = lim u!1 ’0(’ 1(u))  0(  1(u)) = lim u!1 [  1(u)]0 [’ 1(u)]0 = lim u!1   1(u) ’ 1(u) = 0: Then Corollary 5.2 implies the result.  Proof of Corollary 5.8: Since  (s) = R1 s   0(t)dt and   0(t) is strictly decreas- ing in t as t!1, by Lemma 5.5,   0(s)= (s) = o(s 1); s!1. Therefore, by L’Hoˆpital’s rule, lim s!1 log (s) log’(s) = lim s!1 [log (s)]0 [log’(s)]0 = lim s!1  0(s)= (s) ’0(s)=’(s) = 0; which implies that’(s) goes to 0 faster than (s) as s!1 and lims!1 ’(s)= (s) = 0. Thus, limu!0+ ’  1(u)=  1(u) = 0. Then write 1  (u) :=  0(  1(u)) ’0(’ 1(u)) =   0(  1(u))  1(u)  (  1(u))  ’(’ 1(u))  ’0(’ 1(u))’ 1(u)  ’ 1(u)   1(u) : So there exists a 0 < K <1, such that 0  lim u!0+ 1  (u)  lim s!1 K  s 0(s)  (s)  lim u!0+ ’ 1(u)   1(u) = 0: Then Proposition 5.4 implies the result.  Proof of Proposition 5.9: We prove only part (a), since part (b) follows from (a) as in Proposition 5.1. Since E[X1] is finite, E[X1jX2] is finite a.e. F2. Write E[X1jX2 = t;C ] = Z +1 0 P[X1 > xjX2 = t]dx = Z +1 0 f2(t) (@2F )(x; t) f2(t) dx = Z +1 0 f2(t) C 1j2(F1(x)jF2(t))f2(t) f2(t) dx = Z +1 0 f1 C 1j2(F1(x)jF2(t))gdx; 139The conclusion follows if with v = F2(t) < 1, Z 1 0 fC1j2(F1(x)jv) C  1j2(F1(x)jv)gdx = Z 1 0 C1j2(F1(x)jv) ( 1 C 1j2(F1(x)jv) C1j2(F1(x)jv) ) dx  0; (5.14) as t!1. A sufficient condition is lim v!1 C 1j2(ujv) C1j2(ujv) < 1; 0 < u < 1:  Proof of Corollary 5.10: From the assumption, lim v!1 C 1j2(ujv) C1j2(ujv) = lim v!1  0    1(u) +   1(v)  = 0    1(v)  ’0  ’ 1(u) + ’ 1(v)  =’0  ’ 1(v)  = limv!1  (v)  (u) < 1 so that Proposition 5.9 applies.  Proof of Corollary 5.11: From part (b) of Proposition 5.9, we want to show that for fixed 0 < u < 1, limv!0+ [1 C  1j2(ujv)]=[1 C1j2(ujv)] < 1, or lim v!0+ C 1j2(ujv)=C1j2(ujv) > 1; i.e., lim v!0+  0    1(u) +   1(v)   0(  1(v)) =: g(0) > h(0) := lim v!0+ ’0  ’ 1(u) + ’ 1(v)  ’0(’ 1(v)) : (5.15) The strict convexity condition on   1  ’ and the proof of Proposition 5.4 imply that with u fixed,  (  1(u) +   1(v)) =: G(v) > H(v) := ’(’ 1(u) + ’ 1(v)); (5.16) for v > 0 sufficiently small. Also, G(0) = H(0) = 0, so we must have G0(0) > 140H 0(0) for any 0 < u < 1, thus (5.15) holds. since G0 = g and H 0 = h.  Proof of Corollary 5.12: The proof is straightforward due to Corollary 5.10 and the proof of Corollary 5.7.  Proof of Corollary 5.13: The proof is straightforward due to Corollary 5.11 and the proof of Corollary 5.8.  Proof of Lemma 5.14: It can be written that C(uw1; : : : ; uwd) =  0 @  1(uw ) 0 @1 + X i2fj:wj 6=w g   1(uwi)   1(uw ) 1 A 1 A : Letting ti(u) :=   1(uwi)=  1(uw ), since  2 RV0,   1 2 RV1(0+), and thus wi > w implies that limu!0+ ti(u) = 0 for any i 2 fj : wj 6= w  g. Let t := (ti)i2fj:wj 6=w g, and define h(t  ) :=  (s(1 + P i2fj:wj 6=w g ti)). A Taylor expansion for h(t ) about 0 leads that h(t )   (s) + s 0(s) X i2fj:wj 6=w g ti: (5.17) Then choosing s =   1(uw ) in (5.17) proves the claim, and  (u;w) = o(u); u! 0+ is due to Lemma 5.5.  Proof of Proposition 5.15: It is well known that CTE is subadditive for continuous risks (Denuit et al., 2005); that is, for any X0 2 R(F1; : : : ; Fd) CTEp(X 0 1 +    +X 0 d)  dX i=1 CTEp(X 0 i); 0 < p < 1: From Proposition 4.15, for such an X, CTEp(X1 +    +Xd)  dX i=1 CTEp(Xi) = dX i=1 CTEp(X 0 i); p! 1  : 141Thus, CTEp(X1 +    +Xd) & CTEp(X 0 1 +    +X 0 d); p! 1  :  Proof of Proposition 5.17: By Theorem 4.3 of Embrechts et al. (2009a), VaRp is asymptotically subadditive for X0 as p ! 1 . Also, X satisfies Assumption 5.1, and thus VaRp is asymptotically additive for X. So the above asymptotic inequal- ity holds, and tail comonotonicity becomes an asymptotically worst dependence structure in this sense.  Proof of Proposition 5.18: By Theorem 6.8 of McNeil et al. (2005), VaRp is sub- additive for X0 when 0:5  p < 1. Also, notice that if the assumption that X is non-negative in Proposition 4.14 is relaxed, the asymptotic additivity of VaR still holds. Note that the X here is not elliptical; within elliptical distribution families, there are not non-trivial tail comonotonic distributions (see Chapter 4).  142Chapter 6 Second order regular variation and risk measures 6.1 Introduction In actuarial science, some statistical quantities about a random variableX are often referred to as risk measures. Among many of them, value at risk (VaR) and condi- tional tail expectation (CTE) are the most popular risk measures. For 0 < p < 1 and usually with p > 0:9, VaRp refers to the 100p percentile of loss and CTEp is a conditional expectation given the loss exceeds VaRp. Both of them have been adopted by regulations of insurers. For example, CTE has been required for cal- culating the relevant risks of segregated fund in Canada (OSFI, 2011, Chapter 8). VaR has been commonly-used for financial and insurance risks while recently CTE has been suggested to be a more conservative risk measure compared to VaR, es- pecially when loss distributions are heavy-tailed. We refer to Denuit et al. (2005) and McNeil et al. (2005) for relevant risk measures and their relationships. More precisely, the class of heavy-tailed distributions is a very large family and can be defined as K = fF df on (0;1) : R +1 0 e  xdF (x) = 1 for all  > 0g (See Fig- ure 1.4.1 of Embrechts et al. (1997) for the classes of heavy-tailed distributions). In this chapter, we only study an important subclass of the heavy-tailed distribu- tions, consisting of distributions that are supported on the positive real line and have regularly varying survival functions. For narrative convenience, we use the 143term “heavy-tailed distribution” for this subclass of heavy-tailed distributions. From the viewpoint of risk management, tail behavior of loss random variables is important. In the literature, much work has been done to better understand the extremal patterns of aggregate losses by doing asymptotic analysis, such as, Alink et al. (2004, 2005, 2007); Barbe et al. (2006); Embrechts et al. (2009b). In these papers, asymptotic behavior of aggregate dependent losses has been studied. We believe that the asymptotic study on the tail behavior is relevant to understand the magnitude of large losses and the performance of corresponding risk measures. The benefit of doing asymptotic analysis on risk measures, on the other hand, may establish asymptotic relationships between risk measures. For example, as a coher- ent risk measure, CTE has many advantages over VaR; we refer to Artzner et al. (1999) for limitations of VaR compared with a coherent risk measure. However, computing CTE is often more costly than estimating VaR since the former often involves Monte Carlo simulations for non-closed forms of integrations. To this end, studying asymptotic relationships between CTE and VaR becomes promising. Moreover, by studying the asymptotic relationship between CTE and VaR, we will be able to have better sense of how VaR may underestimate heavy-tailed risks, and we refer to Figure 6.1 for an example. Some asymptotic analysis of CTE in terms of VaR has been done most recently by Zhu and Li (2012). In their paper, the non-negative random vector under study has the form of X = (T1R; : : : ; TdR), where R is non-negative, independent of T = (T1; : : : ; Td)  0 and has a regularly varying survival function. Zhu and Li (2012) derived the closed-form first order approximation for CTE of the following form: E[X1jkXk > VaRp(kXk)], as p ! 1. Their result is interesting since the closed-form approximation of CTE only relies on some mild moment conditions on Ti’s. The results obtained in Zhu and Li (2012) is only a first order approximation of the relationship between CTE and VaR, as p ! 1. The first order property is useful since we have known more about VaR while the asymptotic relationship will provide some insights about CTE. However, the limitation is also obvious that the first order asymptotic approximation may not be good for a sub-extremal threshold. So, second order properties would be able to provide more insightful information. On one hand, the second order properties determine the rates of convergence for the first order approximation, and when the speed of convergence is higher, the 144asymptotic approximation by only the first order term has a better chance to provide a good approximation for the sub-extremal level; on the other hand, the second order term itself improves the approximation. In the asymptotic analysis of CTE, second order regular variation (2RV) pro- vides a tractable tool for studying second order properties. 2RV was originally studied in the extreme value theory, to study the speed of convergence of the ex- treme value condition (de Haan and Ferreira, 2006, Section 2). For a general theory of 2RV, we refer to de Haan and Ferreira (2006); de Haan and Stadtmu¨ller (1996) for references. In this chapter, we use 2RV to study the tail behavior of the distribu- tion function F (x), which is more naturally related to the study of risk measures. In Geluk et al. (1997), 2RV on survival functions was studied in the context of convolution and so on. We refer to Degen et al. (2010) for a recent study on risk concentration and diversification under the assumption of 2RV, which studied risk concentration and diversification benefit of multiple losses. In general, we are interested in the behavior of E[Xijg(X1; : : : ; Xd) > t]; t!1; (6.1) where g is a loss aggregate function, and (X1; : : : ; Xd) has a general dependence structure and the margins have some particular tail patterns. However, the scale mixture approach studied in Zhu and Li (2012) provides a wide subset of such ran- dom vector X. We will follow this approach in this chapter and study the second order property of (6.1) for the case where g is a homogeneous function of order 1 and the non-negative X is constructed by the scale mixture approach. More specif- ically, we will study the rates of convergence of asymptotic relationships between CTE and VaR under the framework of 2RV. Closed-form second order approxi- mations have been obtained for both the univariate case and the multivariate case where the random vector is constructed from a scale mixture with a heavy-tailed non-negative random variable. For both cases, we find that the first order approx- imation is affected by only the regular variation index   of marginal survival functions, while the second order approximation is influenced by both the param- eters for first and second order regular variation, and the convergence speed is dominated by the second order parameter only. Many well-known continuous dis- 145tributions for modeling univariate/multivariate losses or financial returns — such as the Student t , Burr, multivariate t , multivariate distributions constructed by reg- ularly varying margins and some upper tail dependent copulas, and some elliptical distributions with regularly varying margins — satisfy the second order conditions that we will study in this chapter. Some side results are second order properties for the well-known Karamata’s Theorem and Breiman’s Theorem. This chapter is organized as following: In Section 6.2, notation, preliminary concepts and results are presented. In Section 6.3, the univariate case with 2RV conditions is studied to understand the asymptotic relationship between CTE and VaR. The study on the multivariate case is reported in Section 6.4. Some discussion of future research is in Section 6.5. Finally, Section 6.6 presents all the proofs. 6.2 Preliminaries Fundamental knowledge about regular variation has been introduced in Section 2.3. For the multivariate case to be studied in Section 6.4, we will study positive random vector X constructed as this form (X1; : : : ; Xd) = (RT1; : : : ; RTd), where the tail behavior of product of two random variables plays an important role. The following result just tells us how the product will inherit the tail behavior of the random variable that has a heavier tail. Proposition 6.1 (Breiman’s Theorem) Suppose X is a non-negative random vari- able such that its survival function F (x) 2 RV  with  > 0, and Y  0 is a random variable, independent of X , with E[Y  + ] <1 for some  > 0. Then P[XY > x]  E[Y  ]P[X > x]; x!1: (6.2) That is, if X is regularly varying, then its right tail behavior will be inherited by the product XY , where Y has a lighter right tail and a finite moment of order higher than E[Y  ]. We refer to Breiman (1965) for a proof of the case where 0 <  < 1, and the proof is also adaptable for proving the case where 0 <  . For some multivariate versions of Breiman’s Theorem, we refer to Resnick (2007). The following definition of the second order regular variation comes from de Haan and Stadtmu¨ller (1996), de Haan and Ferreira (2006) and Geluk et al. 146(1997). See also Neves (2009) for a slightly different version of extended sec- ond order regular variation and Wang and Cheng (2005) for third and higher order regular variation. Definition 6.1 If the survival function of a non-negative random variable X is F := 1 F and F : [0;1)! (0; 1] satisfies that F 2 RV  with  > 0. Then F is said to be of second-order regular variation with parameter   0, if there exists a function A(t) that ultimately has a constant sign with limt!1A(t) = 0 and a constant c 6= 0 such that lim t!1 F (tx)=F (t) x  A(t) = H ; (x) = cx   Z x 1 u  1du; x > 0: (6.3) Then it is written as F 2 2RV  ; and A(t) is referred to as the auxiliary function of F . It is known from de Haan and Stadtmu¨ller (1996) or a more relevant form in Geluk et al. (1997) that if H ; (x) is not a multiple of x  then  < 0 implies that there exists a c 6= 0 such that H ; (x) = cx   x   1  ; jAj 2 RV and no other choices of  are consistent with A(t) ! 0. Moreover, convergence in (6.3) is uniform in x on compact intervals of (0;1). By adjusting A(t), we can always let c = 1, and unless otherwise specified, c is assumed to be 1. We only study the case where  < 0 and  > 1 in this chapter. Also, note that for second order regular variation,  6=  1 (Resnick, 2007, page 68). Therefore, the Pareto distribution with the cdf F (x) = 1 (k=x) ; x  k, where H ; (x) in (6.3) would be 0 for any function A(t), does not satisfy the conditions of second order regular variation and thus will not be studied in this chapter. To illustrate the main result for the multivariate case, we will use the concept of Archimedean copula as a mixture. 1476.3 Univariate cases For the univariate case, a direct application of Karamata’s Theorem (Theorem 2.2) shows that, for a non-negative random variable X 2 RV  with  > 1, CTEp(X) is finite and CTEp(X)     1 VaRp(X); p! 1: (6.4) From (6.4), we know that when p is sufficiently large, CTE and VaR of a regularly varying random variable have a deterministic relationship that is determined by the regular variation index   . However, we do not know how close is the right-hand side of (6.4) to its left-hand side. In practice, we always choose a level p that is strictly less than 1 to evaluate risks. To this end, a higher order approximation is useful. A closed-form second order approximation is ideal. If one can not obtain the closed-form second order approximation, knowledge about how fast the right- hand side converges to the left-hand size of (6.4) is also informative. In this section, we will derive the closed-form second order approximation for the univariate case assuming that the loss random variable is not only regularly varying with index   , but also second regularly varying. This assumption is mild, since some useful parametric distributions, such as Student t , satisfy it (see Example 6.3). Moreover, as a technical tool for our proof, Proposition 6.5 at the end of this section shows some interesting second order properties with respect to Karamata’s Theorem. Now, we are ready to give a technical lemma showing that the slowly varying function involved in the second order regular variation has a certain nice form. Lemma 6.2 Suppose F 2 2RV  ; ;  > 0;  < 0, then we can write F (t) = kt a‘(t); t > 0 with some k > 0 and limt!1 ‘(t) = 1, and moreover we have j1 ‘(t)j 2 RV . In what follows, we give a result of a uniform inequality for the second order regular varying survival distribution function. This result is essentially Theorem 2.3.9 of de Haan and Ferreira (2006) and will be useful to prove our subsequent propositions. We refer to de Haan and Ferreira (2006) and Cheng and Jiang (2001) for relevant discussion. 148Proposition 6.3 Suppose F 2 2RV  ; with  > 0 and  < 0, then for any  ;  > 0, there exists t0 = t0( ;  ) such that for all t; tx  t0 and x > 0,     F (tx)=F (t) x  a(t)  x  x  1        max  x  + + ; x  +    ; (6.5) where a(t) =   [1  ‘(t)]=‘(t) with F (t) = kt  ‘(t), 0 < k < +1 and limt!1 ‘(t) = 1. Remark 6.1 From Proposition 6.3, we may choose the A(t) in Definition 6.1 as A(t) := a(t) =   [1 ‘(t)]=‘(t), and clearly jA(t)j 2 RV . The following result tells us that the rates of approximation of CTEp(X) to    1VaRp(X) are determined by the second order parameter  and are of the order of [VaRp(X)] +1. When  is smaller (more negative), the speed of convergence is faster. In other words, when  is more negative, the first order term can provide a better approximation. Proposition 6.4 Suppose that a non-negative random variableX 2 2RV  ; with  > 1 and  < 0, and write its survival function F (t) := kt  ‘(t). Then CTEp(X)     1 VaRp(X) +  (VaRp(X)); p! 1; (6.6) where  (t) = ta(t) (  1  )(  1) ; (6.7) and a(t) can be chosen as a(t) :=   [1 ‘(t)]=‘(t). Remark 6.2 Since ja(t)j 2 RV and thus jta(t)j 2 RV +1, Equation (6.7) just shows that the second order term becomes more important as  >  1 (because jta(t)j is increasing as t ! 1). So we comment that the second order approxi- mation is especially useful for the case where  1 <  < 0, in which the second order term may not be ignored. In Example 6.5, we will illustrate by an exam- ple for a multivariate case that the second order term significantly improves the approximation. 149Remark 6.3 Proposition 6.4 just indicates that the first order term depends on  only, and the second order term depends on both  and  . The proof in Proposi- tion 6.4 also shows that for  > 1 and  < 0, as t ! 1, F (tx)=F (t) x   A(t) con- verges to x  x   1  uniformly in x on any set of the form [x0;1), where x0 > 0. Then we can exchange the orders of limit and integral operations in order to get limt!1 R1 a P[X>ts]=P[X>t] s  A(t) ds for some a > 0. Remark 6.4 A necessary condition for the asymptotic relationship between CTE and VaR is that, the random variable can not be too heavy-tailed; that is, the regular variation index  should be greater than 1. When   1, the tail behavior becomes much more complicated. In particular, as  < 1, CTEp(X) = 1;8p < 1, and under the current framework, one cannot study the asymptotic relationship between CTE and VaR. Example 6.1 (Hall/Weiss class) Suppose X is a random variable with support [0;1) and distribution function F such that with x  1;  > 1;  < 0, F (x) = 1 2x   (1 + x ). Then clearly, F 2 2RV  ; , and when t := VaRp(X) is big enough, CTEp(X) = t+ R1 t 1 2x   (1 + x )dx 1 2 t   (1 + t ) = t+ t=(  1) + t +1=(  1  ) 1 + t ; which implies, with (1 + t ) 1  1 t ; t! +1, CTEp(X)    1 VaRp(X)   (  1  ) (  1) [VaRp(X)]  +1; p! 1: It means that the rate of convergence is determined by the second order parameter  and is of the order of [VaRp(X)] +1. Figure 6.1 illustrates the effects of  . Example 6.2 (Burr distribution) Consider the Burr distribution with the survival function F (x) = (1 + xb) a; x; a; b > 0, and the second order expansion leads to F (x) = x ab[1 ax b + o(x b)]; x!1; so that F 2 2RV ab; b. 150Figure 6.1: Sub-extremal relationships between CTE and VaR ( = 2) for Hall/Weiss class Effect of the second order parameter ρ Probability level CTE / Va R 1.90 1.92 1.94 1.96 1.98 2.00 0.95 0.96 0.97 0.98 0.99 1.00 ρ=−2 ρ=−1 ρ=−0.5 Now we evaluate how large p must be for the second order approximation of Proposition 6.4 to be decent for the Burr distribution. Assume X  Burr(a; b;  ), where a; b are the two shape parameters and  is the scale parameter; that is, X  F (x) := 1   1 + (x= )b   a . Write F (x) = (1 + (x= )b) a =  abx ab‘(x), where ‘(t) = ((t= ) b + 1) a is a slowly varying function. According to Propo- sition 6.4, an auxiliary function for the Burr distribution can be chosen as A(t) := b[1 ‘(t)]=‘(t) = b h 1 + (t= ) b  a  1 i : 151Clearly, A 2 RV b. Then, letting t := VaRp(X), CTEp(X) = t+ (1 p)  1 Z 1 t  1 + (x= )b   a dx = t+  b(1 p) Z (1+(t= )b) 1 0 (1 y)1=b 1ya 1=b 1dy = t+  b(1 p) Beta  (1 + (t= )b) 1; a 1=b; 1=b  ; (6.8) where Beta( ; a  1=b; 1=b) is an incomplete Beta function with parameters a  1=b; 1=b. We can use (6.8) to get the exact calculation, and then compare it to the second order approximation. Table 6.1 presents the values of p for which a decent second order approxima- tion can be obtained. For a given leading parameter (ab) for the tail heaviness, a larger b leads to a smaller p; that is, the second order parameter ( =  b) is more negative and thus the rates of convergence becomes faster. More interestingly, for a given b, the second order approximation for a lighter tail (larger  ) is not as good as that for a heavier tail. When the tail becomes very light (say,   4), it was hard to get a good second order approximation within the 5% error band. The smallest p’s required that are reported in Table 6.1 do not rely on the scale parameter. However, theoretically, there are infinitely many choices for the auxiliary function A(t). The smallest p required to get a decent second order approximation heavily rely on the choice of A(t). Table 6.1: How large p must be to get a good second order approximation: the values are the corresponding p for which the absolute difference between the second order approximation and the true value is 5% of the true value.  (= ab) b 1.1 1.5 2 3 0.5 0.86977 0.98828 0.99859 0.99994 1 0.32947 0.73711 0.88890 0.97825 1.5 0.11240 0.44426 0.65444 0.86267 The next result extends Karamata’s theorem to a second order regular condi- tion for the case where regular variation index   <  1. Since some continu- 152ous random variables, such as the Student t random variable, have closed-form density functions but do not have closed-form cdf’s, the following result is very useful to derive second order properties, such as the second order parameter and the corresponding auxiliary function, for their survival functions from their density functions. Proposition 6.5 Let g 2 2RV  ; ;  > 1;  < 0, with an auxiliary function A(x), and define g (t) := R1 t g(x)dx. Then g  2 2RV  +1; , the corresponding auxil- iary function is A (t) =   1  1  A(t), and g (t)  t   1 g(t) + tA(t)   1     1  1   1  g(t); t!1: (6.9) Example 6.3 (Student t distribution) Consider the standard Student t distribu- tion with density function f(x) =  (( + 1)=2) p    ( =2)  1 + x2    ( +1)=2 ;  > 1: Then it can be verified that f(x)   (( + 1)=2) p    ( =2)  ( +1)=2x   1  1 2 1 ( + 1)x 2  ; x!1: That is, f(x) 2 2RV   1; 2. By Proposition 6.5, its survival function F 2 2RV  ; 2. For the Student t distribution, the second order parameter is always that  =  2. The second order property can also be obtained from scale mixture representation of the t distribution. In Example 6.4 in the next section, we will derive the same thing from a mixture with a generalized inverse Gamma distribu- tion. 6.4 Multivariate cases From the actuarial viewpoint, we are interested in CTEs of the formE[X1jg(X1; :::; Xd) > t], and in particular E[X1jX2 > t] for the bivariate case, where t is large, and X1; :::; Xd possess some general dependence structure and are heavy-tailed loss 153random variables (with the same RV index). In Chapter 3, the concept of tail or- der has been proposed to study the degree of positive tail association that covers asymptotic dependence and asymptotic independence. In Chapter 3, the upper tail order of a copula C is denoted as  U (C). And  U (C) = 1 corresponds to the usual upper tail dependence case and  U (C) > 1 covers asymptotic independence and even negative dependence cases. For E(X1jX2 > t), some examples and rough analysis suggests that under some regularity conditions on the copula C of (X1; X2),  U (C) = 1) E[X1jX2 > t] = O(t); t!1;  U (C) > 1) E[X1jX2 > t] = o(t); t!1: However, it is difficult to find sufficient conditions to deduce general results for the order of t and the leading coefficient. In Chapter 7, we have more discussions on the tail behavior of the above conditional specifications. The study of second order terms would depend on developing a theory for the second order tail functions of copulas (Joe and Li, 2011). A special example with multivariate regular variation and upper tail depen- dence is the case of heavy-tailed scale mixtures studied by Zhu and Li (2012). They obtained E[X1jX2 > t]  at; t ! 1, with a being a constant relying on some finite moments. In this section, one of our main results is to assume 2RV and get an extension to Zhu and Li’s result to a second order approximation. We also show that heavy-tailed scale mixtures with 2RV conditions covers some parametric distribution families that are commonly used in actuarial science; that is, it is not too restrictive for applications. Now, consider bivariate cases first. Let (X1; X2) = (RT1; RT2); where (T1; T2)  0, independent of R  0. Note here we do not specify the dependence structure between T1 and T2. This class of distributions covers mul- tivariate Pareto distributions and truncated multivariate elliptical distributions, etc. With some regularity conditions on R and moment conditions on T1 and T2, the 154following asymptotic relationship has been derived in Zhu and Li (2012), E[X1jX2 > VaRp(X2)]     1 E[T1T  12 ] E[T 2 ] VaRp(X2); p! 1: (6.10) In this section, we obtain the rate of the convergence of (6.10) and the second order approximation. Proposition 6.6 Let (X1; X2) = (RT1; RT2), where (T1; T2)  0, independent of R  0 such that the survival function of R is FR(t) 2 2RV  ; with  > 1 and  < 0. If E[T1] <1, E[T1T   1  + 2 ] <1 for some  > 0 and E[T    + 2 ] <1 for some  > 0, then an extension of (6.10) can be represented by E[X1jX2 > VaRp(X2)]   1(VaRp(X2)) +  2(VaRp(X2)); p! 1; where  1(t) =    1 E[T1T  12 ] E[T 2 ] t;  2(t) = A(t)t  (      1  E[T1T   1  2 ] E[T 2 ]     1 E[T1T  12 ]E[T    2 ] (E[T 2 ])2 ) : Here, A(t) is the auxiliary function for FR(t). Remark 6.5 Similarly to the univariate case, the first order term only depends on  , and the second order term depends on  and  . In order to get a higher order approximation, existence of higher moments of the form E[T1T   1  + 2 ] for some  > 0 and E[T   + 2 ] < 1 for some  > 0 are necessary. The speed of conver- gence is dominated by the second order parameter  only. Since jA(t)j 2 RV , the convergence speed of E[X1jX2 > VaRp(X2)] !    1 E[T1T   1 2 ] E[T 2 ] VaRp(X2) is of the order of [VaRp(X2)] +1; that is, when  is more negative, the convergence is faster at the sub-extremal level. It is also consistent with the univariate case. In Proposition 6.6, we do not specify the dependence structure between T1 and T2. If we have a random vector T  0 and the conditioning event can be of the form h(X) > t where h : Rd+ ! R+ is homogeneous of order 1, then h(X) = h(T)R and we can take T2 := h(T). 155Proposition 6.7 Suppose there is a function h : Rd+ ! R+, homogeneous of order 1, and a random vector X := (RT1; : : : ; RTd) where T = (T1; : : : ; Td)  0 independent of R  0 and FR 2 2RV  ; with  > 1 and  < 0. If E[T1] < 1, E[T1h  1  + (T)] <1 for some  > 0 andE[h   + (T)] <1 for some  > 0, then E[X1jh(X) > VaRp(h(X))]   1(VaRp(h(X))) +  2(VaRp(h(X))); p! 1; where  1(t) =    1 E[T1h  1(T)] E[h (T)] t;  2(t) = A(t)t        1  E[T1h  1  (T)] E[h (T)]     1 E[T1h  1(T)]E[h   (T)] (E[h (T)])2  : Remark 6.6 Examples of the homogeneous h function are norms of vectors, such as the l1-norm and l1-norm. These two norms correspond to the CTEs of a marginal riskX1 respectively conditioning on the events that the sum of all marginal risks and the maximum of all marginal risks are higher than a threshold. Example 6.4 A d-dimensional elliptical distribution can be constructed by X = (X1; : : : ; Xd) = RD(U1; : : : ; Um), where (U1; : : : ; Um) is uniformly distributed on the surface of the unit hypersphere in Rm, D is a d  m matrix with DDT positive semi-definite, andR is a positive random variable with FR 2 2RV  ; and the auxiliary function is A(x). We may take T = (T1; : : : ; Td) = D(U1; : : : ; Um), and T+i = 0 _ Ti for i = 1; : : : ; d, then the form X + = RT+ fits into the study of this chapter. X+ follows a left-truncated 1 elliptical distribution. Taking the homogeneous function h in Proposition 6.7 to be the l1 norm, and letting t := VaRp  Pd i=1X + i  , then we have for any k 2 f1; : : : ; dg, E " X+k      dX i=1 X+i > t #   1(t) +  2(t); p! 1; 1Unless otherwise stated, left-truncated in this chapter refers to left-truncating below zeros for each element of a random vector. 156where  1(t) =    1 E[Tk( Pd i=1 Ti)   1] E[( Pd i=1 Ti)  ] t;  2(t) = A(t)t  (        1 E[Tk( Pd i=1 Ti)   1  ] E[( Pd i=1 Ti)  ]     1 E[Tk( Pd i=1 Ti)   1]E[( Pd i=1 Ti)    ] (E[( Pd i=1 Ti)  ])2 ) : In particular, we may choose a bivariate standard normal random vector (T1; T2) with identical margins, correlation 0 < % < 1, and let T+i = 0 _ Ti for i = 1; 2, and R = Y  1=2 with Y following Gamma( =2;  =2) in which  =2 are both shape and rate parameters. Then the scale mixture (X1; X2) = (RT1; RT2) is a bivariate t distribution, and (X + 1 ; X + 2 ) = (RT + 1 ; RT + 2 ) is a left-truncated bivariate t distribution. Note that Y  1 follows an Inverse Gamma distribution with both the shape and scale parameters being  =2. Thus the density function for Y  1 is fY  1(x) = ( =2) =2  ( =2) x  =2 1e x  1 =2; x > 0;  > 0: Therefore, the density function of R can be derived as fR(x) = 2fY  1(x 2)x = 2 ( =2) =2  ( =2) x   1e x  2 =2  2 ( =2) =2  ( =2) x   1(1 x 2 =2); x!1: Then it can be verified that fR(x) 2 2RV   1; 2 and the corresponding auxiliary function AfR(x) =  x  2. Hence FR(x) 2 2RV  ; 2 by Proposition 6.5 and the corresponding auxiliary function AFR(x) = ( +1) 1 ( +1) 1 ( 2)AfR(x) =  2  +2x  2. Moreover, from (6.25), we know that the product Xi = RTi will inherit the right tail behavior of R; that is, the univariate Student t random variable Xi 2 2RV  ; 2, which is also consistent to Example 6.3, where the same conclusion is drawn directly from the density function of the t distribution. Then more specifi- cally, letting  = 2 and t := VaRp(X + 1 +X + 2 ), then for the bivariate left-truncated 157t distribution, E  X+1   X+1 +X + 2 > t    1(t) +  2(t); p! 1; where  1(t) = 2 E[T+21 ] + E[T + 1 T + 2 ] E[(T+1 + T + 2 ) 2] t;  2(t) =   2 3 E[T+1 (T + 1 + T + 2 ) 3] E[(T+1 + T + 2 ) 2] + E[T+21 + T + 1 T + 2 ]E[(T + 1 + T + 2 ) 4] (E[(T+1 + T + 2 ) 2])2  AFR(t)t: The higher moments of truncated standard bivariate t random variables can be calculated in what follows by some recurrence relations in Shah and Parikh (1964) or page 313–314 of Kotz et al. (2000). That is,  1(t) = t;  2(t) = g(%)AFR(t)t = g(%)t  1: (6.11) With vr;s := vr;s(%) = E[(T+1 ) r(T+2 ) s] for non-negative integers r; s, g(%) = v4;0 + 4v3;1 + 3v2;2 6(v2;0 + v1;1) = (1 + %)f3[12 + arcsin(%)] + (4 %) p 1 %2 g 3f12 + arcsin(%) + p 1 %2 g : g(%) is strictly increasing in % 2 [ 1; 1]. From the equations in (6.11), we know that for this truncated bivariate standard t distribution, the convergence speed to the first order approximation is dominated by [VaRp(X + 1 + X + 2 )]  1. When the correlation coefficient % is larger — that is, when the dependence between margins is stronger — the second order approximation is more important. Some relevant observations for second order approximation of vector-valued CTE have also been reported in Example 3.2 of Joe and Li (2011). The effect from the tail heaviness of margins may also have subtle influence on the second order approximation, how- ever we have to calculate higher moments in this case. A general conclusion about the effect of marginal heaviness on the second order approximation seems to be interesting and non-trivial to obtain. However, following the discussion in Remark 6.2, for the above bivariate t 158distribution, the second order parameter  =  2. So the second order term will be negligible when [VaRp(X + 1 +X + 2 )] is large enough. In the following Example 6.5, we will illustrate by an example for a case of  1 <  < 0 that the second order term significantly improves the approximation and can not be ignored. Example 6.5 (Burr scale mixture of normal) Let (X1; X2) := (Z+1 R;Z + 2 R), where (Z+1 ; Z + 2 ) is left-truncated standard bivariate normal with correlation % = 0:9 and R  Burr(a = 5; b = 0:4) (i.e., FR 2 2RV 2; 0:4, refer to Example 6.2) with a scale parameter 1000. In Figure 6.2, the vertical axis is the ratio of CTE to VaR CTE VaR := E[X1jX1 +X2 > VaRp(X1 +X2)] VaRp(X1 +X2) ; defined as a function of p. The true value is approximated based on a Monte Carlo simulation with a sample size of 107, and the first and second order approximations are calculated based on Proposition 6.7. Figure 6.2: First/Second order approximations for Burr scale mixture of nor- mal Probability level (p) CTE / Va R 1.0 1.5 2.0 2.5 0.95 0.96 0.97 0.98 0.99 1.00 1st order approximation true value 2rd order approximation Example 6.6 Suppose T1; T2 are independent and exponentially distributed with survival function FT1(x) = FT2(x) = 1  e  x, Q = 1=R and independent of 159T1; T2, FR(x) 2 2RV  ; and (X1; X2) := (RT1; RT2). Then P[X1 > x1; X2 > x2] = Z 1 0 e (x1+x2)qFQ(dq) =  Q(x1 + x2); (6.12) where FQ and  Q are the distribution function and LT of Q, respectively. Take x1 =   1 Q (u1) and x2 =   1 Q (u2), and then P[X1 >   1Q (u1); X2 >   1 Q (u2)] =  Q(  1 Q (u1) +   1 Q (u2)) =: C Q(u1; u2) (6.13) Also, we have P[Xi > x] =  Q(x) for i = 1; 2. Then E[X1jX2 > t] = Z 1 0  Q(x+ t)  Q(t) dx: (6.14) For a random variable R following the inverse Gamma distribution with a shape parameter  and a scale parameter  , the density function fR(x) 2 2RV   1; 1 with an auxiliary function AfR(x) =  x  1. We can derive from the density function and Proposition 6.5 that FR(x) 2 2RV  ; 1 and the auxiliary function AFR(x) =    +1x  1. If we letQ  Gamma( ; 1); that is,  is the shape parameter and  = 1 is the rate parameter, since for the standard exponential random variable the a-th moment is  (a+ 1), then we can get by Proposition 6.6 that E[X1jX2 > t]  1   1 t+ 1   1 ; t!1: In another way, take  Q(x) = (1+x)  , the LT of Gamma( ; 1) and from (6.14) it can be directly verified that E[X1jX2 > t] = 1=(  1) + t=(  1), which is consistent with Proposition 6.6. This example just shows that Proposition 6.6 can also be used for a random vector where margins possess the tail behavior of Q(x) (i.e., P[Xi > x] =  Q(x)) and the dependence structure is the Archimedean copula constructed by  Q(x). This example is interesting since the tail behavior of the LT  Q(x) affects both the tail dependence structure and the tails of margins. FR(x) 2 RV  implies that FQ(x) 2 RV (0). Then by the Karamata’s Tauberian Theorem (e.g., Theorem 1601.7.1’ in Bingham et al. (1987)),  Q(x) 2 RV  . That is, the margins are all regularly varying with index   . Moreover, there is upper tail dependence for (X1; X2), since  Q(t) 2 RV  , the corresponding copula C Q has lower tail dependence (refer to subsection 3.1 of Charpentier and Segers (2009)) and the copula for (X1; X2) is the survival copula of C Q . Based on (6.14), it seems that the second order property of  Q(x) could also be related to the second order parameter of FR. We conjecture that under some conditions, if FR(t) 2 2RV  ; and Q := 1=R, then  Q(t) 2 2RV  ; . 6.5 Concluding remarks Second order regular variation provides a nice theoretical platform for studying second order approximations of limiting properties, like the asymptotic relation- ship between CTE and VaR that we have studied in this chapter. More importantly, many parametric distributions satisfy those theoretical assumptions so that the im- plementation of the main results is feasible. From the viewpoint of risk manage- ment, the study on risks at the sub-extremal level is more realistic and important, and we believe that the study involving the second order condition on asymptotic analysis for risk measures should be promising for this purpose. For future research, it will be interesting to study how marginal tail heaviness and tail dependence structures affect the second order approximations of risk mea- sures. Also, the second order regular variation we have studied on risk measures — no matter for the single risk or for the multiple risks — is univariate second order regular variation. To get a more general result for multiple risks, we need to study the performance of risk measures under multivariate second order regular variation. Moreover, whether the second order parameter  is greater than  1 is critical to decide on the necessity of a second order approximation. So it will be interesting to develop a methodology to test H0 :    1 verses H1 :  >  1. 6.6 Proofs Proof of Lemma 6.2: Since F 2 RV  , we can write F (t) = t  ‘(t) where ‘(t) is slowly varying and non-negative. By Definition 6.1, there is an auxiliary function 161A and a constant c 6= 0 such that lim t!1 x  ‘(tx)=‘(t) x  A(t) = cx  x  1  : Therefore, lim t!1 ‘(tx)=‘(t) 1 A(t) = lim t!1 ‘(tx) ‘(t) A(t)‘(t) = c x  1  : Since the sign of A(t) can be adjusted by changing the sign of c due to Definition 6.1, if c and A(t) have the same sign, then we assume that c > 0 and A(t) > 0, and thus A(t)‘(t) > 0. By Theorem B.2.2 of de Haan and Ferreira (2006), then limt!1 ‘(t) = k with 0 < k < +1 and k  ‘(t) 2 RV . So we can rewrite F (t) = kt  ‘(t) with limt!1 ‘(t) = 1 and also 1  ‘(t) 2 RV . If c and A(t) have different signs, then let A(t) > 0,  c > 0 and then consider the function  ‘(t) instead of ‘(t). Also by Theorem B.2.2 of de Haan and Ferreira (2006), limt!1 ‘(t) =  k with 0 < k < 1. So F (t) = kt  ‘(t) with limt!1 ‘(t) = 1 and also ‘(t) 1 2 RV .  Proof of Proposition 6.3: From Lemma 6.2, we can write F (t) = kt  ‘(t) with 0 < k < +1 and limt!1 ‘(t) = 1. Then by Definition 6.1, with an auxiliary function A(t) and a constant c 6= 0, lim t!1 F (tx)=F (t) x  cA(t) = lim t!1 x  ‘(tx) x  ‘(t) cA(t)‘(t) = x  x  1  : That is, lim t!1 ‘(tx) ‘(t) cA(t)‘(t) = x  1  : If c and A(t) have the same sign, then cA(t)‘(t) > 0 and by Theorem B.2.18 of de Haan and Ferreira (2006), we have for any  ;  > 0, there exists a t0 = t0( ;  ) such that for all t ^ tx  t0,     ‘(tx) ‘(t)   [1 ‘(t)]  x  1        x  max(x ; x  ): (6.15) 162A similar argument can be used for the case where c and A(t) have different signs, and inequality (6.15) still holds. Multiplying both sides of (6.15) by x  finishes the proof.  Proof of Proposition 6.4: Denote that t := VaRp(X), and the asymptotic relation- ship of (6.6) without the term  (t) can be easily derived by applying the Karamata’s theorem. Write F (t) := kt  ‘(t) and choose a(t) :=   [1 ‘(t)]=‘(t). Then CTEp(X) = t+ R1 t P[X > x]dx P[X > t] = t  1 + R1 t P[X > x]dx tP[X > t]  = t  1 + Z 1 t P[X > x] tP[X > t] dx  = t  1 + Z 1 1 P[X > ts] P[X > t] ds  = t 0 @1 + a(t) Z 1 1 P[X>ts] P[X>t]  s   a(t) ds+ 1   1 1 A =    1 t+ ta(t) (  1  )(  1) + ta(t) Z 1 1 P[X>ts] P[X>t]  s   a(t)  s  s  1  ds; (6.16) where the last equality is due to R +1 1 s   s  1  ds = 1 (  1  )(  1) . By Proposition 6.3, when s  1, for any small  ;  such that  > 0 and     1 >  > 0     F (ts)=F (t) s  a(t)  s  s  1        max  s  + + ; s  +    =  s  + + : Since  is arbitrarily small and s  + + is integrable on [1;1), by the dominated convergence theorem and Definition 6.1, the integration term in (6.16) is 0 as t! 1, which proves the claim.  Proof of Proposition 6.5: Since g 2 2RV  ; , letting x = st, lim t!1 R1 t g(x) tg(t)dx 1   1 A(t) = lim t!1 Z 1 t g(x) tg(t)  x  t  +1 A(t) dx = lim t!1 Z 1 1 g(st) g(t)  s   A(t) ds = Z 1 1 lim t!1 g(st) g(t)  s   A(t) ds = 1   1     1  1   1  : (6.17) 163The third equality in (6.17) is due to Remark 6.3. Therefore, Z 1 t g(x) tg(t) dx  1   1 + 1   1     1  1   1  A(t); t!1; which proves (6.9). Then by (6.9) it is easy to verify that g 2 2RV  +1; . Let r := (    1) 1  (  1) 1, then as t!1, g (ty) g (t)  y  +1  (  1) 1yg(ty) +   1rA(ty)yg(ty) (  1) 1g(t) +   1rA(t)g(t)  y  +1   1   1 y  +1(y  1)  A(t) + r  A(ty)  y a+1(y  1)  A(t) + y  +1   r  A(t)y  +1  (  1)   1   1 y  +1(y  1)  + r  A(ty) A(t) y a+1  r  y  +1  A(t)(  1)   1   1 + r  y  +1(y  1)  A(t)(  1) =   1     1 A(t) y  +1(y  1)  : Thus, A (t) =   1    1A(t).  Proof of Proposition 6.6: Denoting t = VaRp(X2) and following the proof of Proposition 2.3 of Zhu and Li (2012), we have E[X1jX2 > t] = t P[X2 > t] Z R2+ ( t1 t2 P  R > t t2  + Z 1 t1 t2 P  R > tw t1  dw ) FT1;T2(dt1; dt2): (6.18) Then it suffices to study the second order approximation of the following two asymptotic relationships. D1(t) := Z R2+ t1 t2 P h R > tt2 i P[X2 > t] FT1;T2(dt1; dt2)! E[T1T  12 ] E[T 2 ] ; t!1; (6.19) 164and D2(t) := Z R2+ R1 t1 t2 P h R > twt1 i dw P[X2 > t] FT1;T2(dt1; td2)! 1   1 E[T1T  12 ] E[T 2 ] ; t!1; (6.20) where both t1; t2 are positive. The proof will include three parts: 1. the second order property of the Breiman’s convergence: P[X2 > t]  E[T 2 ]P[R > t]; t ! 1; 2. the second order property for (6.19); 3. the second order property for (6.20). Part 1: The original proof of Breiman’s theorem is referred to Breiman (1965). Here we need to derive its speed of convergence. Since T2 and R are independent, P[X2 > t] P[R > t] = P[T2R > t] P[R > t] = Z 1 0 FR (t=x) FR(t) FT2(dx) = A(t) Z 1 0 FR(t=x) FR(t)  x A(t) FT2(dx) + E[T  2 ]: Now we claim that if E[T   + 2 ] <1 for some  > 0, then lim t!1 Z 1 0 FR(t=x) FR(t)  x A(t) FT2(dx) = Z 1 0 x x   1  FT2(dx): (6.21) Let h(t; x) := FR(t=x) FR(t)  x A(t) ; g(x) := x x   1  : By Proposition 6.3, we can write FR(t) := kt  ‘(t) with 0 < k < 1 and limt!1 ‘(t) = 1. Also, let A(t) =   [1  ‘(t)]=‘(t) as the one in Proposition 6.3 and write jA(t)j := t ‘A(t), where ‘A(t) is a slowly varying function, since jA(t)j 2 RV . Furthermore, for  > 0 such that E[T   + ] < 1 and any  > 0, 165there exists t0 such that for all t; t=x  t0 and x > 0, jh(t; x) g(x)j   max(x   + ; x     ): Hence, assuming  <    , without loss of generality, Z t=t0 0 jh(t; x) g(x)jFT2(dx)   Z t=t0 0 max(x   + ; x     )FT2(dx)   K1; (6.22) where 0 < K1 < 1 and does not depend on t. When x 2 (t=t0;1), we have xt0 > t and jA(t)j is ultimately decreasing implies that when t is sufficiently large jA(t)j > jA(xt0)j = x t  0j‘A(xt0)j; FR(t) = kt   ‘(t) and FR(t=x)  1 imply that ‘(t=x)  k 1(t=x) < k 1t 0 . Together with limt!1 ‘(t) = 1, we have when t is sufficiently large and x 2 (t=t0;1), jh(t; x)j < x j2k 1t 0  1j x t 0j‘A(xt0)j < x   + K2; (0 < K2 <1) and thus by the dominated convergence theorem, when t is sufficiently large Z 1 t=t0 jh(t; x)jFT2(dx) <  : (6.23) Also, apparently, for t sufficiently large, Z 1 t=t0 jg(x)jFT2(dx) <  : (6.24) Combining (6.22), (6.23) and (6.24) implies that, for any  > 0, when t is suffi- ciently large,     Z 1 0 h(t; x)FT2(dx) Z 1 0 g(x)FT2(dx)      Z 1 0 jh(t; x) g(x)jFT2(dx)  Z t=t0 0 jh(t; x) g(x)jFT2(dx) + Z 1 t=t0 jh(t; x)jFT2(dx) + Z 1 t=t0 jg(x)jFT2(dx) < (K1 + 2) ; 166which proves the claim. Hence, as t! +1, P[X2 > t]  E[T 2 ]P[R > t] + A(t)   E[T   2 ] E[T  2 ]  P[R > t]: (6.25) Part 2: For (6.19), take FR (t) := tFR(t), then FR (t) 2 2RV  +1; and the corresponding auxiliary function is A (t) = A(t). Let M2(B) := Z B Z R+ t1FT1;T2(dt1; dt2); B  R+ denote the marginal mean measure induced by T1. Since M2(R+) = E[T1] < 1, M2( ) is a finite measure. Since E[T1T   1  + 2 ] <1, then by (6.21) in the proof of Part 1, applied to FR , we have lim t!1 Z R2+ t1 t2 FR(t=t2) FR(t) FT1;T2(dt1; dt2) = limt!1 Z R+ FR (t=t2) FR (t) M2(dt2) = E[T1T  12 ] + A(t)   E[T1T   1  2 ] E[T1T   1 2 ]  : (6.26) Also, lim t!1 D1(t) = lim t!1 Z R2+ t1 t2 P h R > tt2 i P[X2 > t] FT1;T2(dt1; dt2) = lim t!1 P [R > t] P[X2 > t]  lim t!1 Z R2+ t1 t2 FR(t=t2) FR(t) FT1;T2(dt1; dt2) (6.27) Part 3: For (6.20), let F  R(t) := R1 t FR(x)dx, since FR(x) 2 2RV  ; , then by Proposition 6.5, F  R(t) 2 2RV  +1; and the corresponding auxiliary function is 167A (t) =   1  1  A(t). Also, letting x = tw=t1, D2(t) = P[R > t] P[X2 > t] Z R2+ R1 t1 t2 FR(tw=t1)dw FR(t) FT1;T2(dt1; dt2) = P[R > t] P[X2 > t] Z R2+ t1 (Z 1 t t2 FR(x) tFR(t) dx ) FT1;T2(dt1; dt2) = P[R > t] P[X2 > t] Z R2+ t1 F  R(t=t2) F  R(t) R1 t FR(x)dx tFR(t) FT1;T2(dt1; dt2): Therefore, lim t!1 D2(t) = lim t!1 P[R > t] P[X2 > t]  lim t!1 R1 t FR(x)dx tFR(t)  lim t!1 Z R2+ t1 F  R(t=t2) F  R(t) FT1;T2(dt1; dt2): (6.28) From (6.21), applied to F  R, because E[T1T   1  + 2 ] <1, as t!1, Z R2+ t1 F  R(t=t2) F  R(t) FT1;T2(dt1; dt2) = Z 1 0 F  R(t=t2) F  R(t) M2(dt2)  E[T1T  12 ] + A (t)   E[T1T   1  2 ] E[T1T   1 2 ]  = E[T1T  12 ] + A(t)    1   1   E[T1T   1  2 ] E[T1T   1 2 ]  (6.29) Hence, combining (6.25), (6.26), (6.27), (6.9), (6.28) and (6.29) together, we will get E[X1jX2 > t]     1 E[T1T  12 ] E[T 2 ] t + A(t)t  (      1  E[T1T   1  2 ] E[T 2 ]     1 E[T1T  12 ]E[T    2 ] (E[T 2 ])2 ) : 168This completes the proof .  Proof of Proposition 6.7: In Proposition 6.6, replace T2 by h(T) since h(X) = h(T)R.  169Chapter 7 Conclusions and future research 7.1 Summary The topics for the thesis have shown their importance in quantitative modeling in insurance and finance where the assumption of normality would fail to explain the rare events and their nonlinear dependence structures. Two themes of the thesis are strength of dependence in the tails, and influence of tail patterns on risk measures. 7.1.1 Strength of dependence in the tails Tail order and intermediate tail dependence For multivariate insurance and financial data, the degree of positive association often appears to be higher on the tail than in the middle. To this end, we have intensively investigated various tail dependence patterns and made contributions to the development of relevant theories, including characterizing an intermediate tail dependence structure. Intermediate tail dependence can be explained as a continuum of strength of dependence in the tail between the tail quadrant independence (the tail behaves like the product of the univariate margins) and the usual tail dependence. We introduce the concepts of tail order and tail order functions for studying copula families that have various tail patterns, and find a new pattern of intermediate tail dependence 170for upper tails of Archimedean copulas and conditions that lead to such a pattern; this fills in gaps in the literature. In addition, we have developed theories that relate tail heaviness of a positive random variable to the tail behavior of the Archimedean copula constructed with the LT of the random variable, which is an insightful way to understand the tail behavior of Archimedean copulas. 7.1.2 Tail patterns and risk measures Study of tail patterns such as tail heaviness and tail dependence plays a critical role in risk assessment. To this end, we have conducted research in the following two aspects. Conservative tail dependence structure The influence of dependence in the tails on risk measures for multivariate insurance or financial data has attracted more and more attention of researchers. However, it is very difficult to give a good estimation of the tail dependence as the data in the tail is sparse. To deal with this situation, we propose a data-driven conservative dependence structure to account for the uncertainty in tail dependence. The conservative dependence structure is referred to as tail comonotonicity. It is assumed that the strongest dependence appears when losses go to infinity, while the dependence on the sub-extremal level is characterized by the second- order parameters that can be estimated using data. The data-driven merit provides a practical way to obtain a better guess on the worst dependence structure, while the strongest positive dependence on the tail guarantees that the less information we have and the larger the losses are, the more conservative assumptions are adopted. We have conducted a thorough investigation on the properties and constructions of this dependence structure, and found interesting asymptotic additivity properties of relevant risk measures. Sufficient conditions have been obtained to justify the conservativity of tail comonotonicity. In addition to theoretical studies, real data analysis and intensive simulations suggest that, by assuming tail comonotonicity, one does not lose much accuracy but gain reasonably conservative risk assessment, especially for high-risk scenarios. 171Sub-extremal risks Sub-extremal risks can be referred to as the risks that are higher than a certain high threshold (e.g., 90% quantile). For modeling sub-extremal losses, an important question is to understand the probabilistic structures in the sub-extremal level. To answer this question, we have studied a general second-order tail heaviness struc- ture and established second-order approximations to conditional tail expectations (CTE) for both univariate and multivariate dependent risks. We use 2RV conditions to study the properties of risk measures on the sub- extremal level. Under some mild conditions that can be easily satisfied, closed- form second-order approximations for both the univariate and multivariate cases of CTE have been derived. The results provide a clear and general probabilistic structure for characterizing the distributional tails, and indicate conditions under which a second order parameter becomes more important. 7.2 Future research A couple of research questions that are relevant to the theory contained in this thesis are presented in what follows. 7.2.1 Tail behavior of CTEs In Chapter 5, E[X1jX2 > t] and E[X1jX2 = t] have been studied in terms of asymptotic comparisons under the condition of different tail dependence patterns. That is, different tail properties of copulas affect the CTEs, and the asymptotic behavior of E(X1jX2 > t) and E(X1jX2 = t) would be another way to show how copula families can be differentiated, and to show that the choice of copula family has a big influence on tail inference. We first use plots to illustrate comparisons between these three cases of usual tail dependence ( = 1), intermediate tail dependence (1 <  < 2) and tail quad- rant independence ( = 2) by Figure 7.1. The Blomqvist’s  for the three columns of the plots in Figure 7.1 were chosen to be  = 0:3; 0:6; 0:9, respectively. In each plot, the parameters for Gumbel, s.Gumbel and MTCJ copulas were calculated based on the same Blomqvist’s  . For the Gumbel copula,  = 1:434; 2:484; 9:709, 172respectively for each column, and so does the  for the s.Gumbel copula. For the MTCJ copula,  = 0:863; 2:764; 13:513, respectively for each column. The pa- rameter for the identical Pareto margins were chosen to be the same in each row; that is,  = 2; 100; 1:1, respectively. Based on Figure 7.1, we find that when the upper tail order is 1 (Gumbel), the CTE plots seem to be linear in t on the whole support no matter what the  is. When the upper tail order is 2 (MTCJ), the CTE lines become very flat. The CTE plots for the intermediate upper tail dependence with the upper tail order 1 <  < 2 (s.Gumbel) are located between the above two cases. However, if the Pareto margins are too heavy-tailed with a very small parameter  (e.g.,  = 1:1 in the last row of Figure 7.1), the CTE plots are not suitable for discriminating the three cases of tail orders. So, it may suggest to choose a relatively large  for the purpose of discriminating the types of tail orders. Moreover, when Blomqvist’s  is very large (e.g.,  = 0:9), it becomes very hard to distinguish tail dependence and intermediate tail dependence; similarly, when Blomqvist’s  is very small, the plot may becomes difficult to differentiate intermediate tail dependence and tail quadrant independence. In what follows, we will try to keep the arguments suitable to general cases when discussing the three types of tail behavior, otherwise, concrete examples will be given to show the possible behavior if a general discussion is not possible at the moment. The research goal will be to obtain checkable sufficient conditions to confirm whether CTE behavior is t1‘(t), t ‘(t) with 0 <  < 1, or t0‘(t), as t!1. Usual tail dependence The linearity in t for the usual tail dependence case can be explained as what fol- lows. If X1 and X2 have the same regularly varying upper tail and their copula possess an upper tail dependence function, then (X1; X2) is multivariate regularly varying (see Weng and Zhang, 2012); let the corresponding intensity measure be  . Choosing B := [0;1]  [1;1] in Theorem 2.2 (1) of Joe and Li (2011), we 173Figure 7.1: CTE plots for E[X1jX2 > t]. The range of t in each plot was chosen to cover the support of t between the 1% and 99% quantiles. Pareto margins(θ = 2); Blomqvist β = 0.3 t E(  X 1 | X 2  > t ) 0 2 4 6 8 10 12 0 2 4 6 8 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 2); Blomqvist β = 0.6 t E(  X 1 | X 2  > t ) 0 5 10 15 0 2 4 6 8 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 2); Blomqvist β = 0.9 t E(  X 1 | X 2  > t ) 0 5 10 15 0 2 4 6 8 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 100); Blomqvist β = 0.3 t E(  X 1 | X 2  > t ) 0.00 0.01 0.02 0.03 0.04 0.00 0.01 0.02 0.03 0.04 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 100); Blomqvist β = 0.6 t E(  X 1 | X 2  > t ) 0.00 0.01 0.02 0.03 0.04 0.05 0.00 0.01 0.02 0.03 0.04 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 100); Blomqvist β = 0.9 t E(  X 1 | X 2  > t ) 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.00 0.01 0.02 0.03 0.04 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 1.1); Blomqvist β = 0.3 t E(  X 1 | X 2  > t ) 0 200 400 600 0 20 40 60 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 1.1); Blomqvist β = 0.6 t E(  X 1 | X 2  > t ) 0 200 400 600 0 20 40 60 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 1.1); Blomqvist β = 0.9 t E(  X 1 | X 2  > t ) 0 200 400 600 0 20 40 60 Gumbel s.Gumbel MTCJ y=x 174have E[X1jX2 > t]  t Z 1 0  ([w;1] [1;1])  B dw; t!1: (7.1) So the CTE plot should be approximately linear in t when t is sufficiently large, as long as the integration in (7.1) is finite. Here, an open question is that, for the Gumbel copula, why the linearity always appears even when t is very small. Intermediate tail dependence The relevant theory for the other two cases is more tricky. Now, let us look at the example of Pareto margins with a survival Gumbel copula more closely, for the intermediate tail dependence case. Example 7.1 (Pareto margins and survival Gumbel copula) Suppose X1 and X2 have a copula C that is the survival copula of a Gumbel copula bC, so that the upper tail order of C is 1 <  = 21= < 2. Then we can write E[X1jX2 > t] = [F (t)] 1 Z 1 0 bC(F (x); F (t))dx = [F (t)] 1 Z 1 0 exp    [ logF (x)] + [ logF (t)]  1=  dx = (1 + t) Z 1 0 exp     [log(1 + x)] + [log(1 + t)]  1=  dx = Z 1 0 exp  s    y + s  1= + y  dy; y := log(1 + x); s := log(1 + t) = s Z 1 0 exp  s      z + 1  1= + z   dz; z := y=s =: s Z 1 0 esg(z)dz; where g(z) :=     z + 1  1= + z. As t ! 1, s ! 1, so we may use a Laplace approximation to evaluate the behavior of R1 0 e sg(z)dz as s ! 1. In order to apply the Laplace approximation, the function g(z) has to satisfy some regularity conditions (see Small, 2010) as follows. 175(1) Clearly, g(z) is differentiable on (0;1), and the global maximum is at- tained when z = z0 = [  =(  1)  1] 1= . As  ;  > 1, 0 < z0 < 1. While g00(z) = (1  ) z  2(1 + z )1=  2 < 0 for z 2 (0;1) when  ;  > 1. (2) Since g(0) = 0, g(1) =  1, g(z) is strictly increasing for z 2 (0; z0] and g(z) is strictly decreasing for z 2 [z0;1), we may choose a 0 <  < z0 and  = g(z0)  maxfg(z0   ); g(z0 +  )g > 0 such that g(z) < g(z0)   for all z 2 (0;1) \ fz : jz  z0j   g. (3) Also, R1 0 expf    z + 1  1= + zgdz < e R1 0 e z(1  )dz <1. Therefore, as t!1, E[X1jX2 > t]  sesg(z0) s 2  sg00(z0) = (1 + t)g(z0) s 2 log(1 + t)  g00(z0) ; and g(z0) =      =(  1)  1  (  1)= > 0 affects the behavior of E[X1jX2 > t]. Note that, the Laplace approximation involves a Taylor expansion to the second order of sg(z) and reweighing of the Gaussian integral for a Gaussian random variable with the variance being  (sg00(z0)) 1. Let  ( ;  ) := g(z0), then @ ( ;  )=@ = 1   1    =(  1)   1= < 0 and thus  ( ;  ) is deceasing in  . So increasing  , the parameter for the Pareto margins, will decrease the speed of E[X1jX2 > t] as t ! 1. Similarly, it can be easily verified that, for  ;  > 1, @ ( ;  )=@ > 0. Therefore, increasing  will increase the speed of E[X1jX2 > t] as t ! 1. Those are consistent to the plots in Figure 7.1. Note that, the speed of E[X1jX2 > t] can be roughly compared by comparing the relative positions of the CTE lines to the “y = x” line. Tail quadrant independence From Figure 7.1, we can find that the CTE line for the tail quadrant independence case (MTCJ) tends to converge to a finite number, if the parameter  for the Pareto margins is sufficiently large. Now, suppose continuous random variables X1 d = X2  F supported on [0;1) with the copula C. Let g(v) := C(u; v)  v for a given 0  u  1. 176Then a Taylor expansion at v = 1 leads to, as v ! 1 , g(v) = (u 1) + (1 v)[1 C1j2(uj1)] + 2  1D22C(u; 1)(v  1)2 +R(u; v); where D22C(u; 1) = @2C(u; v)=@2vjv=1 and the remainder R(u; v) = o((v  1)2). If there exists a v0 < 1 such that v > v0 implies that 2 1D22C(u; 1)(v  1)2 +R(u; v)  0; for u a.e.; (7.2) that is, g(v)  h(v) := (u 1) + (1 v)[1 C1j2(uj1)], and let u = F (x), then C(F (x); v) v  (F (x) 1) + (1 v)[1 C1j2(F (x)j1)]. Therefore as v > v0, E[X1jX2 > t] = (1 v) 1  E[X1] + Z 1 0 [C(F (x); v) v]dx   (1 v) 1  E[X1] + Z 1 0 (F (x) 1) + (1 v)[1 C1j2(F (x)j1)]dx  = Z 1 0 [1 C1j2(F (x)j1)]dx: If G(x) := lim v!1 C1j2(F (x)jv) exists G(x) is the cdf of Y; and E[Y ] <1: (7.3) Then by the dominated convergence theorem, lim t!1 E[X1jX2 > t] = lim v!1 R1 0 fC(F (x); v) v + 1 F (x)gdx 1 v = Z 1 0 lim v!1 C(F (x); v) v + 1 F (x) 1 v dx = Z 1 0  1 C1j2(F (x)j1)  dx: 177That is, E[X1jX2 > t]! E[Y ] <1; t!1: Condition (7.3) is easy to verify. From Table 1.2, tail quadrant independent copulas such as Frank, MTCJ and BB2 all satisfy the condition that G(x) := C1j2(F (x)j1) is the cdf of a random variable Y . By appropriately choosing F (x), we can have E[Y ] < 1. However, the condition (7.2) or related conditions re- quire further investigation, and a similar final conclusion could be obtained with sufficient conditions other than (7.2). Similar patterns can be observed in Figure 7.2 for the corresponding CTE plots of E[X1jX2 = t]. Unlike the former case, E[X1jX2 = t] is relatively harder to get the empirical quantities. So CTE plots for E[X1jX2 > t] have a better potential to be further developed as a diagnostic tool for the patterns of dependence in the tails. We now close this subsection by giving an example for the intermediate tail dependence case. Example 7.2 (Pareto margins and survival Gumbel copula) Suppose X1 and X2 have a copula C that is the survival copula of a Gumbel copula bC, so that the upper tail order of C is 1 <  = 21= < 2. Letting y := log(1 + x); s := log(1 + t) and 178Figure 7.2: CTE plots for E[X1jX2 = t]. The range of t in each plot was chosen to cover the support of t between the 1% and 99% quantiles. Pareto margins(θ = 2); Blomqvist β = 0.3 t E(  X 1 | X 2  > t ) 0 2 4 6 8 0 2 4 6 8 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 2); Blomqvist β = 0.6 t E(  X 1 | X 2  > t ) 0 2 4 6 8 0 2 4 6 8 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 2); Blomqvist β = 0.9 t E(  X 1 | X 2  > t ) 0 2 4 6 8 0 2 4 6 8 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 100); Blomqvist β = 0.3 t E(  X 1 | X 2  > t ) 0.00 0.01 0.02 0.03 0.04 0.00 0.01 0.02 0.03 0.04 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 100); Blomqvist β = 0.6 t E(  X 1 | X 2  > t ) 0.00 0.01 0.02 0.03 0.04 0.00 0.01 0.02 0.03 0.04 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 100); Blomqvist β = 0.9 t E(  X 1 | X 2  > t ) 0.00 0.01 0.02 0.03 0.04 0.00 0.01 0.02 0.03 0.04 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 1.1); Blomqvist β = 0.3 t E(  X 1 | X 2  > t ) 0 20 40 60 0 20 40 60 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 1.1); Blomqvist β = 0.6 t E(  X 1 | X 2  > t ) 0 20 40 60 0 20 40 60 Gumbel s.Gumbel MTCJ y=x Pareto margins(θ = 1.1); Blomqvist β = 0.9 t E(  X 1 | X 2  > t ) 0 20 40 60 0 20 40 60 Gumbel s.Gumbel MTCJ y=x 179z := y=s, we can write E[X1jX2 = t] = Z +1 0 f bC1j2(F (x)jF (t))gdx = Z +1 0 1 F (t) exp    [ logF (x)] + [ logF (t)]  1=   h 1 +  [ logF (x)]=[ logF (t)]   i 1+1= dx = Z 1 0 exp  s    y + s  1= + y   h 1 + (y=s) i 1+1= dy; = s Z 1 0 exp  s      z + 1  1= + z     1 + z   1+1= dz; =: s Z 1 0 h(z)esg(z)dz; where g(z) :=     z + 1  1= + z and h(z) :=  1 + z   1+1= . We may also use a Laplace approximation. From Example 7.1, the regularity conditions for g(z) are hold. In addition, h(z) is continuous on (0;1), and h(z0) 6= 0. Therefore (see Small, 2010), as t!1, E[X1jX2 > t]  sesg(z0)h(z0) s 2  sg00(z0) = (1 + t)g(z0)h(z0) s 2 log(1 + t)  g00(z0) : 7.2.2 Intermediate tail dependence from hidden regular variation Another relevant topic is the relationship1 between intermediate tail dependence and Hidden Regular Variation (HRV) (Resnick, 2002). In Resnick (2007), HRV is defined on the cone E0 := fs 2 E : 9 1  i < j  d; si ^ sj > 0g; (7.4) 1Thanks to Professor Haijun Li for pointing out the connection to Hidden Regular Variation. 180that is, at most d 2 elements are 0, i.e., removing the axes of E := [0;1]d n f0g. More precisely, if there exist scaling functions b(t); b0(t)!1, b(t)=b0(t)! 1 as t!1, such that as t!1, tP  X b(t) 2 B  !  (B) (7.5) for all relatively compact sets B  R d +nf0g with  (@B) = 0, and tP  X b0(t) 2 B  !  0(B) (7.6) for all relatively compact setsB  R d +n[ d i=1fte (i); t  0g satisfying  0(@B) = 0, then X is said to possess HRV on E0. The tail order functions (see Definition 3.2) we have studied is closely related to the concept of HRV. With certain regularity conditions, we can establish the relationship between tail order functions for the intermediate tail dependence case to the Radon measure  0 for the HRV. This is a technical result to be included in a future publication. 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