Conditional extremes in asymmetric financial markets
department or school: Science, Faculty of
department or school: Statistics, Department of
data provider: DSpace
degree campus: UBCV
creator: Zhang, Jinyuan
issued: 2015-07-28T15:00:53Z
issued: 2015
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: The project focuses on the estimation of the probability distribution of a bivariate random vector given that one of the components takes on a large value. These conditional probabilities can be used to quantify the effect of financial contagion when the random vector represents losses on financial assets and as a stress-testing tool in financial risk management. However, it is tricky to quantify these conditional probabilities when the main interest lies in the tails of the underlying distribution. Specifically, empirical probabilities fail to provide adequate estimates while fully parametric methods are subject to large model uncertainty as there is too little data to assess the model fit in the tails. We propose a semi-parametric framework using asymptotic results in the spirit of extreme values theory. The main contributions include an extension of the limit theorem in Abdous et al. [Canad. J. Statist. 33 (2005)] to allow for asymmetry, frequently encountered in financial and insurance applications, and a new approach for inference. The results are illustrated using simulations and two applications in finance.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/54186
note: Conditional Extremes in AsymmetricFinancial MarketsbyJinyuan ZhangB.Sc., The Chinese University of Hong Kong, 2009A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Statistics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)July 2015c© Jinyuan Zhang 2015AbstractThe project focuses on the estimation of the probability distribution of abivariate random vector given that one of the components takes on a largevalue. These conditional probabilities can be used to quantify the effect offinancial contagion when the random vector represents losses on financialassets and as a stress-testing tool in financial risk management. However, itis tricky to quantify these conditional probabilities when the main interestlies in the tails of the underlying distribution. Specifically, empirical prob-abilities fail to provide adequate estimates while fully parametric methodsare subject to large model uncertainty as there is too little data to assessthe model fit in the tails.We propose a semi-parametric framework using asymptotic results in thespirit of extreme values theory. The main contributions include an exten-sion of the limit theorem in Abdous et al. [Canad. J. Statist. 33 (2005)]to allow for asymmetry, frequently encountered in financial and insuranceapplications, and a new approach for inference. The results are illustratedusing simulations and two applications in finance.iiPrefaceThis work was prepared under the supervision of Professor Natalia Nolde.Chapter 3 and 4.1 were developed based on draft of Professor Natalia Nolde.The remaining chapters were independently written by the author. ProfessorNatalia Nolde provided many valuable suggestions on improving the materialin these chapters.iiiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiTable of Contents . . . . . …
has type: Thesis/Dissertation
date issued: 2015-09
is shown at: 10.14288/1.0166440
language: eng
degree discipline: Statistics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NoDerivs 2.5 Canada
rights URI: http://creativecommons.org/licenses/by-nd/2.5/ca/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/54186