Modeling operational risk using the truncation approach
department or school: Science, Faculty of
department or school: Statistics, Department of
data provider: DSpace
degree campus: UBCV
creator: Hadley, Daniel P.
issued: 2018-07-30T15:59:33Z
issued: 2018
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Banks that use the advanced measurement approach to model operational risk may struggle to develop an internal process that produces stable regulatory capital over time. Large decreases in regulatory capital are scrutinized by regulators while large increases may force banks to set aside more assets than necessary. A major source of this instability arises from the loss severity selection process, especially when the selected distribution families for severity risk categories change year-to-year. In this report, we examine the process of selecting severity distributions from a candidate distribution list within the guidelines of the advanced measurement approach, propose useful tools to aid in selecting an appropriate severity distribution, and analyze the effect of selection criteria on regulatory capital. The log sinh-arcsinh distribution family is added to a list of common candidate severity distributions used by industry. This 4-parameter family solves issues introduced by the 4-parameter g-and-h distribution without sacrificing flexibility and shows promise in outperforming 2-parameter families, reducing the frequency of severity distribution families changing year-to-year. Distribution parameters are estimated using the maximum likelihood approach from loss data truncated at a known minimum reporting threshold. Our severity distribution selection process combines truncation probability estimates with Akaike Information Criterion (AIC), Bayesian Information Criterion, modified Anderson-Darling, QQ-plots, and predictive measures such as the quantile scoring function and out-of-sample AIC, and we discuss some of the challenges associated with this process. We then simulate operational losses and calculate regulatory capital, comparing the effect on regulatory capital of selecting loss severity distributions using AIC versus quantile score. A combination of these two criteria is recommended when selecting loss severity distributions.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/66611
note: Modeling Operational Risk using the TruncationApproachbyDaniel P. HadleyB.Sc., Economics, The University of Scranton, 2005A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Statistics)The University of British Columbia(Vancouver)July 2018c© Daniel P. Hadley, 2018The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Modeling Operational Risk using the Truncation Approachsubmitted by Daniel P. Hadley in partial fulfillment of the requirements for thedegree of Master of Science in Statistics.Examining Committee:Natalia Nolde, StatisticsSupervisorHarry Joe, StatisticsAdditional ExamineriiAbstractBanks that use the advanced measurement approach to model operational risk maystruggle to develop an internal process that produces stable regulatory capital overtime. Large decreases in regulatory capital are scrutinized by regulators while largeincreases may force banks to set aside more assets than necessary. A major sourceof this instability arises from the loss severity selection process, especially whenthe selected distribution families for severity risk categories change year-to-year. Inthis report, we examine the process of selecting severity distributions from a candi-date distribution list within the guidelines of the advanced measurement approach,propose useful tools to aid in selecting an appropriate severity distribution, and an-alyze the effect of selection criteria on regulatory capital. The log sinh-arcsinh dis-tribution family is added to a list of common candidate severity distributions usedby industry. This 4-parameter family solves issues introduced by the 4-parameterg-and-h distribution without sacrificing flexibility and shows promise in outper-forming 2-parameter families, reducing the frequency of severity distribution fam-ilies changing year-to-year. Distribut…
has type: Thesis/Dissertation
date issued: 2018-09
is shown at: 10.14288/1.0369252
language: eng
degree discipline: Statistics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/66611