{"title":"Model Risk in the Fundamental Review of the Trading Book: The Case of the Default Risk Charge","authors":"S. Wilkens, Mirela Predescu","doi":"10.2139/SSRN.3053426","DOIUrl":null,"url":null,"abstract":"The recent Fundamental Review of the Trading Book (FRTB) resulted in revised standards for capital requirements for market risks in a bank’s trading book. As part of the ruleset, default risk needs to be measured and capitalized through a dedicated Default Risk Charge (DRC). With the DRC as an extreme tail risk measure at 99.9% confidence level for portfolio default losses at a one-year horizon, there is inherent model risk associated with the reflection of joint defaults. Wilkens and Predescu (2017) proposed an overall framework for modeling the DRC that is based on a Gaussian factor copula model to capture the coincidence of defaults. This paper assesses the resulting model risk by analyzing alternative copulas (Gaussian, Student t, and Clayton) and the influence on the DRC figures with the help of a set of example portfolios. The copula choice can affect the DRC considerably, especially for directional and less diversified portfolios; the influence on typical larger-scale, diversified portfolios is much less pronounced. The uncertainty arising from the calibration of any copula from only a few data points – as implied by the regulation – is at least of equal importance as the selection of the dependence model itself.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"38 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Risk Model Validation","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.2139/SSRN.3053426","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 3
Abstract
The recent Fundamental Review of the Trading Book (FRTB) resulted in revised standards for capital requirements for market risks in a bank’s trading book. As part of the ruleset, default risk needs to be measured and capitalized through a dedicated Default Risk Charge (DRC). With the DRC as an extreme tail risk measure at 99.9% confidence level for portfolio default losses at a one-year horizon, there is inherent model risk associated with the reflection of joint defaults. Wilkens and Predescu (2017) proposed an overall framework for modeling the DRC that is based on a Gaussian factor copula model to capture the coincidence of defaults. This paper assesses the resulting model risk by analyzing alternative copulas (Gaussian, Student t, and Clayton) and the influence on the DRC figures with the help of a set of example portfolios. The copula choice can affect the DRC considerably, especially for directional and less diversified portfolios; the influence on typical larger-scale, diversified portfolios is much less pronounced. The uncertainty arising from the calibration of any copula from only a few data points – as implied by the regulation – is at least of equal importance as the selection of the dependence model itself.
期刊介绍:
As monetary institutions rely greatly on economic and financial models for a wide array of applications, model validation has become progressively inventive within the field of risk. The Journal of Risk Model Validation focuses on the implementation and validation of risk models, and aims to provide a greater understanding of key issues including the empirical evaluation of existing models, pitfalls in model validation and the development of new methods. We also publish papers on back-testing. Our main field of application is in credit risk modelling but we are happy to consider any issues of risk model validation for any financial asset class. The Journal of Risk Model Validation considers submissions in the form of research papers on topics including, but not limited to: Empirical model evaluation studies Backtesting studies Stress-testing studies New methods of model validation/backtesting/stress-testing Best practices in model development, deployment, production and maintenance Pitfalls in model validation techniques (all types of risk, forecasting, pricing and rating)