Model Risk in the Fundamental Review of the Trading Book: The Case of the Default Risk Charge

IF 0.4 4区 经济学 Q4 BUSINESS, FINANCE
S. Wilkens, Mirela Predescu
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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.
交易账簿基础审查中的模型风险:以违约风险收费为例
最近的交易账簿基本审查(FRTB)修订了银行交易账簿中市场风险的资本要求标准。作为规则集的一部分,需要通过专门的违约风险收费(default risk Charge, DRC)对违约风险进行度量和资本化。由于DRC作为一种极端尾部风险指标,在99.9%的置信水平上衡量一年内的投资组合违约损失,因此存在与联合违约反映相关的固有模型风险。Wilkens和Predescu(2017)提出了一个基于高斯因子联结模型(Gaussian factor copula model)的DRC建模总体框架,以捕捉默认的巧合。本文通过分析替代组合(高斯、Student t和克莱顿)以及在一组示例组合的帮助下对DRC数字的影响来评估由此产生的模型风险。copula的选择可以对DRC产生相当大的影响,特别是对于定向和不太多样化的投资组合;对典型的大规模、多元化投资组合的影响要小得多。从仅仅几个数据点校准任何联结所产生的不确定性——正如规则所暗示的那样——至少与依赖模型本身的选择同等重要。
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来源期刊
CiteScore
1.20
自引率
28.60%
发文量
8
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)
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