Mathematical Modeling of Concentration Risk under the Default Risk Charge Using Probability and Statistics Theory

IF 1 Q3 STATISTICS & PROBABILITY
Badreddine Slime
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引用次数: 1

Abstract

In the Fundamental Review of the Trading Book (FRTB), the latest regulation for minimum capital market risk requirements, one of the major changes, is replacing the Incremental Risk Charge (IRC) with the Default Risk Charge (DRC). The DRC measures only the default and does not consider the migration rating risk. The second new change in this approach was that the DRC now includes equity assets, contrary to the IRC. This paper studies DRC modeling under the Internal Model Approach (IMA) and the regulator conditions that every DRC component must respect. The FRTB presents the DRC measurement as Value at Risk (VaR) over a one-year horizon, with the quantile equal to 99.9%. We use multifactor adjustment to measure the DRC and compare it with the Monte Carlo Model to understand how the approach fits. We then define concentration in the DRC and propose two methods to quantify the concentration risk: the Ad Hoc and Add-On methods. Finally, we study the behavior of the DRC with respect to the concentration risk.
违约风险收费下集中风险的概率统计数学建模
在《交易簿基本审查》(FRTB)中,最新的最低资本市场风险要求规定是主要变化之一,即以违约风险收费(DRC)取代增量风险收费(IRC)。DRC仅度量默认值,而不考虑迁移评级风险。这种方法的第二个新变化是,与IRC相反,DRC现在包括股权资产。本文研究了内部模型方法(IMA)下的DRC建模以及每个DRC组件必须遵守的调节器条件。FRTB将DRC度量表示为一年期的风险值(VaR),分位数等于99.9%。我们使用多因素调整来测量DRC,并将其与蒙特卡洛模型进行比较,以了解该方法如何适合。然后,我们定义了DRC中的浓度,并提出了两种量化浓度风险的方法:Ad Hoc和Add-On方法。最后,我们研究了刚果民主共和国在集中风险方面的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Probability and Statistics
Journal of Probability and Statistics STATISTICS & PROBABILITY-
自引率
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发文量
14
审稿时长
18 weeks
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