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Multinomial var backtests: A simple implicit approach to backtesting expected shortfall

机译:多项式var回测:一种简单的隐式方法来回测预期的不足

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摘要

Under the Fundamental Review of the Trading Book (FRTB) capital charges for the trading book are based on the coherent expected shortfall (ES) risk measure, which show greater sensitivity to tail risk. In this paper it is argued that backtesting of expected shortfall-or the trading book model from which it is calculated-can be based on a simultaneous multinomial test of value-at-risk (VaR) exceptions at different levels, an idea supported by an approximation of ES in terms of multiple quantiles of a distribution proposed in Emmer et al. (2015). By comparing Pearson, Nass and likelihood-ratio tests (LRTs) for different numbers of VaR levels N it is shown in a series of simulation experiments that multinomial tests with N ≥ 4 are much more powerful at detecting misspecifications of trading book loss models than standard bi-nomial exception tests corresponding to the case N = 1. Each test has its merits: Pearson offers simplicity; Nass is robust in its size properties to the choice of N ; the LRT is very powerful though slightly over-sized in small samples and more computationally burdensome. A traffic-light system for trading book models based on the multinomial test is proposed and the recommended procedure is applied to a real-data example spanning the 2008 financial crisis.
机译:在《交易簿基本审查》(FRTB)中,交易簿的资本费用基于一致的预期差额(ES)风险度量,这表明对尾部风险的敏感性更高。本文认为,对预期缺口的回测(或从中计算出的交易账簿模型)可以基于不同级别的风险价值(VaR)异常的同时多项检验,这一观点得到了支持。根据Emmer等人提出的分布的多个分位数,对ES进行近似。 (2015)。通过比较不同数量的VaR水平N的Pearson,Nass和似然比检验(LRT),在一系列模拟实验中表明,N≥4的多项式检验在检测交易账簿损失模型的错误指定方面比标准更有效。对应情况N = 1的二项式例外测试。每个测试都有其优点:Pearson提供简单性; Nass对N的选择具有强大的尺寸性质; LRT非常强大,尽管在小样本中尺寸稍大,但在计算上却比较繁琐。提出了基于多项式检验的交易簿模型交通信号灯系统,并将推荐的程序应用于跨越2008年金融危机的真实数据示例。

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