GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series
GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series
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DOI：
10.1080/14697688.2022.2048061
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发表时间：
2021-04
影响因子：
1.3
通讯作者：
Hibiki Kaibuchi;Yoshinori Kawasaki;Gilles Stupfler
中科院分区：
文献类型：
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作者：
Hibiki Kaibuchi;Yoshinori Kawasaki;Gilles Stupfler
风险价值（VaR）是金融风险管理中广泛使用的一种工具。从操作和监管的角度来看，在极端水平下估计损失收益分布的VaR是金融应用中的一个重要问题；特别是，在最近的过去，极端风险值的动态估计受到了大量关注。我们在这里提出了一种新的两步减偏估计方法，用于提前一步动态极端VaR的估计，称为GARCH-UGH（无偏Gomes-de Haan），其中首先使用AR-GARCH模型过滤财务回报，然后将极端分位数的减偏估计器应用于标准化残差。我们的研究结果表明，从几个金融时间序列的历史日收益率的样本内和样本外回溯检验的角度来看，GARCH-UGH对动态极端VaR的估计比历史模拟、采用高斯或Student-t创新的传统AR-GARCH滤波或采用标准极值估计的AR-GARCH滤波获得的估计更准确。
The Value-at-Risk (VaR) is a widely used instrument in financial risk management. The question of estimating the VaR of loss return distributions at extreme levels is an important question in financial applications, both from operational and regulatory perspectives; in particular, the dynamic estimation of extreme VaR given the recent past has received substantial attention. We propose here a new two-step bias-reduced estimation methodology for the estimation of one-step ahead dynamic extreme VaR, called GARCH-UGH (Unbiased Gomes-de Haan), whereby financial returns are first filtered using an AR-GARCH model, and then a bias-reduced estimator of extreme quantiles is applied to the standardized residuals. Our results indicate that the GARCH-UGH estimates of the dynamic extreme VaR are more accurate than those obtained either by historical simulation, conventional AR-GARCH filtering with Gaussian or Student-t innovations, or AR-GARCH filtering with standard extreme value estimates, both from the perspective of in-sample and out-of-sample backtestings of historical daily returns on several financial time series.