Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall
Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall
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DOI：
10.1093/jjfinec/nbn007
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发表时间：
2008-07
影响因子：
2.5
通讯作者：
James W. Taylor
中科院分区：
文献类型：
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作者：
James W. Taylor
我们提出了指数加权分位数回归(EWQR)来估计时变分位数。EWQR成本函数可用作估计与EWQR分位数预测相关的随时间变化的预期缺口的基础。我们在核估计框架中表示EWQR，然后通过采用先前提出的双核估计器对其进行修改，以便为随时间变化相对较快的尾部分位数提供更高的精度。我们引入了双核分位数回归，它将双核思想推广到回归分位数的建模中。在我们对10个股票收益序列的实证研究中，不考虑杠杆效应的新方法的版本能够超越基于GARCH的方法和鱼子酱模型。
We propose exponentially weighted quantile regression (EWQR) for estimating time-varying quantiles. The EWQR cost function can be used as the basis for estimating the time-varying expected shortfall associated with the EWQR quantile forecast. We express EWQR in a kernel estimation framework, and then modify it by adapting a previously proposed double kernel estimator in order to provide greater accuracy for tail quantiles that are changing relatively quickly over time. We introduce double kernel quantile regression, which extends the double kernel idea to the modeling of quantiles in terms of regressors. In our empirical study of 10 stock returns series, the versions of the new methods that do not accommodate the leverage effect were able to outperform GARCH-based methods and CAViaR models.