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Kernel Quantile-Based Estimation of Expected Shortfall
Company: Journal of Risk
Company Url: Click here to open
Year Of Publication: 2010
Month Of Publication: June
Resource Link: Click here to open
Pages: 15-32
Download Count: 0
View Count: 1427
Comment Num: 0
Language: English
Source: article
Who Can Read: Free
Date: 8-5-2012
Publisher: Administrator
Summary
The focus of this paper is on a few kernel-based ES estimators, including jackknife-based bias-correction estimators that have theoretically been documented to reduce bias. Bias reduction is particularly effective in reducing the tail estimation bias as well as the consequential bias that arises in kernel smoothing and ?nite-sample ?tting and, thus, serves as a natural approach to the estimation of extreme quantiles of asset price distributions. By taking advantage of ES as an integral of the quantile function, a new type of ES estimator is proposed. To compare the performance of the estimators, a series of comparative simulation studies are presented and the methods are applied to real data. An estimator that has an analytical form turned out to perform the best.
(volume 12, number 4)
This documented may be downloaded without charge from risk.net.
Author(s)
Yu, Keming Sign in to follow this author
Ally, Abdallah K. Sign in to follow this author
Yang, Shanchao Sign in to follow this author
Hand, David J. Sign in to follow this author
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