Kernel quantile-based estimation of expected shortfall
File(s)
Author(s)
Yu, Keming
Ally, Abdallah K
Yang, Shanchao
Hand, David J
Type
Journal Article
Abstract
Since its proposal as an alternative risk measure to value-at-risk (VaR), expected shortfall (ES) has attracted a great deal of attention in financial risk management, primarily owing to its coherent properties. Recently, there has been an upsurge of research on the estimation of ES from a non-parametric perspective. The focus of this paper is on a few kernel-based ES estimators, including jack knife-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 finite-sample fitting 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
effective in reducing the tail estimation bias as well as the consequential bias that arises in kernel smoothing and finite-sample fitting 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
Date Issued
2010-06-01
Date Acceptance
2010-06-01
Citation
Journal of Risk, 2010, 12 (4), pp.15-32
ISSN
1465-1211
Publisher
Infopro Digital Services
Start Page
15
End Page
32
Journal / Book Title
Journal of Risk
Volume
12
Issue
4
Copyright Statement
Copyright © Infopro Digital Services Limited. 2010 The Author(s).
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000293427300003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
BANDWIDTH SELECTION
Business & Economics
Business, Finance
NONPARAMETRIC-ESTIMATION
SENSITIVITY-ANALYSIS
Social Sciences
Publication Status
Published
Date Publish Online
2010-06-24