Nonparametric Estimation of Expected Shortfall Chen, Song
dc.contributor.department Statistics 2018-02-16T19:05:54.000 2020-07-02T06:56:12Z 2020-07-02T06:56:12Z 2004-04-01
dc.description.abstract <p>The paper evaluates the properties of nonparametric estimators of the expected shortfall, an increasingly popular risk measure in financial risk management. It is found that the existing kernel estimator based on a single bandwidth does not offer variance reduction, which is surprising considering that kernel smoothing reduces the variance of estimators for the value at risk and the distribution function. We reformulate the kernel estimator such that two different bandwidths are employed in the kernel smoothing for the value at risk and the shortfall itself. We demonstrate by both theoretical analysis and simulation studies that the new kernel estimator achieves a variance reduction. The paper also covers the practical issues of bandwidth selection and standard error estimation.</p>
dc.description.comments <p>This preprint was published as Song Xi Chen, "Nonparametric Estimation of Expected Shortfall", <em>Journal of Financial Econometrics</em> (2008): 87-107, doi: <a href="" target="_blank">10.1093/jjfinec/nbm019</a></p>
dc.identifier archive/
dc.identifier.articleid 1040
dc.identifier.contextkey 7331668
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_preprints/44
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 00:17:31 UTC 2022
dc.source.uri 10.1093/jjfinec/nbm019
dc.subject.disciplines Statistics and Probability
dc.subject.keywords kernel estimator
dc.subject.keywords risk measures
dc.subject.keywords smoothing bandwidth
dc.subject.keywords value at risk
dc.subject.keywords weak dependence
dc.title Nonparametric Estimation of Expected Shortfall
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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