Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

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dc.citation.epage343
dc.citation.journalTitlePhysica A: Statistical Mechanics and its Applicationseng
dc.citation.spage330
dc.citation.volume401
dc.contributor.affiliationFaculty of Economics and Business, Department of Business, University of Salamanca, Spainspa
dc.contributor.affiliationSchool of Economics and Finance, Department of Finance, EAFIT University, Colombiaspa
dc.contributor.affiliationFaculty of Economics and Business, Department of Economics, University of Salamanca, Spainspa
dc.contributor.authorB. Del Brio, Estherspa
dc.contributor.authorMora-Valencia, Andrésspa
dc.contributor.authorPerote, Javierspa
dc.contributor.departmentEconomía y Finanzasspa
dc.contributor.departmentFinanzasspa
dc.contributor.programGrupo de Investigación Finanzas y Bancaspa
dc.date2014
dc.date.accessioned2015-11-06T21:15:35Z
dc.date.available2015-11-06T21:15:35Z
dc.date.issued2014
dc.description.abstractThe need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram–Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s tdistributions.eng
dc.identifier.doidoi:10.1016/j.physa.2014.01.037
dc.identifier.issn0378-4371
dc.identifier.urihttp://hdl.handle.net/10784/7616
dc.language.isoengeng
dc.publisherElseviereng
dc.relation.ispartofPhysica A: Statistical Mechanics and its Applications. Vol. 401, 2014, pp.330-343spa
dc.relation.isversionofhttp://www.sciencedirect.com/science/article/pii/S0378437114000491
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S0378437114000491
dc.rightsrestrictedAccesseng
dc.rightsCopyright © 2015 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V.spa
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccesseng
dc.rights.localAcceso restringidospa
dc.sourcePhysica A: Statistical Mechanics and its Applications. Vol. 401, 2014, pp.330-343spa
dc.subject.keywordHedge fundseng
dc.subject.keywordValue-at-riskeng
dc.subject.keywordBacktestingeng
dc.subject.keywordExtreme value theoryeng
dc.subject.keywordGram–Charlier serieseng
dc.subject.keywordCopulaseng
dc.titleSemi-nonparametric VaR forecasts for hedge funds during the recent crisiseng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.type.hasVersionObra publicadaspa
dc.type.hasVersionpublishedVersioneng
dc.type.localArtículospa

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