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2010-02-04Diskussionspapier DOI: 10.18452/4237
Predicting extreme VaR
dc.contributor.authorSchaumburg, Julia
dc.date.accessioned2017-06-16T00:05:00Z
dc.date.available2017-06-16T00:05:00Z
dc.date.created2010-05-27
dc.date.issued2010-02-04
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4889
dc.description.abstractThis paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectValue at Riskeng
dc.subjectrisk managementeng
dc.subjectnonparametric quantile regressioneng
dc.subjectextreme value theoryeng
dc.subjectmonotonizationeng
dc.subjectCAViaReng
dc.subject.ddc330 Wirtschaft
dc.titlePredicting extreme VaR
dc.typeworkingPaper
dc.identifier.urnurn:nbn:de:kobv:11-100111227
dc.identifier.doihttp://dx.doi.org/10.18452/4237
local.edoc.pages28
local.edoc.type-nameDiskussionspapier
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-year2010
dc.title.subtitleNonparametric quantile regression with refinements from extreme value theory
dc.identifier.zdb2195055-6
bua.series.nameSonderforschungsbereich 649: Ökonomisches Risiko
bua.series.issuenumber2010,9

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