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dc.contributor.authorSiburg, Karl Friedrich-
dc.contributor.authorStoimenov, Pavel-
dc.contributor.authorWeiß, Gregor N. F.-
dc.date.accessioned2013-04-29T14:13:06Z-
dc.date.available2013-04-29T14:13:06Z-
dc.date.issued2013-04-29-
dc.identifier.urihttp://hdl.handle.net/2003/30287-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-10472-
dc.description.abstractWe propose to forecast the Value-at-Risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coefficient of lower tail dependence. We compare our proposed method to a conventional copula-GARCH model where the parameter of a Clayton copula is estimated via Canonical Maximum-Likelihood. The superiority of our proposed model is exemplified by analyzing a data sample of nine different financial portfolios. A comparison of the out-of-sample forecasting accuracy of both models confirms that our model yields economically significantly better Value-at-Risk forecasts than the competing parametric calibration strategy.en
dc.language.isoen-
dc.subjectCanonical Maximum-Likelihoodde
dc.subjectCopulaen
dc.subjectnonparametric estimationen
dc.subjecttail dependenceen
dc.subjectValue-at-Risken
dc.subject.ddc610-
dc.titleForecasting Portfolio-Value-at-Risk with Nonparametric Lower Tail Dependence Estimatesen
dc.typeTextde
dc.type.publicationtypepreprinten
dcterms.accessRightsopen access-
Appears in Collections:Preprints der Fakultät für Mathematik

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