Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/25158 
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dc.contributor.authorGiacomini, Enzoen
dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorIgnatieva, Ekaterinaen
dc.contributor.authorSpokoiny, Vladimiren
dc.date.accessioned2007-01-15-
dc.date.accessioned2009-07-23T14:44:17Z-
dc.date.available2009-07-23T14:44:17Z-
dc.date.issued2006-
dc.identifier.urihttp://hdl.handle.net/10419/25158-
dc.description.abstractMeasuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of applications, though, requires a modelling framework different from the multivariate normal. In risk management the non-normal behaviour of most financial time series calls for nonlinear (i.e. non-gaussian) dependency. The correct modelling of non-gaussian dependencies is therefore a key issue in the analysis of multivariate time series. In this paper we use copulae functions with adaptively estimated time varying parameters for modelling the distribution of returns, free from the usual normality assumptions. Further, we apply copulae to estimation of Value-at-Risk (VaR) of a portfolio and show its better performance over the RiskMetrics approach, a widely used methodology for VaR estimation.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2006-075en
dc.subject.jelC14en
dc.subject.ddc330en
dc.subject.keywordValue-at-Risken
dc.subject.keywordtime varying copulaen
dc.subject.keywordadaptive estimationen
dc.subject.keywordnonparametric estimationen
dc.titleInhomogeneous dependency modelling with time varying copulae-
dc.type|aWorking Paperen
dc.identifier.ppn522562183en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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