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dc.contributor.authorGiacomini, Enzoen_US
dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorIgnatieva, Ekaterinaen_US
dc.contributor.authorSpokoiny, Vladimiren_US
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_US
dc.publisher|aSFB 649, Economic Risk|cBerlinen_US
dc.relation.ispartofseries|aSFB 649 discussion paper|x2006-075en_US
dc.subject.keywordtime varying copulaen_US
dc.subject.keywordadaptive estimationen_US
dc.subject.keywordnonparametric estimationen_US
dc.titleInhomogeneous dependency modelling with time varying copulaeen_US
dc.type|aWorking Paperen_US

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