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Title:Inhomogeneous dependency modelling with time varying copulae PDF Logo
Authors:Giacomini, Enzo
Härdle, Wolfgang Karl
Ignatieva, Ekaterina
Spokoiny, Vladimir
Issue Date:2006
Series/Report no.:SFB 649 discussion paper 2006-075
Abstract:Measuring 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.
Subjects:Value-at-Risk
time varying copula
adaptive estimation
nonparametric estimation
JEL:C14
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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