Copulae in tempore varientes
Inhomogeneous Dependence Modeling with Time Varying Copulae
Wolfgang Härdle (Humboldt-Universität, Berlin)
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 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.
JEL classification: C 14, C16, C60, C61
Keywords: Value-at-Risk, time varying copulae, adaptive estimation, nonparametric estimation
For more information please contact Dr. M. Anufriev