Inhomogeneous Dependence Modeling with Time-Varying Copulae
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.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 27 (2009)
Issue (Month): 2 ()
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Other versions of this item:
- Enzo Giacomini & Wolfgang Härdle & Ekaterina Ignatieva & Vladimir Spokoiny, 2006. "Inhomogeneous Dependency Modelling with Time Varying Copulae," SFB 649 Discussion Papers SFB649DP2006-075, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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