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Inhomogeneous Dependency Modelling with Time Varying Copulae Author info | Abstract | Publisher info | Download info | Related research | Statistics Enzo Giacomini
Wolfgang Härdle
Ekaterina Ignatieva
Vladimir Spokoiny
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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.
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number
SFB649DP2006-075.
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Length: 51 pages
Date of creation: Nov 2006Date of revision:
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Keywords: Value-at-Risk ; time varying copula ; adaptive estimation ; nonparametric estimation. ; Other versions of this item:
Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
This paper has been announced in the following NEP Reports :
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