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Inhomogeneous Dependency Modelling with Time Varying Copulae
Company: Center for Applied Statistics and Economics, Homboldt-Universität zu Berlin
Company Url: Click here to open
Year Of Publication: 2006
Month Of Publication: November
Pages: 51
Download Count: 2
View Count: 1303
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 6-23-2011
Publisher: Administrator
Summary
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.
Author(s)
Giacomini, Enzo Sign in to follow this author
Hardle, Wolfgang Sign in to follow this author
Ignatieva, Katja Sign in to follow this author
Spokoiny, Vladimir Sign in to follow this author
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