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Conditional Dependency of Financial Series: The Copula-GARCH Model

  • Eric Jondeau

    (Banque de France, DEER)

  • Michael Rockinger

    (HEC-University of Lausanne)

We develop a new methodology to measure conditional dependency between time series each driven by complicated marginal distributions. We achieve this by using copula functions that link marginal distributions, and by expressing the parameter of the copula as a function of predetermined variables. The marginal model is an autoregressive version of Hansen’s (1994) GARCH-type model with time-varying skewness and kurtosis. Here, we extend, to a dynamic setting, the research that fo-cuses on asymmetries in correlation during extreme events. We show that, for many market indices, dependency increases subsequent to large extreme realizations. Furthermore, for several index pairs, this increase is stronger after crashes. Our model has many potential applications such as VaR measurement and portfolio allocation in non-gaussian environments.

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Paper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp69.

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Date of creation: Dec 2002
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Handle: RePEc:fam:rpseri:rp69
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