Conditional Dependency of Financial Series: The Copula-GARCH Model
AbstractWe 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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Bibliographic InfoPaper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp69.
Date of creation: Dec 2002
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International correlation; Stock indices; Skewed Student-t distribution;
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- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
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- Dominique Guegan & Jing Zhang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00368336, HAL.
- Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00375765, HAL.
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