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Conditional Dependency of Financial Series: The Copula-GARCH Model

Author

Listed:
  • Eric Jondeau

    (Banque de France, DEER)

  • Michael Rockinger

    (HEC-University of Lausanne)

Abstract
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.

Suggested Citation

  • Eric Jondeau & Michael Rockinger, 2002. "Conditional Dependency of Financial Series: The Copula-GARCH Model," FAME Research Paper Series rp69, International Center for Financial Asset Management and Engineering.
    • Handle: RePEc:fam:rpseri:rp69
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    Cited by:

    1. Dominique Guegan & Jing Zang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 777-795.
    2. Su, EnDer, 2014. "Measuring Contagion Risk in High Volatility State between Major Banks in Taiwan by Threshold Copula GARCH Model," MPRA Paper 58161, University Library of Munich, Germany.
    3. Alqaralleh, Huthaifa & Canepa, Alessandra & Zanetti Chini, Emilio, 2020. "COVID-19 Pandemic and Stock Market Contagion: A Wavelet-Copula GARCH Approach," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202012, University of Turin.
    4. Krzysztof Jajuga, 2004. "Application of Copula Functions in a Modelling of Relations in Multivariate Financial Time Series," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 6, pages 15-24.
    5. Alqaralleh, Huthaifa & Canepa, Alessandra & Chini, Zanetti, 2021. "Financial Contagion During the Covid-19 Pandemic: A Wavelet-Copula-GARCH Approach," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202110, University of Turin.
    6. EnDer Su, 2017. "Measuring and Testing Tail Dependence and Contagion Risk Between Major Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 325-351, August.
    7. Cyril Caillault, Dominique Guégan, 2009. "Forecasting VaR and Expected Shortfall Using Dynamical Systems: A Risk Management Strategy," Frontiers in Finance and Economics, SKEMA Business School, vol. 6(1), pages 26-50, April.
    8. ACATRINEI, Marius, 2015. "A Copula-Garch Model For A Proxy Portfolio For Bet-Fi Index," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 19(2), pages 8-16.
    9. Long Kang, 2011. "Asset allocation in a Bayesian copula-GARCH framework: An application to the ‘passive funds versus active funds’ problem," Journal of Asset Management, Palgrave Macmillan, vol. 12(1), pages 45-66, April.
    10. Michał Adam & Piotr Bańbuła & Michał Markun, 2013. "Dependence and contagion between asset prices in Poland and abroad. A copula approach," NBP Working Papers 169, Narodowy Bank Polski.
    11. Yang, Jingping & Cheng, Shihong & Zhang, Lihong, 2006. "Bivariate copula decomposition in terms of comonotonicity, countermonotonicity and independence," Insurance: Mathematics and Economics, Elsevier, vol. 39(2), pages 267-284, October.
    12. EnDer Su, 2018. "Measuring contagion risk in high volatility state among Taiwanese major banks," Risk Management, Palgrave Macmillan, vol. 20(3), pages 185-241, August.
    13. Michal Adam & Piotr Banbula & Michal Markun, 2015. "International Dependence and Contagion across Asset Classes: The Case of Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(3), pages 254-270, May.
    14. Denitsa Stefanova, 2012. "Stock Market Asymmetries: A Copula Diffusion," Tinbergen Institute Discussion Papers 12-125/IV/DSF45, Tinbergen Institute.

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    More about this item

    Keywords

    International correlation; Stock indices; Skewed Student-t distribution;
    All these keywords.

    JEL classification:

    • 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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