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Change analysis of dynamic copula for measuring dependence in multivariate financial data

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Abstract

Data in finance and insurance often cover a long time period. Therefore, the economic factors may induce some changes in the dependence structure. Recently, two methods to analyze such changes using copula have been proposed. The first approach only investigates the changes of copula parameters while ignoring the possibility of the changing for the copula family. The other one stresses to inspect any change for copulas by determining the best copula on every subsamples divided by moving-window. However, the width of moving window and the time interval of moving obviously greatly influence the accuracy of the result due to the copula change. In this paper, we first generalize the types of copula change as follows : (1) the changes of parameters while the copula family keeps static ; (2) the changes of copula family. Then, we propose a series of tests based on the conditional pseudo copula and goodness-of-fit (GOF) test to decide the type of change. Eventually, we take the full advantage of the previous methods by avoiding their deficiencies to deal with the changes. Combining the two brilliant ideas, we obtain all information on the changes (such as the change time, the change value of parameter and the change trend) that play the important role in risk management. Moreover, we illustrate our approach with FX spot data for Asian market, which shows that the approach can be practical in the real world as well.

Suggested Citation

  • Dominique Guégan & Jing Zhang, 2006. "Change analysis of dynamic copula for measuring dependence in multivariate financial data," Cahiers de la Maison des Sciences Economiques b06090, Université Panthéon-Sorbonne (Paris 1).
  • Handle: RePEc:mse:wpsorb:b06090
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    References listed on IDEAS

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    1. Cyril Caillault & Dominique Guegan, 2005. "Empirical estimation of tail dependence using copulas: application to Asian markets," Quantitative Finance, Taylor & Francis Journals, vol. 5(5), pages 489-501.
    2. 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.
    3. Dominique Guégan, 2007. "Global and local stationary modelling in finance : theory and empirical evidence," Documents de travail du Centre d'Economie de la Sorbonne b07053, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    4. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    5. 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.
    6. Alexandra Dias & Paul Embrechts, 2004. "Dynamic copula models for multivariate high-frequency data in finance," Working Papers wpn04-01, Warwick Business School, Finance Group.
    7. Fermanian, Jean-David, 2005. "Goodness-of-fit tests for copulas," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 119-152, July.
    8. Gombay, Edit & Horváth, Lajos, 1996. "On the Rate of Approximations for Maximum Likelihood Tests in Change-Point Models," Journal of Multivariate Analysis, Elsevier, vol. 56(1), pages 120-152, January.
    9. Granger, Clive W.J. & Terasvirta, Timo & Patton, Andrew J., 2006. "Common factors in conditional distributions for bivariate time series," Journal of Econometrics, Elsevier, vol. 132(1), pages 43-57, May.
    10. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
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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. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Dominique Guegan, 2007. "La persistance dans les marchés financiers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00179269, HAL.
    4. Dominique Guégan, 2009. "A Meta-Distribution for Non-Stationary Samples," CREATES Research Papers 2009-24, Department of Economics and Business Economics, Aarhus University.
    5. Rémillard, Bruno & Papageorgiou, Nicolas & Soustra, Frédéric, 2012. "Copula-based semiparametric models for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 30-42.
    6. Aepli, Matthias D. & Frauendorfer, Karl & Fuess, Roland & Paraschiv, Florentina, 2015. "Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation," Working Papers on Finance 1513, University of St. Gallen, School of Finance.
    7. Okyoung Na & Jiyeon Lee & Sangyeol Lee, 2013. "Change point detection in SCOMDY models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 215-238, July.
    8. Zhu, Xiaoqian & Xie, Yongjia & Li, Jianping & Wu, Dengsheng, 2015. "Change point detection for subprime crisis in American banking: From the perspective of risk dependence," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 18-28.

    More about this item

    Keywords

    Copula; dynamic copula; Goodness-of-Fit.;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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