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Empirical estimation of tail dependence using copulas: application to Asian markets

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  • Cyril Caillault
  • Dominique Guegan

Abstract

This paper introduces non-parametric estimators for upper and lower tail dependence whose confidence intervals are obtained with a bootstrap method. We call these estimators 'naive estimators' as they represent a discretization of Joe's formulae linking copulas to tail dependence. We apply the methodology to an empirical data set composed of three composite indexes for the three Tigers (Thailand, Malaysia and Indonesia). The extremes show a dependence structure which is symmetric for the Thai and Malaysian markets and asymmetric for the Thai and Indonesian markets and for the Malaysian and the Indonesian markets. Using these results we estimate the copula (which belongs to the Student or Archimedean copula families) for each pair of markets by two methods. Finally, we provide risk measurements using the best copula associated with each pair of markets.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:quantf:v:5:y:2005:i:5:p:489-501
    DOI: 10.1080/14697680500147853
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    References listed on IDEAS

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    1. Y. Malevergne & D. Sornette, 2003. "Testing the Gaussian copula hypothesis for financial assets dependences," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 231-250.
    2. W. Breymann & A. Dias & P. Embrechts, 2003. "Dependence structures for multivariate high-frequency data in finance," Quantitative Finance, Taylor & Francis Journals, vol. 3(1), pages 1-14.
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    Cited by:

    1. 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.
    2. Dominique Guegan, 2005. "How can we Define the Concept of Long Memory? An Econometric Survey," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 113-149.
    3. Aloui, Riadh & Aïssa, Mohamed Safouane Ben & Nguyen, Duc Khuong, 2011. "Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure?," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 130-141, January.
    4. D. Guegan & J. Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 421-430.
    5. Trottini, Mario & Muralidhar, Krish & Sarathy, Rathindra, 2011. "Maintaining tail dependence in data shuffling using t copula," Statistics & Probability Letters, Elsevier, vol. 81(3), pages 420-428, March.
    6. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.
    7. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    8. Dominique Guégan, 2009. "A Meta-Distribution for Non-Stationary Samples," CREATES Research Papers 2009-24, Department of Economics and Business Economics, Aarhus University.
    9. Bedendo, Mascia & Campolongo, Francesca & Joossens, Elisabeth & Saita, Francesco, 2010. "Pricing multiasset equity options: How relevant is the dependence function?," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 788-801, April.
    10. Herrera, R. & Eichler, S., 2011. "Extreme dependence with asymmetric thresholds: Evidence for the European Monetary Union," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2916-2930, November.
    11. repec:hal:journl:halshs-00375765 is not listed on IDEAS
    12. Bücher, Axel & Jäschke, Stefan & Wied, Dominik, 2015. "Nonparametric tests for constant tail dependence with an application to energy and finance," Journal of Econometrics, Elsevier, vol. 187(1), pages 154-168.

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