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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.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/14697680500147853
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

Volume (Year): 5 (2005)
Issue (Month): 5 ()
Pages: 489-501

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Handle: RePEc:taf:quantf:v:5:y:2005:i:5:p:489-501

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References

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  1. Yannick Malevergne & Didier Sornette, 2003. "Testing the Gaussian copula hypothesis for financial assets dependences," Post-Print hal-00520539, HAL.
  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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Citations

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Cited by:
  1. Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," Post-Print halshs-00375765, HAL.
  2. René Garcia & Georges Tsafack, 2009. "Dependence Structure and Extreme Comovements in International Equity and Bond Markets," CIRANO Working Papers 2009s-21, CIRANO.
  3. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00368334, HAL.
  4. 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.
  5. Dominique Guegan & Jing Zhang, 2006. "Change analysis of dynamic copula for measuring dependence in multivariate financial data," Post-Print halshs-00189141, HAL.
  6. Dominique Guegan, 2005. "How can we define the concept of long memory ? An econometric survey," Post-Print halshs-00179343, HAL.
  7. Dominique Guégan, 2009. "A Meta-Distribution for Non-Stationary Samples," CREATES Research Papers 2009-24, School of Economics and Management, University of Aarhus.
  8. 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.
  9. Riadh Aloui & Mohamed Safouane Ben Aissa & Khuong Nguyen Duc, 2010. "Global Financial Crisis, Extreme Interdependences, and Contagion E§ects: The Role of Economic Structure," Working Papers 15, Development and Policies Research Center (DEPOCEN), Vietnam.
  10. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Post-Print halshs-00368334, HAL.
  11. Dominique Guegan, 2008. "Non-stationarity and meta-distribution," Post-Print halshs-00270708, HAL.
  12. Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00375765, HAL.

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