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Empirical Estimation of Tail Dependence Using Copulas. Application to Asian Markets

  • Cyril Caillault

    (IDHE - Institutions et Dynamiques Historiques de l'Economie - CNRS : UMR8533 - Université Panthéon-Sorbonne - Paris I - Université Paris VIII Vincennes-Saint Denis - Université de Paris X - Nanterre - École normale supérieure de Cachan - ENS Cachan)

  • Dominique Guegan

    ()

    (IDHE - Institutions et Dynamiques Historiques de l'Economie - CNRS : UMR8533 - Université Panthéon-Sorbonne - Paris I - Université Paris VIII Vincennes-Saint Denis - Université de Paris X - Nanterre - École normale supérieure de Cachan - ENS Cachan)

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 "Naïve estimators" as they represent a discretization of Joe's formulae (1997)\nocite{Joe} 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 to each pair of markets.

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Paper provided by HAL in its series Post-Print with number halshs-00180865.

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Date of creation: 2005
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Publication status: Published, Quantitative Finance, 2005, 5, 489 - 501
Handle: RePEc:hal:journl:halshs-00180865
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00180865/en/
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  1. Y. Malevergne & D. Sornette, 2001. "Testing the Gaussian Copula Hypothesis for Financial Assets Dependences," Papers cond-mat/0111310, arXiv.org.
  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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