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A simple graphical method to explore tail-dependence in stock-return pairs

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  • Klaus Abberger

Abstract

For a bivariate data set the dependence structure cannot only be measured globally, for example with the Bravais-Pearson correlation coefficient, but the dependence structure can also be analysed locally. In this article the exploration of dependencies in the tails of the bivariate distribution is discussed. For this a graphical method which is called a chi-plot and which was introduced by Fisher and Switzer is used. Examples with simulated data sets illustrate that the chi-plot is suitable for the exploration of dependencies. This graphical method is then used to examine stock-return pairs. The kind of tail-dependence between returns has consequences, for example, for the calculation of the value at risk and should be modelled carefully. The application of the chi-plot to various daily stock-return pairs shows that different dependence structures can be found. This graph can therefore be an interesting aid for the modelling of returns.

Suggested Citation

  • Klaus Abberger, 2005. "A simple graphical method to explore tail-dependence in stock-return pairs," Applied Financial Economics, Taylor & Francis Journals, vol. 15(1), pages 43-51.
  • Handle: RePEc:taf:apfiec:v:15:y:2005:i:1:p:43-51
    DOI: 10.1080/0960310042000280429
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    References listed on IDEAS

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    1. Thierry Roncalli & Gael Riboulet & Ashkan Nikeghbali & Vado Durrleman & Erick Bouy?, 2001. "Copulas: an Open Field for Risk Management," Working Papers wp01-01, Warwick Business School, Finance Group.
    2. Fortin, Ines & Kuzmics, Christoph, 2002. "Tail-Dependence in Stock-Return Pairs," Economics Series 126, Institute for Advanced Studies.
    3. François Longin, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    4. Fisher N. I. & Switzer P., 2001. "Graphical Assessment of Dependence: Is a Picture Worth 100 Tests?," The American Statistician, American Statistical Association, vol. 55, pages 233-239, August.
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    Cited by:

    1. Alex YiHou Huang, 2009. "A value-at-risk approach with kernel estimator," Applied Financial Economics, Taylor & Francis Journals, vol. 19(5), pages 379-395.
    2. Rodríguez, Jhan & Bárdossy, András, 2015. "Entropy measure for the quantification of upper quantile interdependence in multivariate distributions," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 317-324.

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