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

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  • Abberger, Klaus

Abstract

For a bivariate data set the dependence structure can not only be measured globally, for example with the Bravais-Pearson correlation coefficient, but the dependence structure can also be analyzed 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 chi-plot and which was introduced by Fisher and Switzer (1985, 2001) 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

  • Abberger, Klaus, 2004. "A simple graphical method to explore tail-dependence in stock-return pairs," CoFE Discussion Papers 04/03, University of Konstanz, Center of Finance and Econometrics (CoFE).
    • Handle: RePEc:zbw:cofedp:0403
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    References listed on IDEAS

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    1. Ines Fortin & Christoph Kuzmics, 2002. "Tail‐dependence in stock‐return pairs," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 11(2), pages 89-107, April.
    2. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    3. 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. M. Mehdi Bateni & Mario L. V. Martina & ·Marcello Arosio, 2022. "Multivariate return period for different types of flooding in city of Monza, Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 811-823, October.
    2. Amine Ben Daoued & Nassima Mouhous-Voyneau & Yasser Hamdi & Claire-Marie Duluc & Philippe Sergent, 2020. "Modelling coincidence and dependence of flood hazard phenomena in a Probabilistic Flood Hazard Assessment (PFHA) framework: case study in Le Havre," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(3), pages 1059-1088, February.
    3. 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.
    4. Alex YiHou Huang, 2009. "A value-at-risk approach with kernel estimator," Applied Financial Economics, Taylor & Francis Journals, vol. 19(5), pages 379-395.

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