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Tail-Dependence in Stock-Return Pairs

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Author Info

  • Fortin, Ines

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna)

  • Kuzmics, Christoph

    (Faculty of Economics and Politics, University of Cambridge)

Abstract

The empirical joint distribution of return-pairs on stock indices displays high tail-dependence in the lower tail and low tail-dependence in the upper tail. The presence of tail-dependence is not compatible with the assumption of (conditional) joint normality. The presence of asymmetric-tail dependence is not compatible with the assumption of a joint student-t distribution. A general test for one dependence structure versus another via the profile-likelihood is described and employed in a bivariate GARCH model, where the joint distribution of the disturbances is split into its marginals and its copula. The copula used is such that it allows for the presence of lower tail-dependence and for asymmetric tail-dependence, and that it encompasses the normal or t-copula. The model is estimated using bivariate data on a set of European stock indices. We find that the assumption of normal or student-t dependence is easily rejected in favour of an asymmetrically tail-dependent distribution.

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File URL: http://www.ihs.ac.at/publications/eco/es-126.pdf
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Bibliographic Info

Paper provided by Institute for Advanced Studies in its series Economics Series with number 126.

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Length: 31 pages
Date of creation: Nov 2002
Date of revision:
Handle: RePEc:ihs:ihsesp:126

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Related research

Keywords: Value-at-Risk; Copula; Non-normal bivariate GARCH; Asymmetric dependence; Profile likelihood-ratio test;

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References

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  1. Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
  2. Rockinger, M. & Jondeau, E., 2001. "Conditional Dependency of Financial Series: An Application of Copulas," Working papers 82, Banque de France.
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  4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-47, August.
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Citations

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Cited by:
  1. Krämer, Walter & van Kampen, Maarten, 2011. "A simple nonparametric test for structural change in joint tail probabilities," Economics Letters, Elsevier, vol. 110(3), pages 245-247, March.
  2. Dobrić, Jadran & Frahm, Gabriel & Schmid, Friedrich, 2007. "Dependence of stock returns in bull and bear markets," Discussion Papers in Statistics and Econometrics 9/07, University of Cologne, Department for Economic and Social Statistics.
  3. Klaus Abberger, 2004. "A simple graphical method to explore tail-dependence in stock-return pairs," CoFE Discussion Paper 04-03, Center of Finance and Econometrics, University of Konstanz.
  4. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," Advances in Data Analysis and Classification, Springer, vol. 5(4), pages 323-340, December.
  5. Filip Žikeš, 2007. "Dependence Structure and Portfolio Diversification on Central European Stock Markets," Working Papers IES 2007/02, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jan 2007.
  6. YiHao Lai, 2008. "Does Asymmetric Dependence Structure Matter? A Value-at-Risk View," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 7(3), pages 249-268, December.
  7. Alessio Sancetta, 2004. "Copula Based Monte Carlo Integration in Financial Problems," Working Papers wp04-02, Warwick Business School, Finance Group.
  8. Alexandra Dias & Paul Embrechts, 2004. "Dynamic copula models for multivariate high-frequency data in finance," Working Papers wpn04-01, Warwick Business School, Finance Group.
  9. Fischer, Matthias J., 2003. "Tailoring copula-based multivariate generalized hyperbolic secant distributions to financial return data: an empirical investigation," Discussion Papers 47/2003, Friedrich-Alexander-University Erlangen-Nuremberg, Chair of Statistics and Econometrics.

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