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Tail-Dependence in Stock-Return Pairs

Author

Listed:
  • 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.

Suggested Citation

  • Fortin, Ines & Kuzmics, Christoph, 2002. "Tail-Dependence in Stock-Return Pairs," Economics Series 126, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:126
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    File URL: http://www.ihs.ac.at/publications/eco/es-126.pdf
    File Function: First version, 2002
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    References listed on IDEAS

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    Cited by:

    1. Lai, YiHao & Tseng, Jen-Ching, 2010. "The role of Chinese stock market in global stock markets: A safe haven or a hedge?," International Review of Economics & Finance, Elsevier, vol. 19(2), pages 211-218, April.
    2. Pourkhanali, Armin & Kim, Jong-Min & Tafakori, Laleh & Fard, Farzad Alavi, 2016. "Measuring systemic risk using vine-copula," Economic Modelling, Elsevier, vol. 53(C), pages 63-74.
    3. Knyazev, Alexander & Lepekhin, Oleg & Shemyakin, Arkady, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 42, pages 30-53.
    4. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    5. Alexandra Dias & Paul Embrechts, 2004. "Dynamic copula models for multivariate high-frequency data in finance," Working Papers wpn04-01, Warwick Business School, Finance Group.
    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. 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.
    8. Thomas Fung & Eugene Seneta, 2010. "Modelling and Estimation for Bivariate Financial Returns," International Statistical Review, International Statistical Institute, vol. 78(1), pages 117-133, April.
    9. Alessio Sancetta, 2004. "Copula Based Monte Carlo Integration in Financial Problems," Working Papers wp04-02, Warwick Business School, Finance Group.
    10. Dobric Jadran & Frahm Gabriel & Schmid Friedrich, 2013. "Dependence of Stock Returns in Bull and Bear Markets," Dependence Modeling, De Gruyter Open, vol. 1, pages 94-110, December.
    11. Rub'en Loaiza-Maya & Michael S. Smith & Worapree Maneesoonthorn, 2017. "Time Series Copulas for Heteroskedastic Data," Papers 1701.07152, arXiv.org.
    12. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 323-340, December.
    13. Stübinger, Johannes & Mangold, Benedikt & Krauss, Christopher, 2016. "Statistical arbitrage with vine copulas," FAU Discussion Papers in Economics 11/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    14. 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.
    15. 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.
    16. Dobrić, Jadran & Frahm, Gabriel & Schmid, Friedrich, 2007. "Dependence of stock returns in bull and bear markets," Discussion Papers in Econometrics and Statistics 9/07, University of Cologne, Institute of Econometrics and Statistics.
    17. Marco Valerio Geraci & Tomas Garbaravicius & David Veredas, 2016. "Short Selling in the Tails," Working Papers ECARES ECARES 2016-30, ULB -- Universite Libre de Bruxelles.
    18. 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.
    19. repec:eee:insuma:v:77:y:2017:i:c:p:49-64 is not listed on IDEAS

    More about this item

    Keywords

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

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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