IDEAS home Printed from
   My bibliography  Save this paper

Tail-Dependence in Stock-Return Pairs


  • Fortin, Ines

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

  • Kuzmics, Christoph

    (Faculty of Economics and Politics, University of Cambridge)


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

    Download full text from publisher

    File URL:
    File Function: First version, 2002
    Download Restriction: no

    References listed on IDEAS

    1. Patton, Andrew J, 2001. "Modelling Time-Varying Exchange Rate Dependence Using the Conditional Copula," University of California at San Diego, Economics Working Paper Series qt01q7j1s2, Department of Economics, UC San Diego.
    2. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    3. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    4. Longin, Francois & Solnik, Bruno, 1995. "Is the correlation in international equity returns constant: 1960-1990?," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 3-26, February.
    5. Michael Rockinger & Eric Jondeau, 2001. "Conditional Dependency of Financial Series: An Application of Copulas," Working Papers hal-00601478, HAL.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. François Longin, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    8. A. Sancetta & Satchell, S.E., 2001. "Bernstein Approximations to the Copula Function and Portfolio Optimization," Cambridge Working Papers in Economics 0105, Faculty of Economics, University of Cambridge.
    9. Angelos Kanas, 1998. "Volatility spillovers across equity markets: European evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 8(3), pages 245-256.
    10. Mico Loretan & William B. English, 2000. "Evaluating "correlation breakdowns" during periods of market volatility," International Finance Discussion Papers 658, Board of Governors of the Federal Reserve System (U.S.).
    11. 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 542-547, August.
    12. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    13. Brian H. Boyer & Michael S. Gibson & Mico Loretan, 1997. "Pitfalls in tests for changes in correlations," International Finance Discussion Papers 597, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Alessio Sancetta, 2004. "Copula Based Monte Carlo Integration in Financial Problems," Working Papers wp04-02, Warwick Business School, Finance Group.
    2. 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.
    3. repec:eee:insuma:v:77:y:2017:i:c:p:49-64 is not listed on IDEAS
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    11. 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.
    12. Rub'en Loaiza-Maya & Michael S. Smith & Worapree Maneesoonthorn, 2017. "Time Series Copulas for Heteroskedastic Data," Papers 1701.07152,
    13. 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.
    14. 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.
    15. Marco Valerio Geraci & Tomas Garbaravicius & David Veredas, 2016. "Short Selling in the Tails," Working Papers ECARES ECARES 2016-30, ULB -- Universite Libre de Bruxelles.
    16. Alexandra Dias & Paul Embrechts, 2004. "Dynamic copula models for multivariate high-frequency data in finance," Working Papers wpn04-01, Warwick Business School, Finance Group.
    17. 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.
    18. 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.
    19. 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.

    More about this item


    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


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ihs:ihsesp:126. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Doris Szoncsitz). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.