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Joint distribution of stock indices: Methodological aspects of construction and selection of copula models

Author

Listed:
  • Alexander Knyazev

    (Astrakhan State University, Astrakhan, Russian Federation)

  • Oleg Lepekhin

    (Astrakhan State University, Astrakhan, Russian Federation)

  • Arkady Shemyakin

    (University of St. Thomas, St. Paul, USA)

Abstract

The paper discusses the practical aspects of modeling joint distribution of pairs of national stock indices via copula functions. Parameters of marginal distributions and the association parameter describing the dependence structure are estimated using empirical Bayes method numerically implemented with the help of random walk Metropolis algorithm. A comparison of parametric and semiparametric approaches to copula model construction is performed. The problem of selection of a class of pair copula functions approximating such empirical characteristics of stock indices dependence as Kendall’s concordance, joint empirical cumulative distribution function, and tail behavior.

Suggested Citation

  • Alexander Knyazev & Oleg Lepekhin & Arkady Shemyakin, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 30-53.
    • Handle: RePEc:ris:apltrx:0290
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    References listed on IDEAS

    as
    1. repec:hal:wpspec:info:hdl:2441/c8dmi8nm4pdjkuc9g70969520 is not listed on IDEAS
    2. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    3. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    4. repec:hal:spmain:info:hdl:2441/c8dmi8nm4pdjkuc9g70969520 is not listed on IDEAS
    5. Efim Bronshtein & Elena Prokudina & Anna Gerasimova & Ksenya Dubinskaya, 2011. "Estimation of the interdependence of time series of stocks prices based on copula," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 22-31.
    6. Dean Fantazzini, 2011. "Analysis of multidimensional probability distributions with copula functions. III," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 24(4), pages 100-130.
    7. Marius Hofert & Matthias Scherer, 2011. "CDO pricing with nested Archimedean copulas," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 775-787.
    8. Mandelbrot, Benoit B, 1972. "Correction of an Error in "The Variation of Certain Speculative Prices" (1963)," The Journal of Business, University of Chicago Press, vol. 45(4), pages 542-543, October.
    9. Geert Bekaert & Robert J. Hodrick & Xiaoyan Zhang, 2009. "International Stock Return Comovements," Journal of Finance, American Finance Association, vol. 64(6), pages 2591-2626, December.
    10. Justel, Ana & Peña, Daniel & Zamar, Rubén, 1997. "A multivariate Kolmogorov-Smirnov test of goodness of fit," Statistics & Probability Letters, Elsevier, vol. 35(3), pages 251-259, October.
    11. Joe, Harry, 2005. "Asymptotic efficiency of the two-stage estimation method for copula-based models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 401-419, June.
    12. Kojadinovic, Ivan & Yan, Jun, 2010. "Comparison of three semiparametric methods for estimating dependence parameters in copula models," Insurance: Mathematics and Economics, Elsevier, vol. 47(1), pages 52-63, August.
    13. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    14. You, Leyuan & Daigler, Robert T., 2010. "Is international diversification really beneficial?," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 163-173, January.
    15. T. Ane & L. Ureche-Rangau, 2008. "Time-varying conditional dependence in chinese stock markets," Post-Print hal-00578249, HAL.
    16. 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.
    17. 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.
    18. Coeurdacier, Nicolas & Guibaud, Stéphane, 2011. "International portfolio diversification is better than you think," Journal of International Money and Finance, Elsevier, vol. 30(2), pages 289-308, March.
    19. repec:spo:wpmain:info:hdl:2441/c8dmi8nm4pdjkuc9g70969520 is not listed on IDEAS
    20. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.
    21. Twm Evans & David G. McMillan, 2009. "Financial co-movement and correlation: evidence from 33 international stock market indices," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 1(3), pages 215-241.
    22. Anil Sharma & Neha Seth, 2012. "Literature review of stock market integration: a global perspective," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 4(1), pages 84-122, April.
    23. EnDer Su, 2017. "Measuring and Testing Tail Dependence and Contagion Risk Between Major Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 325-351, August.
    24. Yury Blagoveschensky, 2012. "Basics of copula’s theory," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 26(2), pages 113-130.
    25. Hu, Jian, 2008. "Dependence Structures in Chinese and U.S. Financial Markets -- A Time-varying Conditional Copula Approach," MPRA Paper 11401, University Library of Munich, Germany.
    26. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    27. Dean Fantazzini, 2011. "Analysis of multidimensional probability distributions with copula functions. II," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 23(3), pages 98-132.
    28. Fermanian, Jean-David, 2005. "Goodness-of-fit tests for copulas," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 119-152, July.
    29. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
    30. Puzanova, Natalia, 2011. "A hierarchical Archimedean copula for portfolio credit risk modelling," Discussion Paper Series 2: Banking and Financial Studies 2011,14, Deutsche Bundesbank.
    31. repec:spo:wpecon:info:hdl:2441/c8dmi8nm4pdjkuc9g70969520 is not listed on IDEAS
    32. Thierry Ane & Loredana Ureche-Rangau & Chiraz Labidi-Makni, 2008. "Time-varying conditional dependence in Chinese stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 18(11), pages 895-916.
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    Cited by:

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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