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Goodness-of-fit test for copulas

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  • Panchenko, Valentyn

Abstract

Copulas are often used in finance to characterize the dependence between assets. However, a choice of the functional form for the copula is an open question in the literature. This paper develops a goodness-of-fit test for copulas based on positive definite bilinear forms. The suggested test avoids the use of plug-in estimators that is the common practice in the literature. The test statistics can be consistently computed on the basis of V-estimators even in the case of large dimensions. The test is applied to a dataset of US large cap stocks to assess the performance of the Gaussian copula for the portfolios of assets of various dimension. The Gaussian copula appears to be inadequate to characterize the dependence between assets.

Suggested Citation

  • Panchenko, Valentyn, 2005. "Goodness-of-fit test for copulas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 176-182.
  • Handle: RePEc:eee:phsmap:v:355:y:2005:i:1:p:176-182
    DOI: 10.1016/j.physa.2005.02.081
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    3. Yanqin Fan & Xiaohong Chen & Andrew Patton, 2004. "(IAM Series No 003) Simple Tests for Models of Dependence Between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates," FMG Discussion Papers dp483, Financial Markets Group.
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    Cited by:

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    2. Diermanse, F.L.M. & Geerse, C.P.M., 2012. "Correlation models in flood risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 105(C), pages 64-72.
    3. Steffen Grønneberg & Nils Lid Hjort, 2014. "The Copula Information Criteria," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 436-459, June.
    4. Jean-David Fermanian, 2012. "An overview of the goodness-of-fit test problem for copulas," Papers 1211.4416, arXiv.org.
    5. Kallenberg, Wilbert C.M., 2008. "Modelling dependence," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 127-146, February.
    6. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
    7. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    8. Wang, Zong-Run & Chen, Xiao-Hong & Jin, Yan-Bo & Zhou, Yan-Ju, 2010. "Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH–EVT-Copula model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4918-4928.
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    11. Wanling Huang & Artem Prokhorov, 2014. "A Goodness-of-fit Test for Copulas," Econometric Reviews, Taylor & Francis Journals, vol. 33(7), pages 751-771, October.
    12. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
    13. Yiran Chen & Giray Ökten, 2022. "A goodness-of-fit test for copulas based on the collision test," Statistical Papers, Springer, vol. 63(5), pages 1369-1385, October.
    14. Bruno Rémillard, 2017. "Goodness-of-Fit Tests for Copulas of Multivariate Time Series," Econometrics, MDPI, vol. 5(1), pages 1-23, March.
    15. Shulin Zhang & Qian M. Zhou & Huazhen Lin, 2021. "Goodness-of-fit test of copula functions for semi-parametric univariate time series models," Statistical Papers, Springer, vol. 62(4), pages 1697-1721, August.
    16. Xi, Zhimin & Jing, Rong & Wang, Pingfeng & Hu, Chao, 2014. "A copula-based sampling method for data-driven prognostics," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 72-82.
    17. Noureddine Benlagha, 2014. "Dependence structure between nominal and index-linked bond returns: a bivariate copula and DCC-GARCH approach," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3849-3860, November.
    18. Claudia Klüppelberg & Gabriel Kuhn, 2009. "Copula structure analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 737-753, June.
    19. Sabyasachi Guharay & KC Chang & Jie Xu, 2017. "Robust Estimation of Value-at-Risk through Distribution-Free and Parametric Approaches Using the Joint Severity and Frequency Model: Applications in Financial, Actuarial, and Natural Calamities Domain," Risks, MDPI, vol. 5(3), pages 1-30, July.
    20. Muteba Mwamba, John & Mokwena, Paula, 2013. "International diversification and dependence structure of equity portfolios during market crashes: the Archimedean copula approach," MPRA Paper 64384, University Library of Munich, Germany.
    21. Denecke, Liesa & Müller, Christine H., 2011. "Robust estimators and tests for bivariate copulas based on likelihood depth," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2724-2738, September.
    22. Fantazzini, Dean, 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.
    23. Kallenberg, Wilbert C.M., 2009. "Estimating copula densities, using model selection techniques," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 209-223, October.

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