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The t copula with Multiple Parameters of Degrees of Freedom: Bivariate Characteristics and Application to Risk Management

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  • Xiaolin Luo
  • Pavel V. Shevchenko

Abstract

The t copula is often used in risk management as it allows for modelling tail dependence between risks and it is simple to simulate and calibrate. However, the use of a standard t copula is often criticized due to its restriction of having a single parameter for the degrees of freedom (dof) that may limit its capability to model the tail dependence structure in a multivariate case. To overcome this problem, grouped t copula was proposed recently, where risks are grouped a priori in such a way that each group has a standard t copula with its specific dof parameter. In this paper we propose the use of a grouped t copula, where each group consists of one risk factor only, so that a priori grouping is not required. The copula characteristics in the bivariate case are studied. We explain simulation and calibration procedures, including a simulation study on finite sample properties of the maximum likelihood estimators and Kendall's tau approximation. This new copula can be significantly different from the standard t copula in terms of risk measures such as tail dependence, value at risk and expected shortfall. Keywords: grouped t copula, tail dependence, risk management.

Suggested Citation

  • Xiaolin Luo & Pavel V. Shevchenko, 2007. "The t copula with Multiple Parameters of Degrees of Freedom: Bivariate Characteristics and Application to Risk Management," Papers 0710.3959, arXiv.org, revised Feb 2010.
    • Handle: RePEc:arx:papers:0710.3959
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    References listed on IDEAS

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    1. Fang, Hong-Bin & Fang, Kai-Tai & Kotz, Samuel, 2002. "The Meta-elliptical Distributions with Given Marginals," Journal of Multivariate Analysis, Elsevier, vol. 82(1), pages 1-16, July.
    2. Banachewicz, Konrad & van der Vaart, Aad, 2008. "Tail dependence of skewed grouped t-distributions," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2388-2399, October.
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    Cited by:

    1. Cordelia Rudolph & Uwe Schmock, 2020. "Multivariate Collective Risk Model: Dependent Claim Numbers and Panjer’s Recursion," Risks, MDPI, vol. 8(2), pages 1-31, May.
    2. Fuchs, Sebastian & Tschimpke, Marco, 2024. "A novel positive dependence property and its impact on a popular class of concordance measures," Journal of Multivariate Analysis, Elsevier, vol. 200(C).
    3. Penikas, Henry, 2014. "Investment portfolio risk modelling based on hierarchical copulas," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 18-38.
    4. Hua, Lei & Joe, Harry, 2017. "Multivariate dependence modeling based on comonotonic factors," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 317-333.
    5. Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.
    6. Fermanian, Jean-David & Wegkamp, Marten H., 2012. "Time-dependent copulas," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 19-29.

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