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Using distortions of copulas to price synthetic CDOs

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  • Crane, Glenis
  • van der Hoek, John

Abstract

This paper demonstrates how to use distorted Gaussian copula functions to produce a heavy tailed portfolio loss distribution in the context of synthetic Collateralized Debt Obligations (CDOs). Distortion functions have not previously been used in this area. Hence, we demonstrate that it is possible to simulate realistic tranche prices by incorporating distorted copula functions within a well established CDO pricing system, such as that of JP Morgan. Furthermore, we only require a single dependence parameter for the entire portfolio rather than one per tranche. Thus, we are providing practitioners with a simpler and more flexible alternative to current CDO pricing methods.

Suggested Citation

  • Crane, Glenis & van der Hoek, John, 2008. "Using distortions of copulas to price synthetic CDOs," Insurance: Mathematics and Economics, Elsevier, vol. 42(3), pages 903-908, June.
    • Handle: RePEc:eee:insuma:v:42:y:2008:i:3:p:903-908
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    References listed on IDEAS

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    1. Patricia Mariela Morillas, 2005. "A method to obtain new copulas from a given one," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 61(2), pages 169-184, April.
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    Cited by:

    1. Di Bernardino Elena & Rullière Didier, 2013. "On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators," Dependence Modeling, De Gruyter, vol. 1(2013), pages 1-36, October.
    2. Tao Peng, 2010. "Portfolio Credit Risk Modelling and CDO Pricing - Analytics and Implied Trees from CDO Tranches," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 8, July-Dece.
    3. Tao Peng, 2010. "Portfolio Credit Risk Modelling and CDO Pricing - Analytics and Implied Trees from CDO Tranches," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2010, January-A.
    4. Jin-Chuan Duan & Weimin Miao, 2016. "Default Correlations and Large-Portfolio Credit Analysis," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 536-546, October.

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