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J. Risk Financial Manag. 2016, 9(2), 4; doi:10.3390/jrfm9020004

Application of Vine Copulas to Credit Portfolio Risk Modeling

1
UniCredit Bank AG, Munich, Germany
2
Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Jingzhi Huang
Received: 15 October 2015 / Revised: 28 February 2016 / Accepted: 18 April 2016 / Published: 7 June 2016
(This article belongs to the Special Issue Credit Risk)
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Abstract

In this paper, we demonstrate the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. We show statistical and economic implications of replacing conventional copulas by vine copulas for a subportfolio of the Euro Stoxx 50 and the S&P 500 companies, respectively. Our study includes D-vines and R-vines where the bivariate building blocks are chosen from the Gaussian, the t and the Clayton family. Our findings are (i) the conventional Gauss copula is deficient in modeling the dependence structure of a credit portfolio and economic capital is seriously underestimated; (ii) D-vine structures offer a better statistical fit to the data than classical copulas, but underestimate economic capital compared to R-vines; (iii) when mixing different copula families in an R-vine structure, the best statistical fit to the data can be achieved which corresponds to the most reliable estimate for economic capital. View Full-Text
Keywords: pair-copula constructions; vine copulas; Archimedean and elliptical copulas; credit portfolio risk; economic capital; R-vine; D-vine pair-copula constructions; vine copulas; Archimedean and elliptical copulas; credit portfolio risk; economic capital; R-vine; D-vine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Geidosch, M.; Fischer, M. Application of Vine Copulas to Credit Portfolio Risk Modeling. J. Risk Financial Manag. 2016, 9, 4.

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