Application of Vine Copulas to Credit Portfolio Risk Modeling
AbstractIn 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
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Geidosch, M.; Fischer, M. Application of Vine Copulas to Credit Portfolio Risk Modeling. J. Risk Financial Manag. 2016, 9, 4.
Geidosch M, Fischer M. Application of Vine Copulas to Credit Portfolio Risk Modeling. Journal of Risk and Financial Management. 2016; 9(2):4.Chicago/Turabian Style
Geidosch, Marco; Fischer, Matthias. 2016. "Application of Vine Copulas to Credit Portfolio Risk Modeling." J. Risk Financial Manag. 9, no. 2: 4.
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