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Checking for asymmetric default dependence in a credit card portfolio: A copula approach

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  • Crook, Jonathan
  • Moreira, Fernando
Abstract
Traditional credit risk models adopt the linear correlation as a measure of dependence and assume that credit losses are normally-distributed. However some studies have shown that credit losses are seldom normal and the linear correlation does not give accurate assessment for asymmetric data. Therefore it is possible that many credit models tend to misestimate the probability of joint extreme defaults. This paper employs Copula Theory to model the dependence across default rates in a credit card portfolio of a large UK bank and to estimate the likelihood of joint high default rates. Ten copula families are used as candidates to represent the dependence structure. The empirical analysis shows that, when compared to traditional models, estimations based on asymmetric copulas usually yield results closer to the ratio of simultaneous extreme losses observed in the credit card portfolio. Copulas have been applied to evaluate the dependence among corporate debts but this research is the first paper to give evidence of the outperformance of copula estimations in portfolios of consumer loans. Moreover we test some families of copulas that are not typically considered in credit risk studies and find out that three of them are suitable for representing dependence across credit card defaults.

Suggested Citation

  • Crook, Jonathan & Moreira, Fernando, 2011. "Checking for asymmetric default dependence in a credit card portfolio: A copula approach," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 728-742, September.
    • Handle: RePEc:eee:empfin:v:18:y:2011:i:4:p:728-742
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    1. Koziol, Philipp & Schell, Carmen & Eckhardt, Meik, 2015. "Credit risk stress testing and copulas: Is the Gaussian copula better than its reputation?," Discussion Papers 46/2015, Deutsche Bundesbank.
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    4. Ahmed BenSaïda & Houda Litimi, 2021. "Financial contagion across G10 stock markets: A study during major crises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4798-4821, July.
    5. BenSaïda, Ahmed, 2018. "The contagion effect in European sovereign debt markets: A regime-switching vine copula approach," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 153-165.
    6. Ming-Chu Chiang & I-Chun Tsai, 2016. "Ripple effect and contagious effect in the US regional housing markets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 55-82, January.
    7. Ming-Chu Chiang & I-Chun Tsai, 2016. "Ripple effect and contagious effect in the US regional housing markets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 55-82, January.
    8. Changqing Luo & Mengzhen Li & Zisheng Ouyang, 2016. "An empirical study on the correlation structure of credit spreads based on the dynamic and pair copula functions," China Finance Review International, Emerald Group Publishing Limited, vol. 6(3), pages 284-303, August.

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