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Copula, Correlated Defaults, and Credit VaR

In: Financial Econometrics, Mathematics and Statistics

Author

Listed:
  • Cheng-Few Lee

    (Rutgers University, Department of Finance and Economics, Rutgers Business School)

  • Hong-Yi Chen

    (National Chengchi University, Department of Finance)

  • John Lee

    (Center for PBBEF Research)

Abstract

Almost every financial institution devotes a lot of attention and energy to credit riskCredit risk. The default correlations of credit assets have a fatal influence on credit risk. How to model default correlationDefault correlation correctly has become a prerequisite for the effective management of credit riskCredit risk. In this chapter, we provide a new approach to estimating future credit risk on target portfolio based on the framework of CreditMetricsTM by J. P. Morgan. However, we adopt the perspective of factor copulaCopula and then bring the principal component analysisPrincipal component analysis concept into factor structure to construct a more appropriate dependence structure among credits. In order to examine the proposed method, we use real market data instead of virtual ones. We also develop a tool for risk analysis that is convenient to use, especially for banking loan businesses. The results indicate that people assume dependence structures are normally distributed, which could lead to underestimated risks. On the other hand, our proposed method captures better features of risks, including conspicuous fat-tail effects, even though the factors appear normally distributed.

Suggested Citation

  • Cheng-Few Lee & Hong-Yi Chen & John Lee, 2019. "Copula, Correlated Defaults, and Credit VaR," Springer Books, in: Financial Econometrics, Mathematics and Statistics, chapter 0, pages 419-438, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4939-9429-8_15
    DOI: 10.1007/978-1-4939-9429-8_15
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