Portfolio Credit Risk Model with Extremal Dependence of Defaults and Random Recovery

32 Pages Posted: 23 Jun 2017

See all articles by Jong-June Jeon

Jong-June Jeon

University of Seoul

Sunggon Kim

University of Seoul

Yonghee Lee

University of Seoul

Date Written: June 23, 2017

Abstract

The extremal dependence of defaults, and negative correlation between defaults and their recovery rates, are of major interest in modeling portfolio credit risk. In order to incorporate these two features, we propose a portfolio credit risk model with random recovery rates. The proposed model is an extension of the traditional t-copula model for the credit portfolio with constant recovery rates. A skew-normal copula model is adopted to represent dependent random recovery rates. In our proposed model, various types of dependency between the defaults and their recovery rates are possible, including an inverse relation. We also propose a conditional Monte Carlo simulation algorithm for estimating the probability of a large loss in the model, and an importance sampling version of it. We show that the proposed Monte Carlo simulation algorithm is relatively efficient compared with the plain Monte Carlo simulation. Numerical results are presented to show the performance and efficiency of the algorithms.

Keywords: Portfolio Credit Risk, Random Recovery, Extreme Loss Probability, Importance Sampling, Conditional Monte Carlo Simulation

Suggested Citation

Jeon, Jong-June and Kim, Sunggon and Lee, Yonghee, Portfolio Credit Risk Model with Extremal Dependence of Defaults and Random Recovery (June 23, 2017). Journal of Credit Risk, Vol. 13, No. 2, 2017. Available at SSRN: https://ssrn.com/abstract=2991536

Jong-June Jeon

University of Seoul ( email )

Seoul
Korea, Republic of (South Korea)

Sunggon Kim (Contact Author)

University of Seoul ( email )

Seoul
Korea, Republic of (South Korea)

Yonghee Lee

University of Seoul ( email )

Seoul
Korea, Republic of (South Korea)

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