Importance Sampling for Credit Portfolio Risk with Risk Factors Having t-Copula

Rongda Chen, Ze Wang () and Lean Yu ()
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Rongda Chen: China Academy of Financial Research, Zhejiang University of Finance and Economics, Hangzhou 310018, China2School of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, China3Coordinated Innovation Center of Wealth Management and Quantitative Investment of Zhejiang University of Finance and Economics, Hangzhou 310018, China4Center for Research of Regulation and Policy of Zhejiang Province, Hangzhou 310018, China
Ze Wang: School of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, China

International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 04, 1101-1124

Abstract: This paper proposes an efficient simulation method for calculating credit portfolio risk when risk factors have a heavy-tailed distributions. In modeling heavy tails, its features of return on underlying asset are captured by multivariate t-Copula. Moreover, we develop a three-step importance sampling (IS) procedure in the t-copula credit portfolio risk measure model for further variance reduction. Simultaneously, we apply the Levenberg–Marquardt algorithm associated with nonlinear optimization technique to solve the problem that estimates the mean-shift vector of the systematic risk factors after the probability measure change. Numerical results show that those methods developed in the t-copula model can produce large variance reduction relative to the plain Monte Carlo method, to estimate more accurately tail probability of credit portfolio loss distribution.

Keywords: Credit portfolio risk; t-Copula; Monte Carlo simulation; three-step importance sampling; variance reduction (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219622017500201

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