Estimating multifactor portfolio credit risk: A variance reduction approach

Ming-Hua Hsieh, Yi-Hsi Lee, So-De Shyu and Yu-Fen Chiu

Pacific-Basin Finance Journal, 2019, vol. 57, issue C

Abstract: The importance of credit markets in China and Asia Pacific has been increased significantly in the past decade and international regulation demands high standard in credit risk quantification for financial institutions. Computation for credit risk measures is a challenge problem. Hence this study aims to develop a fast Monte Carlo approach of estimating portfolio credit risk. The method could be applied to estimate the probability of large losses and the expected excess loss above a large threshold of a credit portfolio, which has a dependence structure driven by general factor copula models. Except for the assumption that a global common factor driving the default events of all defaultable obligors exists, the study does not impose any restrictions on the composition of the portfolio (e.g., stochastic recovery rates). Hence, this method can therefore be applied to a wide range of credit risk models. Numerical results demonstrate that the proposed method is efficient under general market conditions. In the high market impact condition, in credit contagion or market collapse environments, the proposed method is even more efficient.

Keywords: Portfolio credit risk; Monte Carlo simulation; Variance reduction; Importance sampling; Factor copula models (search for similar items in EconPapers)
JEL-codes: G01 G13 G20 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:57:y:2019:i:c:s0927538x18301094

DOI: 10.1016/j.pacfin.2018.08.001

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