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Estimating multifactor portfolio credit risk: A variance reduction approach

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

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.

Suggested Citation

  • Hsieh, Ming-Hua & Lee, Yi-Hsi & Shyu, So-De & Chiu, Yu-Fen, 2019. "Estimating multifactor portfolio credit risk: A variance reduction approach," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:pacfin:v:57:y:2019:i:c:s0927538x18301094
    DOI: 10.1016/j.pacfin.2018.08.001
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    References listed on IDEAS

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    1. Yan Wang & Shoudong Chen & Xiu Zhang, 2014. "Measuring systemic financial risk and analyzing influential factors: an extreme value approach," China Finance Review International, Emerald Group Publishing, vol. 4(4), pages 385-398, November.
    2. Glasserman, Paul & Suchintabandid, Sira, 2007. "Correlation expansions for CDO pricing," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1375-1398, May.
    3. Andre Lucas & Pieter Klaassen & Peter Spreij & Stefan Straetmans, 2003. "Tail behaviour of credit loss distributions for general latent factor models," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(4), pages 337-357.
    4. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    5. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    6. Lucas, Andre & Klaassen, Pieter & Spreij, Peter & Straetmans, Stefan, 2001. "An analytic approach to credit risk of large corporate bond and loan portfolios," Journal of Banking & Finance, Elsevier, vol. 25(9), pages 1635-1664, September.
    7. Paul Glasserman & Jingyi Li, 2005. "Importance Sampling for Portfolio Credit Risk," Management Science, INFORMS, vol. 51(11), pages 1643-1656, November.
    8. Achal Bassamboo & Sandeep Juneja & Assaf Zeevi, 2008. "Portfolio Credit Risk with Extremal Dependence: Asymptotic Analysis and Efficient Simulation," Operations Research, INFORMS, vol. 56(3), pages 593-606, June.
    9. Paul Glasserman & Wanmo Kang & Perwez Shahabuddin, 2008. "Fast Simulation of Multifactor Portfolio Credit Risk," Operations Research, INFORMS, vol. 56(5), pages 1200-1217, October.
    10. Raymond Kan & Guofu Zhou, 2017. "Modeling non-normality using multivariatet : implications for asset pricing," China Finance Review International, Emerald Group Publishing, vol. 7(1), pages 2-32, February.
    11. Paul Glasserman & Wanmo Kang & Perwez Shahabuddin, 2007. "Large Deviations In Multifactor Portfolio Credit Risk," Mathematical Finance, Wiley Blackwell, vol. 17(3), pages 345-379, July.
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    More about this item

    Keywords

    Portfolio credit risk; Monte Carlo simulation; Variance reduction; Importance sampling; Factor copula models;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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