IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v57y2019ics0927538x18301094.html
   My bibliography  Save this article

Estimating multifactor portfolio credit risk: A variance reduction approach

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X18301094
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2018.08.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Paul Glasserman & Wanmo Kang & Perwez Shahabuddin, 2008. "Fast Simulation of Multifactor Portfolio Credit Risk," Operations Research, INFORMS, vol. 56(5), pages 1200-1217, October.
    3. 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.
    4. Rama Cont, 2008. "Frontiers in Quantitative Finance: credit risk and volatility modeling," Post-Print hal-00437588, HAL.
    5. 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 Limited, vol. 4(4), pages 385-398, November.
    6. Glasserman, Paul & Suchintabandid, Sira, 2007. "Correlation expansions for CDO pricing," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1375-1398, May.
    7. X. Burtschell & Jonathan Gregory & Jean-Paul Laurent, 2009. "A Comparative Analysis of CDO Pricing Models under the Factor Copula Framework," Post-Print hal-03676448, HAL.
    8. 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.
    9. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    10. 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.
    11. Paul Glasserman & Jingyi Li, 2005. "Importance Sampling for Portfolio Credit Risk," Management Science, INFORMS, vol. 51(11), pages 1643-1656, November.
    12. 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.
    13. Raymond Kan & Guofu Zhou, 2017. "Modeling non-normality using multivariatet: implications for asset pricing," China Finance Review International, Emerald Group Publishing Limited, vol. 7(1), pages 2-32, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Qihe & Tong, Zhiwei & Yang, Yang, 2021. "Large portfolio losses in a turbulent market," European Journal of Operational Research, Elsevier, vol. 292(2), pages 755-769.
    2. Parrini, Alessandro, 2013. "Importance Sampling for Portfolio Credit Risk in Factor Copula Models," MPRA Paper 103745, University Library of Munich, Germany.
    3. Tim J. Brereton & Dirk P. Kroese & Joshua C. Chan, 2012. "Monte Carlo Methods for Portfolio Credit Risk," ANU Working Papers in Economics and Econometrics 2012-579, Australian National University, College of Business and Economics, School of Economics.
    4. Mohamed A. Ayadi & Hatem Ben-Ameur & Nabil Channouf & Quang Khoi Tran, 2019. "NORTA for portfolio credit risk," Annals of Operations Research, Springer, vol. 281(1), pages 99-119, October.
    5. Cheng-Der Fuh & Chuan-Ju Wang, 2017. "Efficient Exponential Tilting for Portfolio Credit Risk," Papers 1711.03744, arXiv.org, revised Apr 2019.
    6. Tang, Qihe & Tang, Zhaofeng & Yang, Yang, 2019. "Sharp asymptotics for large portfolio losses under extreme risks," European Journal of Operational Research, Elsevier, vol. 276(2), pages 710-722.
    7. Anand Deo & Sandeep Juneja, 2021. "Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator," Operations Research, INFORMS, vol. 69(2), pages 361-379, March.
    8. Anand Deo & Sandeep Juneja, 2019. "Credit Risk: Simple Closed Form Approximate Maximum Likelihood Estimator," Papers 1912.12611, arXiv.org.
    9. Chan, Joshua C.C. & Kroese, Dirk P., 2010. "Efficient estimation of large portfolio loss probabilities in t-copula models," European Journal of Operational Research, Elsevier, vol. 205(2), pages 361-367, September.
    10. Konstantinos Spiliopoulos, 2014. "Systemic Risk and Default Clustering for Large Financial Systems," Papers 1402.5352, arXiv.org, revised Feb 2015.
    11. Sak Halis, 2010. "Increasing the number of inner replications of multifactor portfolio credit risk simulation in the t-copula model," Monte Carlo Methods and Applications, De Gruyter, vol. 16(3-4), pages 361-377, January.
    12. Gerardo Manzo & Antonio Picca, 2020. "The Impact of Sovereign Shocks," Management Science, INFORMS, vol. 66(7), pages 3113-3132, July.
    13. Caballero, Diego & Lucas, André & Schwaab, Bernd & Zhang, Xin, 2020. "Risk endogeneity at the lender/investor-of-last-resort," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 283-297.
    14. Sunggon Kim & Jisu Yu, 2023. "Stratified importance sampling for a Bernoulli mixture model of portfolio credit risk," Annals of Operations Research, Springer, vol. 322(2), pages 819-849, March.
    15. Henry Lam & Clementine Mottet, 2017. "Tail Analysis Without Parametric Models: A Worst-Case Perspective," Operations Research, INFORMS, vol. 65(6), pages 1696-1711, December.
    16. Hu, May & Park, Jason, 2019. "Valuation of collateralized debt obligations: An equilibrium model," Economic Modelling, Elsevier, vol. 82(C), pages 119-135.
    17. Albrecht, Peter, 2005. "Kreditrisiken - Modellierung und Management: Ein Überblick," German Risk and Insurance Review (GRIR), University of Cologne, Department of Risk Management and Insurance, vol. 1(2), pages 22-152.
    18. Rongda Chen & Ze Wang & Lean Yu, 2017. "Importance Sampling for Credit Portfolio Risk with Risk Factors Having t-Copula," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1101-1124, July.
    19. André Lucas & Bernd Schwaab & Xin Zhang, 2017. "Modeling Financial Sector Joint Tail Risk in the Euro Area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 171-191, January.
    20. Guangwu Liu, 2015. "Simulating Risk Contributions of Credit Portfolios," Operations Research, INFORMS, vol. 63(1), pages 104-121, February.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:pacfin:v:57:y:2019:i:c:s0927538x18301094. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/pacfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.