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Monte Carlo Methods for Portfolio Credit Risk

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  • Tim J. Brereton
  • Dirk P. Kroese
  • Joshua C. Chan

Abstract

The financial crisis of 2007 – 2009 began with a major failure in credit markets. The causes of this failure stretch far beyond inadequate mathematical modeling (see Donnelly and Embrechts [2010] and Brigo et al. [2009] for detailed discussions from a mathematical finance perspective). Nevertheless, it is clear that some of the more popular models of credit risk were shown to be flawed. Many of these models were and are popular because they are mathematically tractable, allowing easy computation of various risk measures. More realistic (and complex) models come at a significant computational cost, often requiring Monte Carlo methods to estimate quantities of interest.

Suggested Citation

  • 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.
    • Handle: RePEc:acb:cbeeco:2012-579
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    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp579.pdf
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    References listed on IDEAS

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    1. Rama Cont, 2008. "Frontiers in Quantitative Finance: credit risk and volatility modeling," Post-Print hal-00437588, HAL.
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    3. Damiano Brigo & Andrea Pallavicini & Roberto Torresetti, 2009. "Credit models and the crisis, or: how I learned to stop worrying and love the CDOs," Papers 0912.5427, arXiv.org, revised Feb 2010.
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    8. 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.
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    2. 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.
    3. Ma, Yuan-Zhuo & Zhu, Yi-Chen & Li, Hong-Shuang & Nan, Hang & Zhao, Zhen-Zhou & Jin, Xiang-Xiang, 2022. "Adaptive Kriging-based failure probability estimation for multiple responses," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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