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Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

Author

Listed:
  • Shaojie Deng

    (Microsoft, Redmond, Washington 98052)

  • Kay Giesecke

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

  • Tze Leung Lai

    (Department of Statistics, Stanford University, Stanford, California 94305)

Abstract

We provide a sequential Monte Carlo method for estimating rare-event probabilities in dynamic, intensity-based point process models of portfolio credit risk. The method is based on a change of measure and involves a resampling mechanism. We propose resampling weights that lead, under technical conditions, to a logarithmically efficient simulation estimator of the probability of large portfolio losses. A numerical analysis illustrates the features of the method and contrasts it with other rare-event schemes recently developed for portfolio credit risk, including an interacting particle scheme and an importance sampling scheme.

Suggested Citation

  • Shaojie Deng & Kay Giesecke & Tze Leung Lai, 2012. "Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk," Operations Research, INFORMS, vol. 60(1), pages 78-91, February.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:1:p:78-91
    DOI: 10.1287/opre.1110.1008
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    References listed on IDEAS

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    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
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    6. René Carmona & Jean-Pierre Fouque & Douglas Vestal, 2009. "Interacting particle systems for the computation of rare credit portfolio losses," Finance and Stochastics, Springer, vol. 13(4), pages 613-633, September.
    7. Giuseppe Di Graziano & L. C. G. Rogers, 2009. "A Dynamic Approach To The Modeling Of Correlation Credit Derivatives Using Markov Chains," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 45-62.
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    Cited by:

    1. Xiaowei Zhang & Jose Blanchet & Kay Giesecke & Peter W. Glynn, 2015. "Affine Point Processes: Approximation and Efficient Simulation," Mathematics of Operations Research, INFORMS, vol. 40(4), pages 797-819, October.
    2. Guangwu Liu, 2015. "Simulating Risk Contributions of Credit Portfolios," Operations Research, INFORMS, vol. 63(1), pages 104-121, February.
    3. Fred E. Benth & Geir Dahl & Carlo Mannino, 2012. "Computing Optimal Recovery Policies for Financial Markets," Operations Research, INFORMS, vol. 60(6), pages 1373-1388, December.
    4. Konstantinos Spiliopoulos, 2014. "Systemic Risk and Default Clustering for Large Financial Systems," Papers 1402.5352, arXiv.org, revised Feb 2015.
    5. Adam Metzler & Alexandre Scott, 2020. "Importance Sampling in the Presence of PD-LGD Correlation," Risks, MDPI, vol. 8(1), pages 1-36, March.

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