IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v60y2012i1p78-91.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.1110.1008
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.1110.1008?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
    ---><---

    References listed on IDEAS

    as
    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.
    2. Fan Yu, 2007. "Correlated Defaults In Intensity‐Based Models," Mathematical Finance, Wiley Blackwell, vol. 17(2), pages 155-173, April.
    3. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    4. Robert A. Jarrow & David Lando & Fan Yu, 2008. "Default Risk And Diversification: Theory And Empirical Implications," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 19, pages 455-480, World Scientific Publishing Co. Pte. Ltd..
    5. 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.
    6. Robert A. Jarrow & Fan Yu, 2008. "Counterparty Risk and the Pricing of Defaultable Securities," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 20, pages 481-515, World Scientific Publishing Co. Pte. Ltd..
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guangwu Liu, 2015. "Simulating Risk Contributions of Credit Portfolios," Operations Research, INFORMS, vol. 63(1), pages 104-121, February.
    2. 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.
    3. Adam Metzler & Alexandre Scott, 2020. "Importance Sampling in the Presence of PD-LGD Correlation," Risks, MDPI, vol. 8(1), pages 1-36, March.
    4. 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.
    5. Konstantinos Spiliopoulos, 2014. "Systemic Risk and Default Clustering for Large Financial Systems," Papers 1402.5352, arXiv.org, revised Feb 2015.

    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. Kay Giesecke & Baeho Kim & Shilin Zhu, 2011. "Monte Carlo Algorithms for Default Timing Problems," Management Science, INFORMS, vol. 57(12), pages 2115-2129, December.
    2. Yinghui Dong & Kam C. Yuen & Guojing Wang & Chongfeng Wu, 2016. "A Reduced-Form Model for Correlated Defaults with Regime-Switching Shot Noise Intensities," Methodology and Computing in Applied Probability, Springer, vol. 18(2), pages 459-486, June.
    3. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    4. Gouriéroux, C. & Monfort, A. & Renne, J.P., 2014. "Pricing default events: Surprise, exogeneity and contagion," Journal of Econometrics, Elsevier, vol. 182(2), pages 397-411.
    5. Jia-Wen Gu & Wai-Ki Ching & Tak-Kuen Siu & Harry Zheng, 2013. "On pricing basket credit default swaps," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1845-1854, December.
    6. Jia-Wen Gu & Wai-Ki Ching & Tak-Kuen Siu & Harry Zheng, 2014. "On reduced-form intensity-based model with ‘trigger’ events," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 331-339, March.
    7. Feng-Hui Yu & Wai-Ki Ching & Jia-Wen Gu & Tak-Kuen Siu, 2017. "Interacting default intensity with a hidden Markov process," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 781-794, May.
    8. Azizpour, S & Giesecke, K. & Schwenkler, G., 2018. "Exploring the sources of default clustering," Journal of Financial Economics, Elsevier, vol. 129(1), pages 154-183.
    9. Duffie, Darrell, 2005. "Credit risk modeling with affine processes," Journal of Banking & Finance, Elsevier, vol. 29(11), pages 2751-2802, November.
    10. Kraft, Holger & Steffensen, Mogens, 2009. "Asset allocation with contagion and explicit bankruptcy procedures," Journal of Mathematical Economics, Elsevier, vol. 45(1-2), pages 147-167, January.
    11. Lando, David & Nielsen, Mads Stenbo, 2010. "Correlation in corporate defaults: Contagion or conditional independence?," Journal of Financial Intermediation, Elsevier, vol. 19(3), pages 355-372, July.
    12. repec:wyi:journl:002109 is not listed on IDEAS
    13. Feng-Hui Yu & Jiejun Lu & Jia-Wen Gu & Wai-Ki Ching, 2019. "Modeling Credit Risk with Hidden Markov Default Intensity," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1213-1229, October.
    14. Pagès, Henri, 2013. "Bank monitoring incentives and optimal ABS," Journal of Financial Intermediation, Elsevier, vol. 22(1), pages 30-54.
    15. Alain Monfort & Fulvio Pegoraro & Jean-Paul Renne & Guillaume Roussellet, 2021. "Affine Modeling of Credit Risk, Pricing of Credit Events, and Contagion," Management Science, INFORMS, vol. 67(6), pages 3674-3693, June.
    16. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    17. Wolff, Christian & Bams, Dennis & Pisa, Magdalena, 2015. "Credit risk characteristics of US small business portfolios," CEPR Discussion Papers 10889, C.E.P.R. Discussion Papers.
    18. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    19. Lara Cathcart & Lina El-Jahel, 2006. "Pricing defaultable bonds: a middle-way approach between structural and reduced-form models," Quantitative Finance, Taylor & Francis Journals, vol. 6(3), pages 243-253.
    20. Egloff, Daniel & Leippold, Markus & Vanini, Paolo, 2007. "A simple model of credit contagion," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2475-2492, August.
    21. Duffie, Darrell, 2003. "Intertemporal asset pricing theory," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 11, pages 639-742, Elsevier.

    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:inm:oropre:v:60:y:2012:i:1:p:78-91. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.