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A Top-Down Approach to Multiname Credit

Author

Listed:
  • Kay Giesecke

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

  • Lisa R. Goldberg

    (MSCI Barra, Berkeley, California 94704)

  • Xiaowei Ding

    (Morgan Stanley, Purchase, New York 10577)

Abstract

A multiname credit derivative is a security that is tied to an underlying portfolio of corporate bonds and has payoffs that depend on the loss due to default in the portfolio. The value of a multiname derivative depends on the distribution of portfolio loss at multiple horizons. Intensity-based models of the loss point process that are specified without reference to the portfolio constituents determine this distribution in terms of few economically meaningful parameters and lead to computationally tractable derivatives valuation problems. However, these models are silent about the portfolio constituent risks. They cannot be used to address applications that are based on the relationship between portfolio and component risks, for example, constituent risk hedging. This paper develops a method that extends these models to the constituents. We use random thinning to decompose the portfolio intensity into a sum of constituent intensities. We show that a thinning process, which allocates the portfolio intensity to constituents, uniquely exists, and is a probabilistic model for the next-to-default. We derive a formula for the constituent default probability in terms of the thinning process and the portfolio intensity, and develop a semi-analytical transform approach to evaluate it. The formula leads to a calibration scheme for the thinning processes and an estimation scheme for constituent hedge sensitivities. An empirical analysis for September 2008 shows that the constituent hedges generated by our method outperform the hedges prescribed by the Gaussian copula model, which is widely used in practice.

Suggested Citation

  • Kay Giesecke & Lisa R. Goldberg & Xiaowei Ding, 2011. "A Top-Down Approach to Multiname Credit," Operations Research, INFORMS, vol. 59(2), pages 283-300, April.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:2:p:283-300
    DOI: 10.1287/opre.1100.0855
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Christian Gouriéroux & Alain Monfort & Sarah Mouabbi & Jean-Paul Renne, 2021. "Disastrous Defaults [Risk premia and term premia in general equilibrium]," Review of Finance, European Finance Association, vol. 25(6), pages 1727-1772.
    2. Jin-Chuan Duan & Weimin Miao, 2016. "Default Correlations and Large-Portfolio Credit Analysis," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 536-546, October.
    3. Nicole El Karoui & Monique Jeanblanc & Ying Jiao, 2015. "Density approach in modelling successive defaults," Post-Print hal-00870492, HAL.
    4. Justin Sirignano & Kay Giesecke, 2019. "Risk Analysis for Large Pools of Loans," Management Science, INFORMS, vol. 65(1), pages 107-121, January.
    5. Tim Leung & Yang Zhou, 2020. "A Top-Down Approach For The Multiple Exercises And Valuation Of Employee Stock Options," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 23(02), pages 1-29, March.
    6. El Karoui, Nicole & Jeanblanc, Monique & Jiao, Ying, 2017. "Dynamics of multivariate default system in random environment," Stochastic Processes and their Applications, Elsevier, vol. 127(12), pages 3943-3965.
    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. Chamizo, Álvaro & Novales, Alfonso, 2021. "Evaluation of market risk associated with hedging a credit derivative portfolio," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 411-430.
    9. Kay Giesecke & Baeho Kim, 2011. "Risk Analysis of Collateralized Debt Obligations," Operations Research, INFORMS, vol. 59(1), pages 32-49, February.
    10. Masahiko Egami & Rusudan Kevkhishvili, 2016. "An Analysis of Simultaneous Company Defaults Using a Shot Noise Process," Discussion papers e-16-001, Graduate School of Economics , Kyoto University.
    11. Lian Tang & Bin Wang & Kai-Nan Xiang, 2016. "Portfolio credit risk with predetermined default orders," Quantitative Finance, Taylor & Francis Journals, vol. 16(1), pages 131-149, January.
    12. Nicole El Karoui & Monique Jeanblanc & Ying Jiao, 2017. "Dynamics of multivariate default system in random environment," Post-Print hal-01205753, HAL.
    13. Kim, Jungmu & Park, Yuen Jung & Ryu, Doojin, 2016. "Hawkes-diffusion process and the conditional probability of defaults in the Eurozone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 301-310.
    14. Kay Giesecke & Baeho Kim, 2011. "Systemic Risk: What Defaults Are Telling Us," Management Science, INFORMS, vol. 57(8), pages 1387-1405, August.
    15. Christian Koziol & Philipp Koziol & Thomas Schön, 2015. "Do correlated defaults matter for CDS premia? An empirical analysis," Review of Derivatives Research, Springer, vol. 18(3), pages 191-224, October.
    16. Ascheberg, Marius & Bick, Björn & Kraft, Holger, 2013. "Hedging structured credit products during the credit crisis: A horse race of 10 models," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1687-1705.
    17. Shao, Hui, 2017. "Decomposing aggregate risk into marginal risks under partial information: A top-down method," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 97-100.
    18. 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.
    19. Suguru Yamanaka, 2019. "Random thinning model with a truncated credit quality vulnerability factor: Application to top-down-type credit risk assessment," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 1-13, September.
    20. Augustin, Patrick & Subrahmanyam, Marti G. & Tang, Dragon Yongjun & Wang, Sarah Qian, 2014. "Credit Default Swaps: A Survey," Foundations and Trends(R) in Finance, now publishers, vol. 9(1-2), pages 1-196, December.
    21. Dianfa Chen & Jun Deng & Jianfen Feng & Bin Zou, 2017. "An Explicit Default Contagion Model and Its Application to Credit Derivatives Pricing," Papers 1706.06285, arXiv.org, revised Aug 2018.
    22. 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.

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