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Bond and CDS Pricing via the Stochastic Recovery Black-Cox Model

Author

Listed:
  • Albert Cohen

    (Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA)

  • Nick Costanzino

    (Quantitative Analytics, Barclays Capital, 745 7th Ave, New York, NY 10019, USA)

Abstract

Building on recent work incorporating recovery risk into structural models by Cohen & Costanzino (2015), we consider the Black-Cox model with an added recovery risk driver. The recovery risk driver arises naturally in the context of imperfect information implicit in the structural framework. This leads to a two-factor structural model we call the Stochastic Recovery Black-Cox model, whereby the asset risk driver A t defines the default trigger and the recovery risk driver R t defines the amount recovered in the event of default. We then price zero-coupon bonds and credit default swaps under the Stochastic Recovery Black-Cox model. Finally, we compare our results with the classic Black-Cox model, give explicit expressions for the recovery risk premium in the Stochastic Recovery Black-Cox model, and detail how the introduction of separate but correlated risk drivers leads to a decoupling of the default and recovery risk premiums in the credit spread. We conclude this work by computing the effect of adding coupons that are paid continuously until default, and price perpetual (consol bonds) in our two-factor firm value model, extending calculations in the seminal paper by Leland (1994).

Suggested Citation

  • Albert Cohen & Nick Costanzino, 2017. "Bond and CDS Pricing via the Stochastic Recovery Black-Cox Model," Risks, MDPI, vol. 5(2), pages 1-28, April.
  • Handle: RePEc:gam:jrisks:v:5:y:2017:i:2:p:26-:d:96172
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    References listed on IDEAS

    as
    1. Leland, Hayne E, 1994. "Corporate Debt Value, Bond Covenants, and Optimal Capital Structure," Journal of Finance, American Finance Association, vol. 49(4), pages 1213-1252, September.
    2. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    3. Young Ho Eom, 2004. "Structural Models of Corporate Bond Pricing: An Empirical Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 499-544.
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    5. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
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    7. Joseph P. Ogden, 1987. "Determinants Of The Ratings And Yields On Corporate Bonds: Tests Of The Contingent Claims Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 10(4), pages 329-340, December.
    8. Jing-Zhi Huang & Ming Huang, 2012. "How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 2(2), pages 153-202.
    9. Jon Frye, 2000. "Collateral damage detected," Emerging Issues, Federal Reserve Bank of Chicago, issue Sep.
    10. Salah Amraoui & Laurent Cousot & Sebastien Hitier & Jean-Paul Laurent, 2012. "Pricing CDOs with state-dependent stochastic recovery rates," Quantitative Finance, Taylor & Francis Journals, vol. 12(8), pages 1219-1240, February.
    11. Schläfer, Timo & Uhrig-Homburg, Marliese, 2014. "Is recovery risk priced?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 257-270.
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    Cited by:

    1. Albert Cohen & Nick Costanzino, 2017. "A General Framework for Incorporating Stochastic Recovery in Structural Models of Credit Risk," Risks, MDPI, vol. 5(4), pages 1-19, December.
    2. A. Itkin & V. Shcherbakov & A. Veygman, 2017. "Influence of jump-at-default in IR and FX on Quanto CDS prices," Papers 1711.07133, arXiv.org.
    3. Masahiko Egami & Rusudan Kevkhishvili, 2020. "Post-Last Exit Time Process and its Application to Loss-Given-Default Distribution," Papers 2009.00868, arXiv.org, revised Mar 2024.
    4. Anton van Dyk & Gary van Vuuren, 2023. "Measurement and Calibration of Regulatory Credit Risk Asset Correlations," JRFM, MDPI, vol. 16(9), pages 1-19, September.
    5. Albert Cohen, 2018. "Editorial: A Celebration of the Ties That Bind Us: Connections between Actuarial Science and Mathematical Finance," Risks, MDPI, vol. 6(1), pages 1-3, January.

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