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The Term Structure of Expected Recovery Rates

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  • Doshi, Hitesh
  • Elkamhi, Redouane
  • Ornthanalai, Chayawat

Abstract

There is widespread agreement that corporate debts’ recovery rates are time varying, but empirical work in this area is limited. We show that the joint information from the term structure of senior and subordinate credit default swaps can identify the level and the dynamics of recovery rates. We estimate a reduced-form no-arbitrage model on 46 firms across different industries. We find that the term structure of expected recovery rates is, on average, downward sloping. However, an inversion occurs during the 2008 crisis, suggesting the market expects higher recoveries conditional on short-term survival. The inversion is more pronounced for firms in distressed industries.

Suggested Citation

  • Doshi, Hitesh & Elkamhi, Redouane & Ornthanalai, Chayawat, 2018. "The Term Structure of Expected Recovery Rates," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(6), pages 2619-2661, December.
    • Handle: RePEc:cup:jfinqa:v:53:y:2018:i:06:p:2619-2661_00
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    Cited by:

    1. Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
    2. Jean‐François Bégin & Mathieu Boudreault & Mathieu Thériault, 2024. "Leveraging prices from credit and equity option markets for portfolio risk management," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(1), pages 122-147, January.
    3. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
    4. Barbagli, Matteo & François, Pascal & Gauthier, Geneviève & Vrins, Frédéric, 2024. "The role of CDS spreads in explaining bond recovery rates," LIDAM Discussion Papers LFIN 2024002, Université catholique de Louvain, Louvain Finance (LFIN).
    5. Andrea Gamba & Alessio Saretto, 2020. "Growth Options and Credit Risk," Management Science, INFORMS, vol. 66(9), pages 4269-4291, September.
    6. Bo Young Chang & Greg Orosi, 2020. "A Simple Method for Extracting the Probability of Default from American Put Option Prices," Staff Working Papers 20-15, Bank of Canada.
    7. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.
    8. Jansen, Jeroen & Das, Sanjiv R. & Fabozzi, Frank J., 2018. "Local volatility and the recovery rate of credit default swaps," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 1-29.
    9. 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.
    10. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
    11. Bo Young Chang & Greg Orosi, 2020. "A simple method for extracting the probability of default from American put option prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(10), pages 1535-1547, October.

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