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Implied recovery

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  • Das, Sanjiv R.
  • Hanouna, Paul

Abstract

In the absence of forward-looking models for recovery rates, market participants tend to use exogenously assumed constant recovery rates in pricing models. We develop a flexible jump-to-default model that uses observables: the stock price and stock volatility in conjunction with credit spreads to identify implied, endogenous, dynamic functions of the recovery rate and default probability. The model in this paper is parsimonious and requires the calibration of only three parameters, enabling the identification of the risk-neutral term structures of forward default probabilities and recovery rates. Empirical application of the model shows that it is consistent with stylized features of recovery rates in the literature. The model is flexible, i.e. it may be used with different state variables, alternate recovery functional forms, and calibrated to multiple debt tranches of the same issuer. The model is robust, i.e. evidences parameter stability over time, is stable to changes in inputs, and provides similar recovery term structures for different functional specifications. Given that the model is easy to understand and calibrate, it may be used to further the development of credit derivatives indexed to recovery rates, such as recovery swaps and digital default swaps, as well as provide recovery rate inputs for the implementation of Basel II.

Suggested Citation

  • Das, Sanjiv R. & Hanouna, Paul, 2009. "Implied recovery," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1837-1857, November.
  • Handle: RePEc:eee:dyncon:v:33:y:2009:i:11:p:1837-1857
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    Cited by:

    1. Lily Y. Liu, 2017. "Estimating Loss Given Default from CDS under Weak Identification," Supervisory Research and Analysis Working Papers RPA 17-1, Federal Reserve Bank of Boston.
    2. Khieu, Hinh D. & Mullineaux, Donald J. & Yi, Ha-Chin, 2012. "The determinants of bank loan recovery rates," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 923-933.
    3. Baglioni, Angelo & Cherubini, Umberto, 2013. "Within and between systemic country risk. Theory and evidence from the sovereign crisis in Europe," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1581-1597.
    4. Zinna, Gabriele, 2013. "Sovereign default risk premia: Evidence from the default swap market," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 15-35.
    5. Marcin Jaskowski & Michael McAleer, 2012. "Estimating implied recovery rates from the term structure of CDS spreads," KIER Working Papers 836, Kyoto University, Institute of Economic Research.
    6. Azusa Takeyama & Nick Constantinou & Dmitri Vinogradov, 2012. "A Framework for Extracting the Probability of Default from Stock Option Prices," IMES Discussion Paper Series 12-E-14, Institute for Monetary and Economic Studies, Bank of Japan.
    7. Arthur M. Berd, 2009. "A Guide to Modeling Credit Term Structures," Papers 0912.4623, arXiv.org, revised Dec 2009.
    8. Konstantinos Spiliopoulos & Richard B. Sowers, 2010. "Recovery Rates in investment-grade pools of credit assets: A large deviations analysis," Papers 1006.2711, arXiv.org, revised Aug 2011.
    9. Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
    10. Kamga, Merlin Kuate & Wilde, Christian, 2017. "Liquidity premia in CDS markets," SAFE Working Paper Series 173, Leibniz Institute for Financial Research SAFE.
    11. Schläfer, Timo & Uhrig-Homburg, Marliese, 2014. "Is recovery risk priced?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 257-270.
    12. Kalimipalli, Madhu & Nayak, Subhankar & Perez, M. Fabricio, 2013. "Dynamic effects of idiosyncratic volatility and liquidity on corporate bond spreads," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2969-2990.
    13. J. Baixauli & Susana Alvarez, 2012. "Implied Severity Density Estimation: An Extended Semiparametric Method to Compute Credit Value at Risk," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 115-129, August.
    14. 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.
    15. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
    16. Liu, Wenchien & Miu, Peter & Chang, Yuanchen & Ozdemir, Bogie, 2012. "Information asymmetry and bank regulation: Can the spread of debt contracts be explained by recovery rates?," Journal of Financial Intermediation, Elsevier, vol. 21(1), pages 123-150.
    17. Brian J. Henderson & Heather Tookes, 2012. "Do Investment Banks' Relationships with Investors Impact Pricing? The Case of Convertible Bond Issues," Management Science, INFORMS, vol. 58(12), pages 2272-2291, December.
    18. Spiliopoulos, Konstantinos & Sowers, Richard B., 2011. "Recovery rates in investment-grade pools of credit assets: A large deviations analysis," Stochastic Processes and their Applications, Elsevier, vol. 121(12), pages 2861-2898.
    19. J. Samuel Baixauli & Susana Alvarez, 2010. "The Role of Market-Implied Severity Modeling for Credit VaR," Annals of Economics and Finance, Society for AEF, vol. 11(2), pages 337-353, November.
    20. Heather Tookes & Brian Henderson, 2010. "Do Investment Banks' Relationships with Investors Impact Pricing? The Case of Convertible Bond Issues," Yale School of Management Working Papers amz2667, Yale School of Management.
    21. Michael Adler & Jeong Song, 2010. "The behavior of emerging market sovereigns' credit default swap premiums and bond yield spreads," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(1), pages 31-58.

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