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Modeling the Loss Distribution

Author

Listed:
  • Sudheer Chava

    () (College of Management, Georgia Institute of Technology, Atlanta, Georgia 30308)

  • Catalina Stefanescu

    () (ESMT European School of Management and Technology, 10178 Berlin, Germany)

  • Stuart Turnbull

    () (Bauer College of Business, University of Houston, Houston, Texas 77204)

Abstract

In this paper, we focus on modeling and predicting the loss distribution for credit risky assets such as bonds and loans. We model the probability of default and the recovery rate given default based on shared covariates. We develop a new class of default models that explicitly accounts for sector specific and regime dependent unobservable heterogeneity in firm characteristics. Based on the analysis of a large default and recovery data set over the horizon 1980-2008, we document that the specification of the default model has a major impact on the predicted loss distribution, whereas the specification of the recovery model is less important. In particular, we find evidence that industry factors and regime dynamics affect the performance of default models, implying that the appropriate choice of default models for loss prediction will depend on the credit cycle and on portfolio characteristics. Finally, we show that default probabilities and recovery rates predicted out of sample are negatively correlated and that the magnitude of the correlation varies with seniority class, industry, and credit cycle. This paper was accepted by Wei Xiong, finance.

Suggested Citation

  • Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
  • Handle: RePEc:inm:ormnsc:v:57:y:2011:i:7:p:1267-1287
    as

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    File URL: http://dx.doi.org/10.1287/mnsc.1110.1345
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    References listed on IDEAS

    as
    1. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
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    9. Das, Sanjiv R. & Hanouna, Paul, 2009. "Implied recovery," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1837-1857, November.
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    12. 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.
    13. Opler, Tim C & Titman, Sheridan, 1994. " Financial Distress and Corporate Performance," Journal of Finance, American Finance Association, vol. 49(3), pages 1015-1040, July.
    14. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters,in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549 World Scientific Publishing Co. Pte. Ltd..
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    Citations

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    Cited by:

    1. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    2. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2012. "Survival Analysis in LGD Modeling," European Financial and Accounting Journal, University of Economics, Prague, vol. 2012(1), pages 6-27.
    3. Manasa Gopal & Markus Pasche, 2012. "Market-based Eurobonds Without Cross-Subsidisation," Global Financial Markets Working Paper Series 2012-37, Friedrich-Schiller-University Jena.
    4. Wu, Yang-Che & Chung, San-Lin, 2010. "Catastrophe risk management with counterparty risk using alternative instruments," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 234-245, October.
    5. Alexander Becker & Alexander F. R. Koivusalo & Rudi Schafer, 2012. "Empirical Evidence for the Structural Recovery Model," Papers 1203.3188, arXiv.org.
    6. 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.
    7. Bruche, Max & González-Aguado, Carlos, 2010. "Recovery rates, default probabilities, and the credit cycle," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 754-764, April.
    8. Campello, Murillo & Gao, Janet, 2017. "Customer concentration and loan contract terms," Journal of Financial Economics, Elsevier, vol. 123(1), pages 108-136.
    9. Anisa Caja & Quentin Guibert & Frédéric Planchet, 2015. "Influence of Economic Factors on the Credit Rating Transitions and Defaults of Credit Insurance Business," Working Papers hal-01178812, HAL.
    10. Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
    11. Xu, Xin, 2013. "Forecasting Bankruptcy with Incomplete Information," MPRA Paper 55024, University Library of Munich, Germany, revised 31 Mar 2014.
    12. Schläfer, Timo & Uhrig-Homburg, Marliese, 2014. "Is recovery risk priced?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 257-270.
    13. Shi, Xiaojun & Tang, Qihe & Yuan, Zhongyi, 2017. "A limit distribution of credit portfolio losses with low default probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 156-167.
    14. Cangemi, Robert R. & Mason, Joseph R. & Pagano, Michael S., 2012. "Options-based structural model estimation of bond recovery rates," Journal of Financial Intermediation, Elsevier, vol. 21(3), pages 473-506.
    15. Ruey-Ching Hwang & Huimin Chung & Jiun-Yi Ku, 2013. "Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market," Journal of Financial Services Research, Springer;Western Finance Association, vol. 43(3), pages 321-341, June.
    16. Hwang, Ruey-Ching & Chung, Huimin & Chu, C.K., 2010. "Predicting issuer credit ratings using a semiparametric method," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 120-137, January.
    17. repec:eee:jbfina:v:79:y:2017:i:c:p:42-56 is not listed on IDEAS
    18. Ruey-Ching Hwang & Huimin Chung & C. K. Chu, 2016. "A Two-Stage Probit Model for Predicting Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 50(3), pages 311-339, December.
    19. Cantia, Catalin & Tunaru, Radu, 2017. "A factor model for joint default probabilities. Pricing of CDS, index swaps and index tranches," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 21-35.
    20. Hwang, Ruey-Ching, 2012. "A varying-coefficient default model," International Journal of Forecasting, Elsevier, vol. 28(3), pages 675-688.

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