Modeling the Loss Distribution
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.
Volume (Year): 57 (2011)
Issue (Month): 7 (July)
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- 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.
- John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008.
"In Search of Distress Risk,"
Journal of Finance,
American Finance Association, vol. 63(6), pages 2899-2939, December.
- John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2005. "In Searach of Distress Risk," Harvard Institute of Economic Research Working Papers 2081, Harvard - Institute of Economic Research.
- Campbell, John Y. & Hilscher, Jens & Szilagyi, Jan, 2005. "In search of distress risk," Discussion Paper Series 1: Economic Studies 2005,27, Deutsche Bundesbank, Research Centre.
- Szilagyi, Jan & Hilscher, Jens & Campbell, John, 2008. "In Search of Distress Risk," Scholarly Articles 3199070, Harvard University Department of Economics.
- John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2006. "In Search of Distress Risk," NBER Working Papers 12362, National Bureau of Economic Research, Inc.
- Egorov, Alexei V. & Hong, Yongmiao & Li, Haitao, 2006. "Validating forecasts of the joint probability density of bond yields: Can affine models beat random walk?," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 255-284.
- Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
- Daniel M. Covitz & Song Han, 2004. "An empirical analysis of bond recovery rates: exploring a structural view of default," Finance and Economics Discussion Series 2005-10, Board of Governors of the Federal Reserve System (U.S.).
- Merton, Robert C, 1974.
"On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,"
Journal of Finance,
American Finance Association, vol. 29(2), pages 449-470, May.
- Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
- 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.
- Opler, Tim C & Titman, Sheridan, 1994. " Financial Distress and Corporate Performance," Journal of Finance, American Finance Association, vol. 49(3), pages 1015-1040, July.
- Sudheer Chava & Robert A. Jarrow, 2004.
"Bankruptcy Prediction with Industry Effects,"
Review of Finance,
European Finance Association, vol. 8(4), pages 537-569.
- 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.
- Hong, Yongmiao & Li, Haitao & Zhao, Feng, 2007. "Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates," Journal of Econometrics, Elsevier, vol. 141(2), pages 736-776, December.
- Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
- Gilson, Stuart C. & John, Kose & Lang, Larry H. P., 1990. "Troubled debt restructurings*1: An empirical study of private reorganization of firms in default," Journal of Financial Economics, Elsevier, vol. 27(2), pages 315-353, October.
- Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
- Acharya, Viral V & Bharath, Sreedhar T & Srinivasan, Anand, 2003. "Understanding the Recovery Rates on Defaulted Securities," CEPR Discussion Papers 4098, C.E.P.R. Discussion Papers.
- Das, Sanjiv R. & Hanouna, Paul, 2009. "Implied recovery," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1837-1857, November.