Abstract

http://ssrn.com/abstract=903784
 
 

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Multi-Period Corporate Default Prediction with Stochastic Covariates


Darrell Duffie


Stanford University - Graduate School of Business; National Bureau of Economic Research (NBER)

Leandro Saita


Independent

Ke Wang


University of Tokyo - Faculty of Economics

September 2005

FDIC Center For Financial Research Working Paper No. 2006-05

Abstract:     
We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and macroeconomic covariates. For U.S. Industrial firms, based on over 390,000 firm-months of data spanning 1980 to 2004, the level and shape of the estimated term structure of conditional future default probabilities depends on a firm's distance to default (a volatility-adjusted measure of leverage), on the firm's trailing stock return, on trailing S&P 500 returns, and on U.S. interest rates, among other covariates. Variation in a firm's distance to default has a substantially greater effect on the term structure of future default hazard rates than does a comparatively significant change in any of the other covariates. Default intensities are estimated to be lower with higher short-term interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models.

Number of Pages in PDF File: 44

Keywords: default, bankruptcy, duration analysis, doubly stochastic

JEL Classification: C41, G33, E44

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Date posted: May 23, 2006  

Suggested Citation

Duffie, Darrell and Saita, Leandro and Wang, Ke, Multi-Period Corporate Default Prediction with Stochastic Covariates (September 2005). FDIC Center For Financial Research Working Paper No. 2006-05. Available at SSRN: http://ssrn.com/abstract=903784 or http://dx.doi.org/10.2139/ssrn.903784

Contact Information

James Darrell Duffie
Stanford University - Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
United States
650-723-1976 (Phone)
650-725-7979 (Fax)

National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Leandro Saita (Contact Author)
Independent
No Address Available
United States
Ke Wang
University of Tokyo - Faculty of Economics ( email )
7-3-1 Hongo, Bunkyo-ku
Tokyo 113-0033
Japan
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