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Multi-Period Corporate Default Prediction with Stochastic Covariates


Darrell Duffie


Stanford University - Graduate School of Business

Leandro Saita


Stanford Graduate School of Business

Ke Wang


University of Tokyo - Faculty of Economics

January 2006

NBER Working Paper No. w11962

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 1979 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. Distance to default is the most influential covariate. 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: 46

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Date posted: April 24, 2006  

Suggested Citation

Duffie, Darrell , Saita, Leandro and Wang, Ke, Multi-Period Corporate Default Prediction with Stochastic Covariates (January 2006). NBER Working Paper No. w11962. Available at SSRN: http://ssrn.com/abstract=877467

Contact Information

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

Leandro Saita
Stanford Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
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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