Multi-Period Corporate Default Prediction with Stochastic Covariates

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

44 Pages Posted: 23 May 2006  

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

Multiple version iconThere are 2 versions of this paper

Date Written: September 2005

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.

Keywords: default, bankruptcy, duration analysis, doubly stochastic

JEL Classification: C41, G33, E44

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: https://ssrn.com/abstract=903784 or http://dx.doi.org/10.2139/ssrn.903784

James Darrell Duffie

Stanford University - Graduate School of Business ( email )

655 Knight Way
Knight Management Center
Stanford, CA 94305-7298
United States
650-723-1976 (Phone)
650-725-8916 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Leandro Saita (Contact Author)

Independent

No Address Available

Ke Wang

University of Tokyo - Faculty of Economics ( email )

7-3-1 Hongo, Bunkyo-ku
Tokyo 113-0033
Japan

Paper statistics

Downloads
644
Rank
27,838
Abstract Views
2,108