Multi-Period Corporate Default Prediction with Stochastic Covariates

46 Pages Posted: 24 Apr 2006 Last revised: 9 Sep 2010

See all articles by Darrell Duffie

Darrell Duffie

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

Leandro Saita

Independent

Ke Wang

Board of Governors of the Federal Reserve System

Multiple version iconThere are 2 versions of this paper

Date Written: January 2006

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.

Suggested Citation

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

James Darrell Duffie (Contact Author)

Stanford University - Graduate School of Business ( email )

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National Bureau of Economic Research (NBER)

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Leandro Saita

Independent

No Address Available

Ke Wang

Board of Governors of the Federal Reserve System ( email )

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Washington, DC 20551
United States

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