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Multi-period Corporate Default Prediction with Stochastic Covariates

by Darrell Duffie of Stanford University,
Leandro Saita of Stanford University, and
Ke Wang of the University of Tokyo

March 2007

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 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. The out-of-sample predictive performance of the model is an improvement over that of other available models.

JEL Classification: C41, G33, E44.

Keywords: default, bankruptcy, duration analysis, doubly stochastic, distance to default.

Published in: Journal of Financial Economics, Vol. 83, No. 3, (March 2007), pp. 635-665.

Previously titled: Multi-period Corporate Failure Prediction With Stochastic Covariates

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