Multi-Period Corporate Default Prediction With Stochastic Covariates

Darrell Duffie, Leandro Siata and Ke Wang

No 11962, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C41 E44 G33 (search for similar items in EconPapers)
Date: 2006-01
New Economics Papers: this item is included in nep-fin and nep-for
Note: AP CF
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Citations: View citations in EconPapers (8)

Published as Duffie, Darrell, Leandro Saita and Ke Wang. "Multi-Period Corporate Default Prediction with Stochastic Covariates." Journal of Financial Economics 83 (2007): 635-665.

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Journal Article: Multi-period corporate default prediction with stochastic covariates (2007)
Working Paper: Multi-Period Corporate Default Prediction With Stochastic Covariates (2005)
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