Multi-Period Corporate Default Prediction With Stochastic Covariates

Darrell Duffie, Leandro Saita and Ke Wang
Additional contact information
Leandro Saita: Graduate School of Business, Stanford University
Ke Wang: Faculty of Economics, University of Tokyo

No CARF-F-047, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

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. Variation in a firm?s distance to default has a substantially greater eect 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.

Pages: 45 pages
Date: 2005-09
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Citations: View citations in EconPapers (13)

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https://www.carf.e.u-tokyo.ac.jp/old/pdf/workingpaper/fseries/48.pdf (application/pdf)

Related works:
Journal Article: Multi-period corporate default prediction with stochastic covariates (2007)
Working Paper: Multi-Period Corporate Default Prediction With Stochastic Covariates (2006)
Working Paper: Multi-Period Corporate Default Prediction With Stochastic Covariates (2005)
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