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Multi-Period Corporate Default Prediction With Stochastic Covariates

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  • Darrel Duffie

    (Graduate School of Business, Stanford University)

  • Leandro Saita

    (Graduate School of Business, Stanford University)

  • Ke Wang

    (Faculty of Economics, 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 e ect 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.

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Bibliographic Info

Paper provided by Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo in its series CARF F-Series with number CARF-F-047.

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Length: 45 pages
Date of creation: Sep 2005
Date of revision:
Handle: RePEc:cfi:fseres:cf047

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