@techreport{NBERw11962,
 title = "Multi-Period Corporate Default Prediction With Stochastic Covariates",
 author = "Darrell Duffie and Leandro Siata and Ke Wang",
 institution = "National Bureau of Economic Research",
 type = "Working Paper",
 series = "Working Paper Series",
 number = "11962",
 year = "2006",
 month = "January",
 URL = "http://www.nber.org/papers/w11962",
 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.},
}