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  E-mail Message: I thought you might be interested in this item at http://www.worldcat.org/oclc/756561833 Title: Multi-Period Corporate Default Prediction With Stochastic Covariates Author: Ke Wang; Darrell Duffie; Leandro Siata; National Bureau of Economic Research Publisher: Cambridge, Mass. : National Bureau of Economic Research, 2006. OCLC:756561833

  
  

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Subjects

• Mathematical and Quantitative Methods -- Duration Analysis
• Financial Economics -- Bankruptcy
• Macroeconomics and Monetary Economics -- Financial Markets and the Macroeconomy

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Multi-Period Corporate Default Prediction With Stochastic Covariates

Author:	Ke Wang; Darrell Duffie; Leandro Siata; National Bureau of Economic Research.
Publisher:	Cambridge, Mass. : National Bureau of Economic Research, 2006.
Series:	NBER working paper series, no. w11962.
Edition/Format:	eBook : Document : EnglishView all editions and formats
Summary:	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.  Read more...
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