TY  - JOUR
AU  - Duffie,Darrell
AU  - Siata,Leandro
AU  - Wang,Ke
TI  - Multi-Period Corporate Default Prediction With Stochastic Covariates
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 11962
PY  - 2006
Y2  - January 2006
UR  - http://www.nber.org/papers/w11962
L1  - http://www.nber.org/papers/w11962.pdf
N1  - Author contact info:
Darrell Duffie
Graduate School of Business
Stanford University
Stanford, CA  94305-5015
Tel: 650/723-1976
Fax: 650/725-7979
E-Mail: duffie@stanford.edu

Leandro Saita
745 7th Ave.
New York, NY 10019
Tel: 415-518-0461
E-Mail: lsaita@gmail.com

Ke Wang
University of Tokyo
E-Mail: kewang@e.u-tokyo.ac.jp
AB  - 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.
ER  -