Multi-period corporate default prediction with stochastic covariates

• 成果类型: Article
• 署名作者: Duffie, Darrell; Saita, Leandro; Wang, Ke
• 署名单位: Stanford University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2005.10.011
• 发表日期: 2007
• 页码: 635-665
• 关键词: default; bankruptcy; DURATION ANALYSIS; doubly stochastic; distance to default

摘要

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and macroeconomic covariates. For US Industrial firms, based on over 390,000 firm-months of data spanning 1980 to 2004, the 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 US interest rates. The out-of-sample predictive performance of the model is an improvement over that of other available models. (c) 2006 Elsevier B.V. All rights reserved.