Federal Deposit InsuranceCorporation• Center for Financial Researchh
Sanjiv R. Das
Darrell Duffie
Nikunj Kapadia
Risk-Based Capital Standards,
Deposit Insurance and Procyclicality
Risk-Based Capital Standards,
Deposit Insurance and Procyclicality
FDIC Center for Financial Research
Working Paper
No. 2006-05
kkk
An Empirical
An Empirical Analysis
State-
Efraim Benmel Efraim Benmelech May, 2005
June 20
May , 2005 Asset S2005-14
September 2005
Federal Deposit InsuranceCorporation• Center for Financial Researchh
Sanjiv R. Das
Darrell Duffie
Nikunj Kapadia
Risk-Based Capital Standards,
Deposit Insurance and Procyclicality
Risk-Based Capital Standards,
Deposit Insurance and Procyclicality
FDIC Center for Financial Research
Working Paper
No. 2006-05
kkk
An Empirical
An Empirical Analysis
State-
Efraim Benmel Efraim Benmelech May, 2005
June 20
May , 2005 Asset S2005-14
September 2005
Federal Deposit InsuranceCorporation• Center for Financial Researchh
Multi-Period Corporate Default Prediction
With Stochastic Covariates∗
Darrell Duffie, Leandro Saita, and Ke Wang
First Version: August 30, 2003
Current Version: September 1, 2005
Abstract
We provide maximum likelihood estimators of term structures of
conditional probabilities of corporate default, incorporating the dy-
namics of firm-specific and macroeconomic covariates. For U.S. In-
dustrial firms, based on over 390,000 firm-months of data spanning
1980 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 trail-
ing stock return, on trailing S& P 500 returns, and on U.S. interest
rates, among other covariates. Variation in a firm’s distance to de-
fault has a substantially greater effect 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 predic-
tive performance of the model is an improvement over that of other
available models.
Keywords: default, bankruptcy, duration analysis, doubly stochastic,
distance to default JEL classification: C41, G33, E44
∗We are grateful for financial support from Moodys Corporation and from The Federal
Deposit Insurance Corporation. We have benefited from remarks from Takeshi Amemiya,
Susan Athey, Richard Cantor, Brad Effron, Jeremy Fox, Peyron Law, Aprajit Mahajan,
and Ilya Strebulaev. This paper extends earlier work by two of the authors under the title
“Multi-Period Corporate Failure Prediction With Stochastic Covariates.” Duffie and Saita
are at The Graduate School of Business, Stanford University. Wang is at The Faculty of
Economics, The University of Tokyo.
With Stochastic Covariates∗
Darrell Duffie, Leandro Saita, and Ke Wang
First Version: August 30, 2003
Current Version: September 1, 2005
Abstract
We provide maximum likelihood estimators of term structures of
conditional probabilities of corporate default, incorporating the dy-
namics of firm-specific and macroeconomic covariates. For U.S. In-
dustrial firms, based on over 390,000 firm-months of data spanning
1980 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 trail-
ing stock return, on trailing S& P 500 returns, and on U.S. interest
rates, among other covariates. Variation in a firm’s distance to de-
fault has a substantially greater effect 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 predic-
tive performance of the model is an improvement over that of other
available models.
Keywords: default, bankruptcy, duration analysis, doubly stochastic,
distance to default JEL classification: C41, G33, E44
∗We are grateful for financial support from Moodys Corporation and from The Federal
Deposit Insurance Corporation. We have benefited from remarks from Takeshi Amemiya,
Susan Athey, Richard Cantor, Brad Effron, Jeremy Fox, Peyron Law, Aprajit Mahajan,
and Ilya Strebulaev. This paper extends earlier work by two of the authors under the title
“Multi-Period Corporate Failure Prediction With Stochastic Covariates.” Duffie and Saita
are at The Graduate School of Business, Stanford University. Wang is at The Faculty of
Economics, The University of Tokyo.