"Multi-Period Corporate Default Prediction With Stochastic Covariates"

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and m acroeconomic 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. Variation in a firm's distance to default has a substantially greater effection 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. Theout-of-sample predictive performance of the model is an improvement over that of other available models.

Multi-Period Corporate Default Prediction With Stochastic Covariates

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. Variation in a firm?s distance to default has a substantially greater eect 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 predictive performance of the model is an improvement over that of other available models.

Common Failings: How Corporate Defaults are Correlated

We develop, and apply to data on U.S. corporations from 1979-2004, tests of the standard doubly-stochastic assumption under which firms'default times are correlated only as implied by the correlation of factors determining their default intensities. This assumption is violated in the presence of contagion or "frailty" (unobservable explanatory variables that are correlated across firms). Our tests do not depend on the time-series properties of default intensities. The data do not support the joint hypothesis of well specified default intensities and the doubly-stochastic assumption. There is also some evidence of default clustering in excess of that implied by the doubly-stochastic model with the given intensities.

Multi-Period Corporate Default Prediction With Stochastic Covariates

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of ?rm-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\u27s distance to default (a volatility-adjusted measure of leverage), on the firm\u27s trailing stock return, on trailing S& P 500 returns, and on U.S. interest rates, among other covariates. Variation in a firm\u27s distance to default has a substantially greater effection 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. Theout-of-samplepredictive performance of the model is an improvement over that of other available models.本文フィルはリンク先を参照のこ

Multi-Period Corporate Default Prediction With Stochastic Covariates

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of ?rm-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. Variation in a firm's distance to default has a substantially greater effection 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. Theout-of-samplepredictive performance of the model is an improvement over that of other available models

Frailty Correlated Default

The probability of extreme default losses on portfolios of U.S. corporate debt is much greater than would be estimated under the standard assumption that default correlation arises only from exposure to observable risk factors. At the high confidence levels at which bank loan portfolio and collateralized debt obligation (CDO) default losses are typically measured for economic capital and rating purposes, conventionally based loss estimates are downward biased by a full order of magnitude on test portfolios. Our estimates are based on U.S. public nonfinancial firms between 1979 and 2004. We find strong evidence for the presence of common latent factors, even when controlling for observable factors that provide the most accurate available model of firm-by-firm default probabilities. Copyright (c) 2009 the American Finance Association.