Multiperiod corporate default prediction—A forward intensity approach

Duan, Jin‐Chuan; Sun, Jie; Wang, Tao

doi:10.1016/j.jeconom.2012.05.002

Cited by 271 publications

(136 citation statements)

References 30 publications

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“…Comparing Figures 3 and 4 clearly shows that DTD * is more stable over time than is DTD, confirming an earlier assertion about large sampling errors associated with the estimate for parameter µ. The transformed-data MLE method (including other liabilities) has been previously implemented to obtain DTDs in a default study of US firms by Duan et al (2012). In that study, all exchange-listed firms are put into the data sample, which of course includes financial firms.…”

Section: Other Liabilities and The Transformed-data Mle Estimation Mesupporting

confidence: 68%

“…A scale change, however, inflates asset volatility simply because of those large but artificial moves in the asset values. Duan (2010) and Duan et al (2012) proposed to scale the implied asset value by its corresponding book value. Consider, for example, a firm that has just doubled its asset base without making changes to any other aspect of its operations.…”

Section: The Transformed-data Maximum Likelihood Estimation Methodsmentioning

confidence: 99%

“…To account for other liabilities, a method for their inclusion into the default point was proposed and implemented in Duan (2010) and Duan et al (2012). A haircut is applied to other liabilities much like what the KMV assumption does to the long-term debt.…”

Section: Other Liabilities and The Transformed-data Mle Estimation Mementioning

confidence: 99%

“…The following log-likelihood function used in Duan (2010) and Duan et al (2012) has scaled the asset value by its corresponding book value:…”

Section: Other Liabilities and The Transformed-data Mle Estimation Mementioning

confidence: 99%

“…Because financial firms typically have a large proportion of liabilities that cannot be accounted for by the KMV estimation method (for example, policy obligations of an insurance company), the method tends to inflate asset volatility and cause a distortion to DTD. We argue that the maximum likelihood method proposed first by Duan (1994) and modified later by Duan (2010) and Duan et al (2012) to deal with financial firms is the most appropriate and flexible method for estimating DTD.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Measuring Distance-to-Default for Financial and Non-Financial Firms

Duan

Tao

2012

Global Credit Review

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Section: Other Liabilities and The Transformed-data Mle Estimation Mesupporting

confidence: 68%

Section: The Transformed-data Maximum Likelihood Estimation Methodsmentioning

confidence: 99%

Section: Other Liabilities and The Transformed-data Mle Estimation Mementioning

confidence: 99%

“…The following log-likelihood function used in Duan (2010) and Duan et al (2012) has scaled the asset value by its corresponding book value:…”

Section: Other Liabilities and The Transformed-data Mle Estimation Mementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring Distance-to-Default for Financial and Non-Financial Firms

Duan

Tao

2012

Global Credit Review

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Environmental, social and governance disclosure and default risk

2021

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We investigate whether environmental, social and governance (ESG) disclosure is related to default risk. Using a sample of US nonfinancial institutions from 2006 to 2017, we find that ESG disclosure is positively related to Merton's distance to default and is negatively related to the credit default swap spread, which suggests that firms with a higher ESG disclosure have lower default risk. Our analysis further indicates that the inverse effect of ESG disclosure on default risk is through increased profitability and reduced performance variability and cost of debt. We also document that the negative impact of ESG disclosure on default risk is existent only for mature and older firms. These results are important for all stakeholders of firms, including shareholders and bondholders to consider firm's ESG disclosure in conjunction with life cycle stage before making their investment decisions.

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Forecasting Forward Defaults with the Discrete‐Time Hazard Model

Hwang

Chu

2013

Journal of Forecasting

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For predicting forward default probabilities of firms, the discrete‐time forward hazard model (DFHM) is proposed. We derive maximum likelihood estimates for the parameters in DFHM. To improve its predictive power in practice, we also consider an extension of DFHM by replacing its constant coefficients of firm‐specific predictors with smooth functions of macroeconomic variables. The resulting model is called the discrete‐time varying‐coefficient forward hazard model (DVFHM). Through local maximum likelihood analysis, DVFHM is shown to be a reliable and flexible model for forward default prediction. We use real panel datasets to illustrate these two models. Using an expanding rolling window approach, our empirical results confirm that DVFHM has better and more robust out‐of‐sample performance on forward default prediction than DFHM, in the sense of yielding more accurate predicted numbers of defaults and predicted survival times. Thus DVFHM is a useful alternative for studying forward default losses in portfolios. Copyright © 2013 John Wiley & Sons, Ltd.

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Multiperiod corporate default prediction—A forward intensity approach

Cited by 271 publications

References 30 publications

Measuring Distance-to-Default for Financial and Non-Financial Firms

Measuring Distance-to-Default for Financial and Non-Financial Firms

Environmental, social and governance disclosure and default risk

Forecasting Forward Defaults with the Discrete‐Time Hazard Model

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