The Annals of Applied Statistics

Disentangling and assessing uncertainties in multiperiod corporate default risk predictions

Miao Yuan, Cheng Yong Tang, Yili Hong, and Jian Yang

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Abstract

Measuring the corporate default risk is broadly important in economics and finance. Quantitative methods have been developed to predictively assess future corporate default probabilities. However, as a more difficult yet crucial problem, evaluating the uncertainties associated with the default predictions remains little explored. In this paper, we attempt to fill this blank by developing a procedure for quantifying the level of associated uncertainties upon carefully disentangling multiple contributing sources. Our framework effectively incorporates broad information from historical default data, corporates’ financial records, and macroeconomic conditions by (a) characterizing the default mechanism, and (b) capturing the future dynamics of various features contributing to the default mechanism. Our procedure overcomes the major challenges in this large scale statistical inference problem and makes it practically feasible by using parsimonious models, innovative methods, and modern computational facilities. By predicting the marketwide total number of defaults and assessing the associated uncertainties, our method can also be applied for evaluating the aggregated market credit risk level. Upon analyzing a US market data set, we demonstrate that the level of uncertainties associated with default risk assessments is indeed substantial. More informatively, we also find that the level of uncertainties associated with the default risk predictions is correlated with the level of default risks, indicating potential for new scopes in practical applications including improving the accuracy of default risk assessments.

Article information

Source
Ann. Appl. Stat., Volume 12, Number 4 (2018), 2587-2617.

Dates
Received: June 2016
Revised: April 2018
First available in Project Euclid: 13 November 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1542078057

Digital Object Identifier
doi:10.1214/18-AOAS1170

Mathematical Reviews number (MathSciNet)
MR3875713

Keywords
Competing risks corporate default probability EM algorithm dynamic factor model high-dimensional time series prediction interval

Citation

Yuan, Miao; Tang, Cheng Yong; Hong, Yili; Yang, Jian. Disentangling and assessing uncertainties in multiperiod corporate default risk predictions. Ann. Appl. Stat. 12 (2018), no. 4, 2587--2617. doi:10.1214/18-AOAS1170. https://projecteuclid.org/euclid.aoas/1542078057


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Supplemental materials

  • Supplement to “Disentangling and assessing uncertainties in multiperiod corporate default risk predictions”. Detail of the EM algorithm.