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December 2018 Disentangling and assessing uncertainties in multiperiod corporate default risk predictions
Miao Yuan, Cheng Yong Tang, Yili Hong, Jian Yang
Ann. Appl. Stat. 12(4): 2587-2617 (December 2018). DOI: 10.1214/18-AOAS1170

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.

Citation

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Miao Yuan. Cheng Yong Tang. Yili Hong. Jian Yang. "Disentangling and assessing uncertainties in multiperiod corporate default risk predictions." Ann. Appl. Stat. 12 (4) 2587 - 2617, December 2018. https://doi.org/10.1214/18-AOAS1170

Information

Received: 1 June 2016; Revised: 1 April 2018; Published: December 2018
First available in Project Euclid: 13 November 2018

zbMATH: 07029467
MathSciNet: MR3875713
Digital Object Identifier: 10.1214/18-AOAS1170

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 4 • December 2018
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