A joint model of failures and credit ratings.

Publication: Scientific journalJournal articlepeer-review

Abstract

We propose a novel framework for credit risk modeling, where default or failure information and rating or expert information are jointly incorporated in the model. These sources of information are modeled as response variables in a multivariate ordinal regression model estimated by a composite likelihood procedure. The proposed framework provides probabilities of default conditional on the rating information observed at the beginning of a predetermined period and is able to account for missing failure or credit rating information. Our approach is, to the best of our knowledge, the first that consistently combines failure-prediction models, where default indicators are used as responses, with so-called shadow rating models, where the responses are estimates of default probabilities usually derived from the leading credit rating agencies. In our empirical analysis we apply the proposed framework to a data set of US firms over the period from 1985 to 2014. Different sets of financial ratios constructed from financial statements and market information are selected as bankruptcy predictors in line with the standard literature in failure-prediction modeling. We find that the joint model of failures and credit ratings outperforms state-of-the-art failure-prediction models and shadow rating approaches in terms of prediction accuracy and discriminatory power
Original languageEnglish
Pages (from-to)1 - 28
JournalJournal of Credit Risk
Volume16
Issue number4
DOIs
Publication statusPublished - 2020

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 101
  • 102022 Software development
  • 101015 Operations research
  • 101018 Statistics
  • 101019 Stochastics
  • 502009 Corporate finance

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