Journal of Credit Risk

A joint model of failures and credit ratings

Rainer Hirk, Laura Vana, Kurt Hornik and Stefan Pichler

  • This paper proposes a novel statistical framework for credit risk modeling. The class of multivariate ordinal regression models allows us to include, in addition to binary default observations, ordinal credit ratings or expert opinions from different information sources as response variables.
  • Financial ratios and market information can be used as bankruptcy predictors in the joint modeling framework.
  • The proposed multivariate framework is able to account for missing observations in the response variables and offers probabilities of default (PDs) estimates conditional on the observed ratings at the beginning of each period.
  • An extensive empirical analysis with out-of-firm, out-of-year and rolling windows analyses on a data set of US firms over the period from 1985 to 2014 gives evidence that adding rating information leads to an improvement in the predictive performance and discriminatory power.

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.

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