Dynamic modelling of corporate credit ratings and defaults

Laura Vana Gür, Kurt Hornik

Publication: Scientific journalJournal articlepeer-review

Abstract

In this article, we propose a longitudinal multivariate model for binary and ordinal outcomes <br/>to describe the dynamic relationship among firm defaults and credit ratings from various raters. The <br/>latent probability of default is modelled as a dynamic process which contains additive firm-specific <br/>effects, a latent systematic factor representing the business cycle and idiosyncratic observed and <br/>unobserved factors. The joint set-up also facilitates the estimation of a bias for each rater which captures <br/>changes in the rating standards of the rating agencies. Bayesian estimation techniques are employed <br/>to estimate the parameters of interest. Several models are compared based on their out-of-sample <br/>prediction ability and we find that the proposed model outperforms simpler specifications. The joint <br/>framework is illustrated on a sample of publicly traded US corporates which are rated by at least one <br/>of the credit rating agencies S&P, Moody’s and Fitch during the period 1995–2014.
Original languageEnglish
JournalStatistical Modelling
DOIs
Publication statusPublished - 2021

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 101 not use (legacy)
  • 102022 Software development
  • 101015 Operations research
  • 101018 Statistics
  • 101019 Stochastics
  • 502009 Corporate finance

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