Statistical Modelling 23 (4) (2023), 357–375
Dynamic modelling of corporate credit ratings and defaults
Laura Vana,
Department of Finance,
Accounting and Statistics,
Institute for Statistics and Mathematics,
Vienna University of Economics and Business,
Austria.
e-mail: laura.vana@wu.ac.at
Kurt Hornik,
Department of Finance,
Accounting and Statistics,
Institute for Statistics and Mathematics,
Vienna University of Economics and Business,
Austria.
Abstract:
In this article, we propose a longitudinal multivariate model for binary and ordinal outcomes
to describe the dynamic relationship among firm defaults and credit ratings from various raters. The
latent probability of default is modelled as a dynamic process which contains additive firm-specific
effects, a latent systematic factor representing the business cycle and idiosyncratic observed and
unobserved factors. The joint set-up also facilitates the estimation of a bias for each rater which captures
changes in the rating standards of the rating agencies. Bayesian estimation techniques are employed
to estimate the parameters of interest. Several models are compared based on their out-of-sample
prediction ability and we find that the proposed model outperforms simpler specifications. The joint
framework is illustrated on a sample of publicly traded US corporates which are rated by at least one
of the credit rating agencies S&P, Moody’s and Fitch during the period 1995–2014.
Keywords:
Bayesian inference; credit risk modelling, credit ratings, default probability, longitudinal
model, multivariate ordinal data
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