Dynamic modelling of corporate credit ratings and defaults

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