Dependent Credit Migrations
by Jonathan Wendin of ETH Zürich, and
Abstract: This paper examines latent risk factors in models for migration risk. We employ the standard statistical framework for ordered categorical variables and induce dependence between migrations by means of latent risk factors. By assuming a Markov process for the dynamics of the latent factors, the model can be interpreted as a state space model. The paper contains an empirical study on quarterly migration data from Standard & Poor's for the years 1981-2000, in which the ordered logit model with serially correlated latent factors is fitted by computational Bayesian techniques (Gibbs sampling). Apart from highlighting the usefulness of the Gibbs sampler for statistical inference in models of this kind, the survey in particular investigates the issues of rating-specific factor loadings and heterogeneity among industry sectors, with emphasis on their implications in terms of implied asset correlations.
Keywords: Credit risk, State space models, Multivariate random effects, Gibbs sampling.
Published in: Journal of Credit Risk, Vol. 2, No. 3, (Fall 2006), pp. 87-114.