This paper examines latent risk factors in models for credit 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 for the time series of migrations. The paper contains an empirical study of 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 investigates in particular the issues of rating-specific factor loadings and heterogeneity among industry sectors, with emphasis on their implications for implied asset correlation values.