Rating dynamics of fallen angels and their speculative grade-rated peers: Static vs. Dynamic approach

Abstract

This study adopts the survival analysis framework (Allison, P. D. (1984). Event history analysis. Beverly Hills: Sage) to examine issuer-heterogeneity and timeheterogeneity in the rating migrations of fallen angels (FAs) and their speculative grade-rated peers (FA peers). Cox′s hazard model is considered the preeminent method to estimate the probability that an issuer survives in its current rating grade at any point in time t over the time horizon T. In this study, estimation is based on two Cox′s hazard models, including a proportional hazard model (Cox, Journal of Royal Statistical Society Series B (Methodological) 34:187–220, 1972) and a dynamic hazard model. The first model employs a static estimation approach and time-independent covariates, whereas the second uses a dynamic estimation approach and time-dependent covariates. To allow for any dependence among rating states of the same issuer, the marginal event-specific method (Wei et al., Journal of The American Statistical Association 84:1065–1073, 1989) was used to obtain robust variance estimates. For validation purpose, the Brier score (Brier, Monthly Weather Review 78(1):1–3, 1950) and its covariance decomposition (Yates, Organizational Behaviour and Human Performance 30:132–156, 1982) were applied to assess the forecast performance of estimated models in forming time-varying survival probability estimates for issuers out of sample. It was found that FAs and their peers exhibit strong but markedly different dependences on rating history, industry sectors, and macroeconomic conditions. These factors jointly, and in several cases separately, are more important than the current rating in determining future rating changes. A key finding is that past rating behaviors persist even after controlling for the industry sector and the evolution of the macroeconomic environment over the time for which the current rating persists. Switching from a static to a dynamic estimation framework markedly improves the forecast performance of the upgrade model for FAs. The results suggest that rating history provides important diagnostic information and different rating paths require different dynamic migration models.