Hidden hybrid Markov/semi-Markov chains
Computational Statistics & Data Analysis
Early warning systems for sovereign debt crises: The role of heterogeneity
Computational Statistics & Data Analysis
Estimating discrete Markov models from various incomplete data schemes
Computational Statistics & Data Analysis
Structural model of credit migration
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Different estimators of rating transition matrices have been proposed in the literature but their behaviour has been studied mainly in the context of corporate ratings. The finite-sample bias and variability of three sovereign credit migration estimators is investigated through bootstrap simulations. These are a discrete multinomial estimator and two continuous-time hazard rate methods, one of which neglects time heterogeneity in the rating process whereas the other accounts for it. Panel logit models and spectral analysis are utilized to study the properties of the rating process. The sample consists of Moody's ratings 1981-2004 for 72 industrialized and emerging economies. Hazard rate estimators yield more accurate default probabilities. The time homogeneity assumption leads to underestimating the default probability and greater migration risk is inferred upon relaxing it. There is evidence of duration dependence and downgrade momentum effects in the rating process. These findings have important implications for economic and regulatory capital allocation and for the pricing of credit sensitive instruments.