2017/01/17 by S. Baena-Mirabete, Pedro Puig, P. Puig
Business, Management and Accounting · Economics, Econometrics and Finance · #Banking stability, regulation, efficiency #Credit Risk and Financial Regulations #Financial Distress and Bankruptcy Prediction
paper · doi:10.1111/rssa.12267
openalex publication_date 2017/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Summary We propose several parsimonious models for higher order Markov chains, applied to the study of municipal rating migrations in credit risk. In full parameterized Markov chain models, the number of parameters increases very rapidly as the order in the Markov chain grows and this can yield biased estimates when certain sequences of states are rare. For some processes, as in the case of credit ratings, this problem is accentuated because the transitions between distant states are unlikely (persistent transitions). We introduce the short and long persistence models and compare them with the full parameterized Markov chain, achieving a better fit with a lower number of parameters. Furthermore, downgrade momentum effects are found in the rating process, which are consistent with recent empirical findings.