Parsimonious higher order Markov models for rating transitions

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)


© 2017 Royal Statistical Society 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.
Original languageEnglish
Pages (from-to)107-131
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Issue number1
Publication statusPublished - 1 Jan 2018


  • Debt issuer
  • Markov chain
  • Municipal ratings
  • Rating momentum
  • Short–long persistence


Dive into the research topics of 'Parsimonious higher order Markov models for rating transitions'. Together they form a unique fingerprint.

Cite this