Parsimonious Higher Order Markov Models for Rating Transitions
针对信用评级迁移中高阶马尔可夫链参数过多导致估计偏差的问题，提出短持久和长持久两种简约模型，用更少参数实现更好拟合，并发现评级下调的动量效应。
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.