Abstract
Many articles offer credit transition matrix is modelled by time-homogeneous Markov Chain. However, some articles in overseas provide the existence of time series patterns and relationship with economic situations in ratings changes. No empirical study regarding systematic approaches for time series analysis among articles related ratings changes in Korea, and that is a reason we conduct this article. This article uses time series data are credit ratings migrations matrices of all three ratings agencies in Korea from 1998 to 2020. We find time series patterns in most of ratings changes, especially rating downgrades changes. Additionally, we propose a time series model that is an autoregressive model and estimate cumulative default rates using a model for ratings changes. Our model shows a better estimated result compared to a simple Markov model.
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