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Empirical Bayes Estimation and Comparison of Credit Migration Matrices
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 Title & Authors
Empirical Bayes Estimation and Comparison of Credit Migration Matrices
Kim, Sung-Chul; Park, Ji-Yeon;
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In order to overcome the lack of Korean credit rating migration data, we consider an empirical Bayes procedure to estimate credit rating migration matrices. We derive the posterior probabilities of Korean credit rating transitions by utilizing the Moody`s rating migration data and the credit rating assignments from Korean rating agency as prior information and likelihood, respectively. Metrics based upon the average transition probability are developed to characterize the migration matrices and compare our Bayesian migration matrices with some given matrices. Time series data for the metrics show that our Bayesian matrices are stable, while the matrices based on Korean data have large variation in time. The bootstrap tests demonstrate that the results from the three estimation methods are significantly different and the Bayesian matrices are more affected by Korean data than the Moody`s data. Finally, Monte Carlo simulations for computing the values of a portfolio and its credit VaRs are performed to compare these migration matrices.
Credit migration matrices;empirical Bayes estimation;average transition probability;bootstrap;credit VaR;
 Cited by
표본추출방법을 이용한 기술보증기금의 사후평가 대상 기업의 선정,서정욱;노맹석;남주하;

Journal of the Korean Data Analysis Society, 2011. vol.13. 1, pp.245-255
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