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Under a new Basel capital accord, bank regulators might use quantitative measures when evaluating the eligibility of internal credit rating systems for the internal ratings based approach. Based on data from Deutsche Bundesbank and using a simulation approach, we find that it is possible to identify strongly inferior rating systems out-of time based on statistics that measure either the quality of ranking borrowers from good to bad, or the quality of individual default probability forecasts. Banks do not significantly improve system quality if they use credit scores instead of ratings, or logistic regression default probability estimates instead of historical data. Banks that are not able to discriminate between high- and low-risk borrowers increase their average capital requirements due to the concavity of the capital requirements function.
We provide insights into determinants of the rating level of 371 issuers which defaulted in the years 1999 to 2003, and into the leader-follower relationship between Moody’s and S&P. The evidence for the rating level suggests that Moody’s assigns lower ratings than S&P for all observed periods before the default event. Furthermore, we observe two-way Granger causal-ity, which signifies information flow between the two rating agencies. Since lagged rating changes influence the magnitude of the agencies’ own rating changes it would appear that the two rating agencies apply a policy of taking a severe downgrade through several mild down-grades. Further, our analysis of rating changes shows that issuers with headquarters in the US are less sharply downgraded than non-US issuers. For rating changes by Moody’s we also find that larger issuers seem to be downgraded less severely than smaller issuers.
This paper compares the accuracy of credit ratings of Moody s and Standard&Poors. Based on 11,428 issuer ratings and 350 defaults in several datasets from 1999 to 2003 a slight advantage for the rating system of Moody s is detected. Compared to former research the robustness of the results is increased by using nonparametric bootstrap approaches. Furthermore, robustness checks are made to control for the impact of Watchlist entries, staleness of ratings and the effect of unsolicited ratings on the results.