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Some Issues on Criterion for Kolmogorov-Smirnov Test in Credit Rating Model Validation

신용평가모형에서 콜모고로프-스미르노프 검정기준의 문제점

  • Park, Yong-Seok (Research Institute of Applied Statistics, Sungkyunkwan University) ;
  • Hong, Chong-Sun (Department of Statistics, Sungkyunkwan University)
  • 박용석 (성균관대학교 응용통계연구소) ;
  • 홍종선 (성균관대학교선 통계학과)
  • Published : 2008.11.30

Abstract

Kolmogorov-Smirnov(K-S) statistic has been widely used for the model validation of credit rating models. Validation criteria for the K-S statistic is empirically used at the levels of 0.3 or 0.4 which are much larger than the critical values of K-S test statistic. We examine whether these criteria are reasonable and appropriate through the simulations according to various sample sizes, type II error rates, and the ratio of bads among data. The simulation results say that the currently used validation criteria are too lower than values of K-S statistics obtained from any credit rating models in Korea, so that any credit rating models have good discriminatory power. In this work, alternative criteria of K-S statistic are proposed as critical levels under realistic situations of credit rating models.

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