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Some Issues on Criterion for Kolmogorov-Smirnov Test in Credit Rating Model Validation
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 Title & Authors
Some Issues on Criterion for Kolmogorov-Smirnov Test in Credit Rating Model Validation
Park, Yong-Seok; Hong, Chong-Sun;
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 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.
 Keywords
Credit rating model;critical value;discriminatory power;Kolmogorov-smirnov statistic;validation;
 Language
Korean
 Cited by
1.
신용평가모형에서 타당성검증 통계량들의 판단기준,박용석;홍종선;임한승;

Communications for Statistical Applications and Methods, 2009. vol.16. 2, pp.239-347 crossref(new window)
 References
1.
송문섭, 박창순, 이정진 (2003). , 자유아카데미

2.
홍종선, 이창혁, 김지훈 (2008). 범주형 재무자료에 대한 신용평가모형 검증 비교, <한국통계학회논문집>, 15, 615-631

3.
Daniel, W. W. (1990). Applied Nonparametric Statistics, 2nd ed, PWS-Kent, Boston

4.
Engelmann, B., Hayden, E. and Tasche, D. (2003a). Measuring the discriminative power of rating systems, Discussion paper, Series 2: Banking and Financial Supervision

5.
Engelmann, B., Hayden, E. and Tasche, D. (2003b). Testing rating accuracy, Risk, 16, 82-86

6.
Fernandes, J. E. (2005). Corporate credit risk modeling: Quantitative rating system and probability of default estimation. Available from : http://pwp.netcabo.pt/jedfernandes/JEF CorporateCreditRisk.pdf

7.
Hand, D. J. (1994). Assessing classification rules, Journal of applied statistics, 21, 3-16 crossref(new window)

8.
Joseph, M. P. (2005). A PD validation framework for Basel II internal ratings-based systems, Credit Scoring and Credit Control IV

9.
Koh, H. C. (1992). The sensitivity of optimal cutoff points to misclassification costs of Type I and Type II errors in the going concern prediction context, Journal of Business Finance & Accounting, 19, 187-197 crossref(new window)

10.
Tasche, D. (2006). Validation of internal rating systems and PD estimates, eprint arXiv:physics/0606071

11.
Thomas, L. C., Edelman, D. B. and Crook, J. B. (2002). Credit Scoring and Its Applications, Society for Industrial Mathematics, Philadelphia

12.
Wilkie, A. D. (1992). Measures for comparing scoring systems, Credit Scoring and Credit Control, Eds. Thomas, L. C., Crook, J. N and Edelman, D. B. Oxford: Carendon, 123-138

13.
Wilkie, A. D. (2004). Measures for comparing scoring systems, In Readings in Credit Scoring-recent developments, advances, and aims, Eds. Thomas, L. C., Crook, J. N, and Edelman, D. B. Oxford finance