Advertisement

Credit Risk Model and Bayesian Improvement for Companies in China

  • Lan LuoEmail author
  • Jian Xiong
  • Qing Zhou
Conference paper
  • 1k Downloads
Part of the Computational Risk Management book series (Comp. Risk Mgmt)

Abstract

The paper focuses on credit risk measuring methods and tries to find suitable model for China’s listed companies’ credit risk measurement. According to the default condition in Chinese listed companies, we apply the factor analysis to the correlative data, and give the default discrimination model by Logistic regression. As the precision of the credit risk default model affect the bank’s risk status and profit directly, the paper uses the Bayesian estimate to improve the predictive power of credit risk default models. Comparing the precision of two models by AUC value and Brier Score, the result shows that the value of AUC of Standard estimator is 0.834 while the same value of Bayesian estimator is 0.870. It shows that the Bayesian estimate has a higher predictive power of precision and stability.

Keywords

Credit Risk Empirical Bayesian Estimate Factor Analysis Logistic Regression 

References

  1. Adkins L, Hill RC (1996) Using prior information in the probit model: empirical risks of Bayes, empirical Bayes, and Stein estimators. In: Berry DA, Chaloner KM, Geweke JK (eds) Bayesian analysis in statistics and econometrics. John Wiley and Sons, New York, pp 79–90Google Scholar
  2. Altman E, Haldeman R, Narayanan P (1977) ZETA analysis: a new model to identify bankruptcy risk of corporations. J Banking Finance (l):29–54Google Scholar
  3. Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78:1–3CrossRefGoogle Scholar
  4. Stein RM (2005) The relationship between default prediction and lending profits: integrating ROC analysis and loan pricing. J Bank Finance 29:1213–1236CrossRefGoogle Scholar
  5. Stein RM, Jordao F (2003) What is a more powerful model worth? Moody s KMV Inc, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.College of Mathematics and Information ScienceGuangzhou UniversityGuangzhouPeople’s Republic of China
  2. 2.The Institute of Mathematical Sciencesthe Chinese University of Hong KongHong KongPeople’s Republic of China
  3. 3.Teradata Information Systems Limited, Guangzhou BrandGuangzhouPeople’s Republic of China

Personalised recommendations