Predicting issuer credit ratings using a semiparametric method

Ruey Ching Hwang*, Hui-Min Chung, C. K. Chu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


This paper proposes a prediction method based on an ordered semiparametric probit model for credit risk forecast. The proposed prediction model is constructed by replacing the linear regression function in the usual ordered probit model with a semiparametric function, thus it allows for more flexible choice of regression function. The unknown parameters in the proposed prediction model are estimated by maximizing a local (weighted) log-likelihood function, and the resulting estimators are analyzed through their asymptotic biases and variances. A real data example for predicting issuer credit ratings is used to illustrate the proposed prediction method. The empirical result confirms that the new model compares favorably with the usual ordered probit model.

Original languageEnglish
Pages (from-to)120-137
Number of pages18
JournalJournal of Empirical Finance
Issue number1
StatePublished - 1 Jan 2010


  • Industry effect
  • Issuer credit rating
  • Market-driven variable
  • Ordered linear probit model
  • Ordered semiparametric probit model


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