Predicting issuer credit ratings using generalized estimating equations

Abstract

The dynamic ordered probit model (DOPM) with autocorrelation structure is proposed as a model for credit risk forecasting. It is more appropriate than the DOPM with independence structure, because correlations among repeated credit ratings have been observed by Altman and Kao [J. Financ. Anal., 1992, 48, 64-75] and Parnes [Financ. Res. Lett., 2007, 4, 217-226]. The unknown parameters in the proposed model are estimated by a generalized estimating equations (GEE) approach (Lipsitz et al. [Statist. Med., 1994, 13, 1149-1163). The GEE approach has been applied in many applications to analyse correlated repeated data due to its less-stringent distributional assumptions and robustness properties. Real data examples are used to illustrate the proposed model. The empirical results confirm that the proposed model compares favorably to the usual DOPM with independence structure, in the sense that the out-of-sample total error rate produced by the former is not only of smaller magnitude, but also of lower volatility. Thus the proposed model is a useful alternative for credit risk forecasting.

Modelling market implied ratings using LASSO variable selection techniques

“…Both studies show that several predictors are important in forecasting credit ratings such as the size of the company, balance sheet position, stock market performance and industry effects. In addition, modelling long-term ratings in a dynamic setting has shown improvements in forecasting (see Hwang, 2011 andHwang, 2013).…”

Rating firms and sensitivity analysis

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