Forecasting Credit Ratings with the Varying-coefficient Model
The dynamic ordered varying-coefficient probit model (DOVPM) is proposed as a model for studying credit ratings.It is constructed by replacing the constant coefficients of firm-specific predictors in the dynamic ordered probit model (DOPM; Blume et al.1998) with the smooth functions of macroeconomic variables.Thus,the proposed model allows the effects of firm-specific predictors on credit risk to change with macroeconomic dynamics (Pesaran et al.2006).The unknown coefficient functions in DOVPM are estimated using a local maximum likelihood method.Real data examples for studying credit ratings are used to illustrate the proposed model.Our empirical results show that macroeconomic dynamics significantly affect the sensitivities of firm-specific predictors on credit ratings,and there are nonlinear relationships between them.To compare the out-of-sample performance of DOPM and DOVPM,using an expanding rolling window approach,our empirical results confirm that the advantages of DOVPM over DOPM are twofold.First,the out-of-sample firmby-firm rating probabilities predicted by DOVPM are more accurate and robust.Second,the out-of- sample total error rates of the prediction rule based on DOVPM are not only of smaller magnitudes but also of lower volatility.Thus,the proposed DOVPM is a useful alternative for credit rating forecasting.
Credit rating forecasting Dynamic ordered probit model Expanding rolling window approach Predicted number of ratings Varying-coefficient model
Ruey-Ching Hwang
Department of Finance,National Dong Hwa University,Taiwan 974
国内会议
上海
英文
759-795
2012-11-02（万方平台首次上网日期，不代表论文的发表时间）