Forecasting industry sector default rates through dynamic factor models

ABSTRACT In this paper we use a reduced-form model for the analysis of portfolio credit risk. For this purpose, we fit a dynamic factor model to a large data set of default rate proxies and macro-variables for Italy. Multiple step ahead density and probability forecasts are obtained by employing both the direct and indirect methods of prediction together with stochastic simulation of the dynamic factor model. We first find that the direct method is the best performer regarding the out-of-sample projection of financial distressful events. In a second stage of the analysis, we find that reducedform portfolio credit risk measures obtained through the dynamic factor model are lower than those corresponding to the internal-ratings-based analytic formula suggested by Basel II. Moreover, the direct method of forecasting gives the smallest portfolio credit risk measures. Finally, when using the indirect method of forecasting, the simulation results suggest that an increase in the number of dynamic factors (for a given number of principal components) increases portfolio credit risk.