Dynamic Factor Analysis Of Industry Sector Default Rates And Implication For Portfolio Credit Risk Modelling

In this paper the authors use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, they fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macro-variables for Italy. Multi step ahead density and probability forecasts are obtained by employing both the direct and indirect method of prediction together with stochastic simulation of the DF model. They, 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, the direct method of forecasting through principal components is shown to provide the least sensitive measures of Portfolio Credit Risk to various multifactor model specifications.