Dynamic Factor Analysis of Industry Sector Default Rates and Implication for Portfolio Credit Risk Modelling
27 Pages Posted: 22 Jun 2007
Date Written: May 2007
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, 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. 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, 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. Finally, the simulation results suggest that the benefits in terms of credit risk diversification tend to diminish with an increasing number of factors, especially when using the indirect method of forecasting.
Keywords: Factor Model, Forecasting, Stochastic Simulation, Risk Management, Banking
JEL Classification: C32, C53, E17, G21, G33
Suggested Citation: Suggested Citation
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