Dynamic Factor analysis of industry sector default rates and implication for Portfolio Credit Risk Modelling

Andrea Cipollini and Giuseppe Missaglia

MPRA Paper from University Library of Munich, Germany

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: Dynamic Factor Model; Forecasting; Stochastic Simulation; Risk Management; Banking (search for similar items in EconPapers)
JEL-codes: C53 G21 G33 (search for similar items in EconPapers)
Date: 2007-05-30
New Economics Papers: this item is included in nep-ban, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Dynamic Factor analysis of industry sector default rates and implication for Portfolio Credit Risk Modelling (2007)
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