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

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cipollini, andrea
missaglia, giuseppe

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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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 3582.

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Date of creation: 30 May 2007
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Handle: RePEc:pra:mprapa:3582

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Related research
Keywords: Dynamic Factor Model; Forecasting; Stochastic Simulation; Risk Management; Banking;

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Find related papers by JEL classification:
G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages

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  5. Philipp J. Schönbucher, 2000. "Factor Models for Portofolio Credit Risk," Bonn Econ Discussion Papers bgse16_2001, University of Bonn, Germany. [Downloadable!]
  6. Lucas, Andre & Klaassen, Pieter & Spreij, Peter & Straetmans, Stefan, 2001. "An analytic approach to credit risk of large corporate bond and loan portfolios," Journal of Banking & Finance, Elsevier, vol. 25(9), pages 1635-1664, September. [Downloadable!] (restricted)
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  7. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August. [Downloadable!] (restricted)
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  8. André Lucas & Siem Jan Koopman, 2005. "Business and default cycles for credit risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 311-323. [Downloadable!]
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  9. Hamerle, Alfred & Liebig, Thilo & Rösch, Daniel, 2003. "Credit Risk Factor Modeling and the Basel II IRB Approach," Discussion Paper Series 2: Banking and Financial Studies 2003,02, Deutsche Bundesbank, Research Centre. [Downloadable!]
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  13. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  14. Martin Summer & Helmut Elsinger & Alfred Lehar, 2002. "Risk Assessment for Banking Systems," Working Papers 79, Oesterreichische Nationalbank (Austrian Central Bank). [Downloadable!]
  15. Hamerle, Alfred & Liebig, Thilo & Scheule, Harald, 2004. "Forecasting Credit Portfolio Risk," Discussion Paper Series 2: Banking and Financial Studies 2004,01, Deutsche Bundesbank, Research Centre. [Downloadable!]
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