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

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  • Andrea Cipollini

    ()

  • Giuseppe Missaglia

    ()

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 macrovariables 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, we find that reduced form Portfolio Credit Risk measures obtained through DF are lower than the one corresponding to the Internal Ratings Based analytic formula suggested by Basel 2. 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.

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File URL: http://www.recent.unimore.it/wp/RECent-wp7.pdf
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Bibliographic Info

Paper provided by University of Modena and Reggio E., Dept. of Economics in its series Center for Economic Research (RECent) with number 007.

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Length: pages 28
Date of creation: Oct 2007
Date of revision:
Handle: RePEc:mod:recent:007

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Web page: http://www.recent.unimore.it/
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Related research

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

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  1. Michael B. Gordy, 2002. "A risk-factor model foundation for ratings-based bank capital rules," Finance and Economics Discussion Series 2002-55, Board of Governors of the Federal Reserve System (U.S.).
  2. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2008. "Opening the Black Box: Structural Factor Models with Large Cross-Sections," Working Papers ECARES 2008_036, ULB -- Universite Libre de Bruxelles.
  3. 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.
  4. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  5. Philipp J. Schönbucher, 2000. "Factor Models for Portofolio Credit Risk," Bonn Econ Discussion Papers bgse16_2001, University of Bonn, Germany.
  6. Lucas, André & Klaassen, Pieter & Spreij, Peter, 1999. "An analytic approach to credit risk of large corporate bond and loan portfolios," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  7. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
  8. M. Hashem Pesaran & Til Schuermann & Björn-Jakob Treutler & Scott M. Weiner & April, . "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Center for Financial Institutions Working Papers 03-13, Wharton School Center for Financial Institutions, University of Pennsylvania.
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  10. Giuseppe Marotta & Chiara Pederzoli & Costanza Torricelli, 2005. "Forward-looking estimation of default probabilities with Italian data," Heterogeneity and monetary policy 0504, Universita di Modena e Reggio Emilia, Dipartimento di Economia Politica.
  11. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
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  13. Dirk Tasche, 2005. "Measuring sectoral diversification in an asymptotic multi-factor framework," Papers physics/0505142, arXiv.org, revised Jul 2006.
  14. Forni, Mario & Lippi, Marco & Reichlin, Lucrezia, 2003. "Opening the Black Box: Structural Factor Models versus Structural VARs," CEPR Discussion Papers 4133, C.E.P.R. Discussion Papers.
  15. 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.
  16. 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.
  17. Hanson, Samuel G. & Pesaran, M. Hashem & Schuermann, Til, 2008. "Firm heterogeneity and credit risk diversification," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 583-612, September.
  18. Koopman, Siem Jan & Lucas, Andre & Klaassen, Pieter, 2005. "Empirical credit cycles and capital buffer formation," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 3159-3179, December.
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Cited by:
  1. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.

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