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Forecasting Cross-Sections of Frailty-Correlated Default

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  • Siem Jan Koopman

    ()
    (VU University Amsterdam)

  • Andr� Lucas

    ()
    (VU University Amsterdam)

  • Bernd Schwaab

    ()
    (VU University Amsterdam)

Abstract

We propose a novel econometric model for estimating and forecasting cross-sections of time-varying conditional default probabilities. The model captures the systematic variation in corporate default counts across e.g. rating and industry groups by using dynamic factors from a large panel of selected macroeconomic and financial data as well as common unobserved risk factors. All factors are statistically and economically significant and together capture a large part of the time-variation in observed default rates. In this framework we improve the out-of-sample forecasting accuracy associated with conditional default probabilities by about 10-35% in terms of Mean Absolute Error, particularly in years of default stress. Forthcoming in the Journal of Econometrics .

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-029/4.

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Date of creation: 20 Mar 2008
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Handle: RePEc:dgr:uvatin:20080029

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Web page: http://www.tinbergen.nl

Related research

Keywords: Non-Gaussian Panel Data; Common Factors; Unobserved Components; Forecasting Conditional Default Probabilities;

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References

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  1. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, Elsevier, vol. 14(2), pages 131-149, March.
  2. Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Discussion Paper, Tilburg University, Center for Economic Research 1998-141, Tilburg University, Center for Economic Research.
  3. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, American Finance Association, vol. 62(1), pages 93-117, 02.
  4. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, Elsevier, vol. 83(3), pages 635-665, March.
  5. 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, Wharton School Center for Financial Institutions, University of Pennsylvania 03-13, Wharton School Center for Financial Institutions, University of Pennsylvania.
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  7. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, Elsevier, vol. 142(1), pages 399-424, January.
  8. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, Elsevier, vol. 24(1-2), pages 119-149, January.
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  13. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 26, pages 510-525.
  14. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  15. Nyblom, Jukka & Harvey, Andrew, 2000. "Tests Of Common Stochastic Trends," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 16(02), pages 176-199, April.
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Citations

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Cited by:
  1. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(1), pages 42-54, January.
  2. Drew Creal & Siem Jan Koopman & Andr� Lucas, 2008. "A General Framework for Observation Driven Time-Varying Parameter Models," Tinbergen Institute Discussion Papers, Tinbergen Institute 08-108/4, Tinbergen Institute.
  3. Drew Creal & Siem Jan Koopman & Andre Lucas, 2009. "A General Framework for Observation Driven Time-Varying Parameter Models," Global COE Hi-Stat Discussion Paper Series, Institute of Economic Research, Hitotsubashi University gd08-038, Institute of Economic Research, Hitotsubashi University.
  4. Stefan Kerbl & Michael Sigmund, 2011. "What Drives Aggregate Credit Risk?," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), Oesterreichische Nationalbank (Austrian Central Bank), issue 22, pages 72-87.

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