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A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk

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  • Koopman, Siem Jan
  • Lucas, André

Abstract

We model 1981–2005 quarterly default frequencies for a panel of U.S. firms in different rating and age classes from the Standard and Poor database. The data are decomposed into systematic and firm-specific risk components, where the systematic component reflects the general economic conditions and the default climate. We need to cope with: the shared exposure of each age cohort, industry, and rating class to the same systematic risk factor; strongly non-Gaussian features of the individual time series; possible dynamics of an unobserved common risk factor; changing default probabilities over the age of the rating; and missing observations. We propose a non-Gaussian multivariate state-space model that deals with all of these issues simultaneously. The model is estimated using importance sampling techniques that have been modified to a multivariate setting. We show in a simulation study that such a multivariate approach improves the performance of the importance sampler. In our empirical work, we find that systematic credit risk may differ substantially in terms of magnitude and timing across industries.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 26 (2008)
Issue (Month): ()
Pages: 510-525
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Handle: RePEc:bes:jnlbes:v:26:y:2008:p:510-525

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References

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  1. Siem Jan Koopman & André Lucas, 2003. "Business and Default Cycles for Credit Risk," Tinbergen Institute Discussion Papers 03-062/2, Tinbergen Institute, revised 09 Jan 2003.
  2. Altman, Edward I. & Suggitt, Heather J., 2000. "Default rates in the syndicated bank loan market: A mortality analysis," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 229-253, January.
  3. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
  4. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
  5. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
  6. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
  7. Linda Allen & Anthony Saunders, 2003. "A survey of cyclical effects in credit risk measurement model," BIS Working Papers 126, Bank for International Settlements.
  8. Dietsch, Michel & Petey, Joel, 2004. "Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 773-788, April.
  9. 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.
  10. Altman, Edward I, 1989. " Measuring Corporate Bond Mortality and Performance," Journal of Finance, American Finance Association, vol. 44(4), pages 909-22, September.
  11. Danielsson, J & Richard, J-F, 1993. "Accelerated Gaussian Importance Sampler with Application to Dynamic Latent Variable Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S153-73, Suppl. De.
  12. Anil Bangia & Francis X. Diebold & Til Schuermann, 2000. "Ratings Migration and the Business Cycle, With Application to Credit Portfolio Stress Testing," Center for Financial Institutions Working Papers 00-26, Wharton School Center for Financial Institutions, University of Pennsylvania.
  13. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, December.
  14. Cowan, Adrian M. & Cowan, Charles D., 2004. "Default correlation: An empirical investigation of a subprime lender," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 753-771, April.
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Citations

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Cited by:
  1. Konrad Banachewicz & Aad van der Vaart & André Lucas, 2006. "Modeling Portfolio Defaults using Hidden Markov Models with Covariates," Tinbergen Institute Discussion Papers 06-094/2, Tinbergen Institute.
  2. Konrad Banachewicz & André Lucas, 2007. "Quantile Forecasting for Credit Risk Management using possibly Mis-specified Hidden Markov Models," Tinbergen Institute Discussion Papers 07-046/2, Tinbergen Institute.
  3. Xin Huang & Hao Zhou & Haibin Zhu, 2009. "A Framework for Assessing the Systemic Risk of Major Financial Institutions," BIS Working Papers 281, Bank for International Settlements.
  4. Abel Elizalde, 2006. "CREDIT RISK MODELS IV: UNDERSTANDING AND PRICING CDOs," Working Papers wp2006_0608, CEMFI.
  5. Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor van der Geest, 2008. "Estimating systematic continuous-time trends in recidivism using a non-Gaussian panel data model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 104-130.
  6. Zhu, Haibin & Tarashev, Nikola A., 2008. "The pricing of correlated default risk: evidence from the credit derivatives market," Discussion Paper Series 2: Banking and Financial Studies 2008,09, Deutsche Bundesbank, Research Centre.
  7. Siem Jan Koopman & André Lucas & Bernd Schwaab, 2008. "Forecasting Cross-Sections of Frailty-Correlated Default," Tinbergen Institute Discussion Papers 08-029/4, Tinbergen Institute.
  8. Michael Kalkbrener & Akwum Onwunta, 2009. "Validating Structural Credit Portfolio Models," Working Papers 014, COMISEF.

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