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Discrete versus Continuous State Switching Models for Portfolio Credit Risk

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  • André Lucas

    ()
    (ECO/FIN, Faculty of Economics and Business Administration, Vrije Universiteit Amsterdam)

  • Pieter Klaassen

    ()
    (ABN AMRO Bank NV, Amsterdam)

Abstract

Dynamic models for credit rating transitions are important ingredients for dynamic credit risk analyses. We compare the properties of two such models that have recently been put forward. The models mainly differ in their treatment of systematic risk, which can be modeled either using discrete states (e.g., expansion versus recession) or continous states. It turns out that the implied asset correlations for discrete state switching models are implausibly low compared to correlation estimates in the literature. Given these limited correlations, we conclude that care has to be taken when discrete state regime switching models are employed for dynamic credit risk management. As a side result of our analysis, we obtain indirect evidence that default correlations may change over the business cycle.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 03-075/2.

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Date of creation: 29 Sep 2003
Date of revision: 30 Sep 2003
Handle: RePEc:dgr:uvatin:20030075

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

Related research

Keywords: credit risk; regime switching; latent variable models; factor models;

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References

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  1. Bangia, Anil & Diebold, Francis X. & Kronimus, Andre & Schagen, Christian & Schuermann, Til, 2002. "Ratings migration and the business cycle, with application to credit portfolio stress testing," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 445-474, March.
  2. 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.
  3. Pamela Nickell & William Perraudin & Simone Varotto, 2001. "Stability of ratings transitions," Bank of England working papers 133, Bank of England.
  4. Jarrow, Robert A & Turnbull, Stuart M, 1995. " Pricing Derivatives on Financial Securities Subject to Credit Risk," Journal of Finance, American Finance Association, vol. 50(1), pages 53-85, March.
  5. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
  6. Linda Allen & Anthony Saunders, 2003. "A survey of cyclical effects in credit risk measurement model," BIS Working Papers 126, Bank for International Settlements.
  7. 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.
  8. Michael B. Gordy, 1998. "A comparative anatomy of credit risk models," Finance and Economics Discussion Series 1998-47, Board of Governors of the Federal Reserve System (U.S.).
  9. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
  10. Siem Jan Koopman & André Lucas & Pieter Klaassen, 2002. "Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation," Tinbergen Institute Discussion Papers 02-107/2, Tinbergen Institute.
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Citations

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Cited by:
  1. Konrad Banachewicz & André Lucas & Aad van der Vaart, 2008. "Modelling Portfolio Defaults Using Hidden Markov Models with Covariates," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 155-171, 03.
  2. Guler Aras & Lale Aslan, 2011. "Capital structure and credit risk management: evidence from Turkey," International Journal of Accounting and Finance, Inderscience Enterprises Ltd, vol. 3(1), pages 1-20.
  3. Petr JAKUBÍK, 2007. "Macroeconomic Environment and Credit Risk (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 57(1-2), pages 60-78, March.
  4. Konrad Banachewicz & André Lucas, 2008. "Quantile forecasting for credit risk management using possibly misspecified hidden Markov models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 566-586.
  5. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 42-54, January.
  6. Petr Jakubík, 2007. "Credit Risk and the Finnish Economy," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 1(3), pages 254-285, November.
  7. Konrad Banachewicz & Andr� Lucas, 2007. "Quantile Forecasting for Credit Risk Management using possibly Mis-specified Hidden Markov Models," Tinbergen Institute Discussion Papers 07-046/2, Tinbergen Institute.
  8. Siem Jan Koopman & Roman Kraeussl & Andre Lucas & Andre Monteiro, 2006. "Credit Cycles and Macro Fundamentals," Tinbergen Institute Discussion Papers 06-023/2, Tinbergen Institute.
  9. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.
  10. Konrad Banachewicz & Aad van der Vaart & Andr� Lucas, 2006. "Modeling Portfolio Defaults using Hidden Markov Models with Covariates," Tinbergen Institute Discussion Papers 06-094/2, Tinbergen Institute.
  11. Kim, Mi Ae & Jang, Bong-Gyu & Lee, Ho-Seok, 2008. "A first-passage-time model under regime-switching market environment," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2617-2627, December.

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