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Modeling Portfolio Defaults using Hidden Markov Models with Covariates

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Author Info

  • Konrad Banachewicz

    ()
    (Vrije Universiteit Amsterdam)

  • Aad van der Vaart

    ()
    (Vrije Universiteit Amsterdam)

  • Andr� Lucas

    ()
    (Vrije Universiteit Amsterdam)

Abstract

We extend the Hidden Markov Model for defaults of Crowder, Davis, and Giampieri (2005) to include covariates. The covariates enhance the prediction of transition probabilities from high to low default regimes. To estimate the model, we extend the EM estimating equations to account for the time varying nature of the conditional likelihoods due to sample attrition and extension. Using empirical U.S. default data, we find that GDP growth, the term structure of interest rates and stock market returns impact the state transition probabilities. The impact, however, is not uniform across industries. We only find a weak correspondence between industry credit cycle dynamics and general business cycles.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 06-094/2.

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Date of creation: 25 Oct 2006
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Handle: RePEc:dgr:uvatin:20060094

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

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Keywords: defaults; Markov switching; default regimes;

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References

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  1. Siem Jan Koopman & Roman Kraeussl & Andre Lucas & Andre Monteiro, 2006. "Credit Cycles and Macro Fundamentals," Tinbergen Institute Discussion Papers 06-023/2, Tinbergen Institute.
  2. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
  3. Lucas, Andre & Klaassen, Pieter, 2006. "Discrete versus continuous state switching models for portfolio credit risk," Journal of Banking & Finance, Elsevier, vol. 30(1), pages 23-35, January.
  4. 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.
  5. Siem Jan Koopman & Andr� Lucas & Robert J. Daniels, 2005. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," DNB Working Papers, Netherlands Central Bank, Research Department 055, Netherlands Central Bank, Research Department.
  6. 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.
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Cited by:
  1. Konrad Banachewicz & André Lucas, 2008. "Quantile forecasting for credit risk management using possibly misspecified hidden Markov models," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 27(7), pages 566-586.
  2. Benjamin Neudorfer & Michael Sigmund & Alexander Trachta, 2011. "Detecting Financial Stability Vulnerabilities in Due Time: Can Simple Indicators Identify a Complex Issue?," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), Oesterreichische Nationalbank (Austrian Central Bank), issue 22, pages 59-71.
  3. Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012. "Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
  4. Elliott, Robert J. & Chen, Zhiping & Duan, Qihong, 2009. "Insurance claims modulated by a hidden Brownian marked point process," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 163-172, October.
  5. Dimitris Gavalas & Theodore Syriopoulos, 2014. "Bank Credit Risk Management and Migration Analysis; Conditioning Transition Matrices on the Stage of the Business Cycle," International Advances in Economic Research, Springer, Springer, vol. 20(2), pages 151-166, May.
  6. Dimitris Gavalas & Theodore Syriopoulos, 2014. "Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 2(1), pages 122-143, March.
  7. Spezia, L. & Cooksley, S.L. & Brewer, M.J. & Donnelly, D. & Tree, A., 2014. "Modelling species abundance in a river by Negative Binomial hidden Markov models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 71(C), pages 599-614.
  8. 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.
  9. 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.
  10. repec:onb:oenbwp:y:2011:i:22:b:1 is not listed on IDEAS

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