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Quantile Forecasting for Credit Risk Management using possibly Mis-specified Hidden Markov Models

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Author Info

  • Konrad Banachewicz

    ()
    (Vrije Universiteit Amsterdam)

  • Andr� Lucas

    ()
    (Vrije Universiteit Amsterdam)

Abstract

Recent models for credit risk management make use of Hidden Markov Models (HMMs). The HMMs are used to forecast quantiles of corporate default rates. Little research has been done on the quality of such forecasts if the underlying HMM is potentially mis-specified. In this paper, we focus on mis-specification in the dynamics and the dimension of the HMM. We consider both discrete and continuous state HMMs. The differences are substantial. Underestimating the number of discrete states has an economically significant impact on forecast quality. Generally speaking, discrete models underestimate the high-quantile default rate forecasts. Continuous state HMMs, however, vastly overestimate high quantiles if the true HMM has a discrete state space. In the reverse setting, the biases are much smaller, though still substantial in economic terms. We illustrate the empirical differences using U.S. default data.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 07-046/2.

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Date of creation: 13 Jun 2007
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Handle: RePEc:dgr:uvatin:20070046

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

Related research

Keywords: defaults; Markov switching; misspecification; quantile forecast; Expectation-Maximization; simulated maximum likelihood; importance sampling;

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References

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  1. Konrad Banachewicz & André Lucas & Aad van der Vaart, 2008. "Modelling Portfolio Defaults Using Hidden Markov Models with Covariates," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 11(1), pages 155-171, 03.
  2. Pamela Nickell & William Perraudin & Simone Varotto, 2001. "Stability of ratings transitions," Bank of England working papers, Bank of England 133, Bank of England.
  3. Lucas, Andre & Klaassen, Pieter, 2006. "Discrete versus continuous state switching models for portfolio credit risk," Journal of Banking & Finance, Elsevier, 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, Elsevier, vol. 26(2-3), pages 445-474, March.
  5. Siem Jan Koopman & André Lucas & Robert Daniels, 2005. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Tinbergen Institute Discussion Papers, Tinbergen Institute 05-060/4, Tinbergen Institute.
  6. 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.
  7. 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.
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Cited by:
  1. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2014. "A new quantile regression forecasting model," Journal of Business Research, Elsevier, Elsevier, vol. 67(5), pages 779-784.

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