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Quantile forecasting for credit risk management using possibly misspecified hidden Markov models

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Author Info
Konrad Banachewicz (Department of Mathematics, Vrije University Amsterdam, The Netherlands)
André Lucas (Department of Finance and Tinbergen Institute, Vrije University Amsterdam, The Netherlands)

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Abstract

Recent models for credit risk management make use of hidden Markov models (HMMs). 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 misspecified. In this paper, we focus on misspecification in the dynamics and 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 US default data. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1072
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 27 (2008)
Issue (Month): 7 ()
Pages: 566-586
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Handle: RePEc:jof:jforec:v:27:y:2008:i:7:p:566-586

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. 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. [Downloadable!] (restricted)
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  2. Siem Jan Koopman & André Lucas & Robert J. Daniels, 2005. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," DNB Working Papers 055, Netherlands Central Bank, Research Department. [Downloadable!]
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  3. 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. [Downloadable!] (restricted)
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  4. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 131-149, March. [Downloadable!] (restricted)
  5. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January. [Downloadable!] (restricted)
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  6. Siem Jan Koopman & Roman Kraeussl & Andre Lucas & Andre Monteiro, 2006. "Credit Cycles and Macro Fundamentals," Tinbergen Institute Discussion Papers 06-023/2, Tinbergen Institute. [Downloadable!]
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  7. 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. [Downloadable!] (restricted)
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