Quantile Forecasting for Credit Risk Management using possibly Mis-specified Hidden Markov Models
AbstractRecent 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 07-046/2.
Date of creation: 13 Jun 2007
Date of revision:
Contact details of provider:
Web page: http://www.tinbergen.nl
defaults; Markov switching; misspecification; quantile forecast; Expectation-Maximization; simulated maximum likelihood; importance sampling;
Other versions of this item:
- 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.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-07-07 (All new papers)
- NEP-ECM-2007-07-07 (Econometrics)
- NEP-FOR-2007-07-07 (Forecasting)
- NEP-RMG-2007-07-07 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Koopman, Siem Jan & Lucas, AndrÃ©, 2008.
"A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 26, pages 510-525.
- Siem Jan Koopman & AndrÃ© 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.
- 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 05-060/4, Tinbergen Institute.
- Koopman, Siem Jan & Kräussl, Roman & Lucas, André, 2006.
"Credit cycles and macro fundamentals,"
CFS Working Paper Series
2006/33, Center for Financial Studies (CFS).
- André Lucas & Pieter Klaassen, 2003.
"Discrete versus Continuous State Switching Models for Portfolio Credit Risk,"
Tinbergen Institute Discussion Papers
03-075/2, Tinbergen Institute, revised 30 Sep 2003.
- 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.
- Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000.
"Stability of rating transitions,"
Journal of Banking & Finance,
Elsevier, vol. 24(1-2), pages 203-227, January.
- 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.
- 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.
- 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.
- Konrad Banachewicz & André Lucas & Aad van der Vaart, 2008.
"Modelling Portfolio Defaults Using Hidden Markov Models with Covariates,"
Royal Economic Society, vol. 11(1), pages 155-171, 03.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Antoine Maartens (+31 626 - 160 892)).
If references are entirely missing, you can add them using this form.