A Bayesian Simulation Approach to Inference on a Multi-State Latent Factor Intensity Model
AbstractThis paper provides a Bayesian approach to inference on a multi-state latent factor intensity model to manage the problem of highly analytically intractable pdfs. The sampling algorithm used to obtain posterior distributions of the model parameters includes a particle filter step and a Metropolis-Hastings step within a Gibbs sampler. A simulated example is conducted to show the feasibility and accuracy of this sampling algorithm. The approach is applied to the case of credit ratings transition matrices.
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 Melbourne Institute of Applied Economic and Social Research, The University of Melbourne in its series Melbourne Institute Working Paper Series with number wp2008n16.
Length: 20 pages
Date of creation: Aug 2008
Date of revision:
Contact details of provider:
Postal: Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, Victoria 3010 Australia
Phone: +61 3 8344 2100
Fax: +61 3 8344 2111
Web page: http://www.melbourneinstitute.com/
More information through EDIRC
This paper has been announced in the following NEP Reports:
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.:
- Siem Jan Koopman & André Lucas & André Monteiro, 2005.
"The Multi-State Latent Factor Intensity Model for Credit Rating Transitions,"
Tinbergen Institute Discussion Papers
05-071/4, Tinbergen Institute, revised 04 Jul 2005.
- Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
- Pamela Nickell & William Perraudin & Simone Varotto, 2001.
"Stability of ratings transitions,"
Bank of England working papers
133, Bank of England.
- Bauwens, Luc & Veredas, David, 2004.
"The stochastic conditional duration model: a latent variable model for the analysis of financial durations,"
Journal of Econometrics,
Elsevier, vol. 119(2), pages 381-412, April.
- BAUWENS, Luc & VEREDAS, David, . "The stochastic conditional duration model: a latent variable model for the analysis of financial durations," CORE Discussion Papers RP -1688, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- 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.
- John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
- Hu, Yen-Ting & Kiesel, Rudiger & Perraudin, William, 2002. "The estimation of transition matrices for sovereign credit ratings," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1383-1406, July.
- 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.
- Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-57, July.
- Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
- André Lucas & Siem Jan Koopman, 2005.
"Business and default cycles for credit risk,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 20(2), pages 311-323.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jenny Chen).
If references are entirely missing, you can add them using this form.