The multi-state latent factor intensity model for credit rating transitions
AbstractA new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semi-Markov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model. This discussion paper has resulted in a publication in the Journal of Econometrics , 142(1), 399-424.
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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 142 (2008)
Issue (Month): 1 (January)
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconom
Other versions of this item:
- 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.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
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 & Kräussl, Roman & Lucas, André, 2006.
"Credit cycles and macro fundamentals,"
CFS Working Paper Series
2006/33, Center for Financial Studies (CFS).
- Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000.
"Stability of rating transitions,"
Journal of Banking & Finance,
Elsevier, vol. 24(1-2), pages 203-227, January.
- Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007.
"Multi-period corporate default prediction with stochastic covariates,"
Journal of Financial Economics,
Elsevier, vol. 83(3), pages 635-665, March.
- Darrel Duffie & Leandro Saita & Ke Wang, 2005. "Multi-Period Corporate Default Prediction With Stochastic Covariates," CARF F-Series CARF-F-047, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
- Darrell Duffie & Leandro Siata & Ke Wang, 2006. "Multi-Period Corporate Default Prediction With Stochastic Covariates," NBER Working Papers 11962, National Bureau of Economic Research, Inc.
- Darrel Duffie & Leandro Saita & Ke Wang, 2005. "Multi-Period Corporate Default Prediction With Stochastic Covariates," CIRJE F-Series CIRJE-F-373, CIRJE, Faculty of Economics, University of Tokyo.
- 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.
- Patrick Gagliardini & Christian Gourieroux, 2004. "Stochastic Migration Models with Application to Corporate Risk," Working Papers 2004-35, Centre de Recherche en Economie et Statistique.
- Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 505-531, September.
- BAUWENS, Luc & HAUTSCH, Nikolaus, 2003. "Dynamic latent factor models for intensity processes," CORE Discussion Papers 2003103, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Andre Monteiro & Georgi V. Smirnov & Andre Lucas, 2006. "Nonparametric Estimation for Non-Homogeneous Semi-Markov Processes: An Application to Credit Risk," Tinbergen Institute Discussion Papers 06-024/2, Tinbergen Institute, revised 27 Mar 2006.
- 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.
- Van den Berg, Gerard J., 2000.
"Duration Models: Specification, Identification, and Multiple Durations,"
9446, University Library of Munich, Germany.
- Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, September.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- 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
055, Netherlands Central Bank, Research Department.
- 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 & 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 & Lucas, Andre & Klaassen, Pieter, 2005. "Empirical credit cycles and capital buffer formation," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 3159-3179, December.
- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
- Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
- Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
- Gagliardini, P. & Gourieroux, C., 2005. "Migration correlation: Definition and efficient estimation," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 865-894, April.
- Carling, Kenneth & Jacobson, Tor & Lindé, Jesper & Roszbach, Kasper, 2002. "Capital Charges under Basel II: Corporate Credit Risk Modelling and the Macro Economy," Working Paper Series 142, Sveriges Riksbank (Central Bank of Sweden).
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.