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The multi-state latent factor intensity model for credit rating transitions

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  • Koopman, Siem Jan
  • Lucas, Andre
  • Monteiro, Andre

Abstract

A 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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 142 (2008)
Issue (Month): 1 (January)
Pages: 399-424
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Handle: RePEc:eee:econom:v:142:y:2008:i:1:p:399-424

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For corrections or technical questions regarding this item, or to correct its listing, contact: (Jeroen Loos).

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References

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  1. Van den Berg, Gerard J., 2000. "Duration Models: Specification, Identification, and Multiple Durations," MPRA Paper 9446, University Library of Munich, Germany.
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Citations

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Cited by:
  1. Nikolaus Hautsch, 2007. "Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model," CFS Working Paper Series 2007/25, Center for Financial Studies.
  2. Drew Creal & Siem Jan Koopman & André Lucas, 2008. "A General Framework for Observation Driven Time-Varying Parameter Models," Tinbergen Institute Discussion Papers 08-108/4, Tinbergen Institute.
  3. Monteiro, André A., . "The econometrics of randomly spaced financial data: a survey," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/5995, Universidad Carlos III de Madrid.
  4. Luc Bauwens & Nikolaus Hautsch, 2007. "Modelling Financial High Frequency Data Using Point Processes," SFB 649 Discussion Papers SFB649DP2007-066, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Chew Lian Chua & G. C. Lim & Penelope Smith, 2008. "A Bayesian Simulation Approach to Inference on a Multi-State Latent Factor Intensity Model," Melbourne Institute Working Paper Series wp2008n16, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  6. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
  7. Siem Jan Koopman & André Lucas & Bernd Schwaab, 2008. "Forecasting Cross-Sections of Frailty-Correlated Default," Tinbergen Institute Discussion Papers 08-029/4, Tinbergen Institute.
  8. Chew Lian Chua & Robert Dixon & G. C. Lim, 2007. "What Drives Worker Flows?," Melbourne Institute Working Paper Series wp2007n34, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.

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