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A Bayesian Simulation Approach to Inference on a Multi-State Latent Factor Intensity Model

Author

Listed:
  • Chew Lian Chua

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • G. C. Lim

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Penelope Smith

    (Westpac Banking Corporation, Sydney)

Abstract

This 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.

Suggested Citation

  • 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.
    • Handle: RePEc:iae:iaewps:wp2008n16
    as

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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2008n16.pdf
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    References listed on IDEAS

    as
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    2. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
    3. 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.
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    8. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," LIDAM Discussion Papers CORE 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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