A Bayesian Simulation Approach to Inference on a Multi-State Latent Factor Intensity Model
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.
|Date of creation:||Aug 2008|
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- Siem Jan Koopman & André Lucas & André Monteiro, 2005.
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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.
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- 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.
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