In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact ¯nite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion (DIC) which is straightforwardly obtained from the MCMC output. The method is implemented on the basic structural credit risk model with pure microstructure noises and some more general specifications using daily equity data from US and emerging markets. We ¯nd empirical evidence that microstructure noises are positively correlated with the ¯rm values in emerging markets.
MCMC, Credit risk, Microstructure noise, Structural models, Deviance information criterion
Applied Statistics | Econometrics | Finance and Financial Management
SMU Economics and Statistics Working Paper Series, No. 17-2009
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HUANG, Shirley J. and YU, Jun.
Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises. (2009). 1-30. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1154
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