Publication Type

Working Paper

Version

publishedVersion

Publication Date

11-2009

Abstract

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.

Keywords

MCMC, Credit risk, Microstructure noise, Structural models, Deviance information criterion

Discipline

Applied Statistics | Econometrics | Finance and Financial Management

Research Areas

Econometrics; Finance

First Page

1

Last Page

30

Publisher

SMU Economics and Statistics Working Paper Series, No. 17-2009

City or Country

Singapore

Copyright Owner and License

Authors

Comments

Published in Journal of Econometrics https://doi.org/10.1016/j.jedc.2010.05.008

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