Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2010

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 finite 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 devian 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 find empirical evidence that microstructure noises are positively correlated with the firm values in emerging markets.

Keywords

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

Discipline

Econometrics | Finance and Financial Management | Portfolio and Security Analysis

Research Areas

Finance; Econometrics

Publication

Journal of Economic Dynamics and Control

Volume

34

Issue

11

First Page

2259

Last Page

2272

ISSN

0165-1889

Identifier

10.1016/j.jedc.2010.05.008

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.jedc.2010.05.008

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