Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises

Shirley Huang Author Email:shirleyhuang@smu.edu.sg and Jun Yu
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Shirley Huang Author Email:shirleyhuang@smu.edu.sg: Lee Kong Chian School of Business, Singapore Management University

Working Papers from Singapore Management University, Sim Kee Boon Institute for Financial Economics

Abstract: In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstruc- ture 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 distri- bution, 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 deviance information criterion (DIC) which is straightforwardly ob- tained 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 mar- kets. 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 (search for similar items in EconPapers)
Pages: 31 Pages
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Published in SMU-SKBI CoFie Working Paper

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http://www.smu.edu.sg/institutes/skbife/downloads/ ... ructure%20Noises.pdf

Related works:
Journal Article: Bayesian analysis of structural credit risk models with microstructure noises (2010)
Working Paper: Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:skb:wpaper:cofie-07-2008

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