Structural credit risk models with microstructure noise: An empirical analysis for China
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
In this paper a Bayesian Markov chain Monte Carlo (MCMC) method discussed in Huang and Yu (2010) is applied to estimate the credit risk models with microstructure noise, using the daily equity data from China. In literature, the observed equity prices are known to be influenced by market microstructure effects so that they deviate from the corresponding efficient prices. Credit risk models with microstructure noise is a way to depict this relationship. In the Bayesian framework, we employ Gibbs sampling, which is a Markov chain Monte Carlo (MCMC) technique, to analyze such models. We estimate the model with Gaussian iid microstructure term, using equity data of the firms in the Shanghai Stock Exchange 50 index constitutes. Estimates in the model converge well when we use the data of 6 firms out of 16 in our sample.