Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets

摘要: Abstract The market microstructure (MM) models using normal distribution are useful tools for modeling financial time series, but they cannot explain essential characteristics of skewness and heavy tails, which may occur in a market. To cope with this problem, heavy-tailed model based on Student- t (MM- ) is proposed paper. Under the assumption non-normality, an efficient Markov chain Monte Carlo (MCMC) method developed parameter estimation model. simulation study verifies effectiveness approach. In empirical study, various stock indices compared to MM other distributions, such as mixture two distributions. Empirical results indicate that prices/returns have tails MM- provides better fit than distributions some series. Comparison different type also done, demonstrates fits three stochastic volatility (SV- distribution.