A Bayesian inference for time series via copula-based Markov chain models

This paper studies the nonstandardized Student's t-distribution for fitting serially correlated observations where serial dependence is described by the copula-based Markov chain. Due to the computational difficulty of obtaining maximum likelihood estimates, alternatively, we develop Bayesian inference using the empirical Bayes method through the resampling procedure. We provide the simulations to examine the performance and also analyze the stock price data in empirical studies for illustration.