Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach

摘要

We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA (p, q)-GARCH (r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data.