Exact Maximum Likelihood and Bayesian Estimation of the Stochastic Volatility Model

This paper considers the classical and Bayesian approaches to the estimation of the stochastic volatility (SV) model. The estimation procedures rely heavily on the fact that SV model can be written in the State Space Form (SSF) with non-Ga ussian disturbances. The first widely employed estimation procedure to use this model was the quasi-maximum likelihood estimator proposed by Harvey et al. The Bayesian approach was proposed by Jacquier et al.(1994). Lately, many papers have appeared in the literature dealing with non-Gaussian state space models which directly influenced the estimation of the SV model. Some of these methods proposed to estimate the SV model are compared using the Sao Paulo stock exchange index (IBOVESPA) and simulated series. The influence of outliers is also considered.