Bayesian Analysis of Stochastic Volatility Models.

New techniques for the analysis of stochastic volatility models are developed. A Metropolis algorithm is used to construct a Markov Chain simulation tool. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of the authors' method. In addition, multistep-ahead predictive densities can be constructed. The authors illustrate their method by analyzing stock data. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches.