Novel techniques for Bayesian inference in univariate and multivariate stochastic volatility models

In this paper we exploit properties of the likelihood function of the stochastic volatility model to show that it can be approximated accurately and efficiently using a response surface methodology. The approximation is across the plausible range of parameter values and all possible data and is found to be highly accurate. The methods extend easily to multivariate models and are applied to artificial data as well as ten exchange rates and all stocks of FTSE100 using daily data. Formal comparisons with multivariate GARCH models are undertaken using a special prior for the GARCH parameters. The comparisons are based on marginal likelihood and the Bayes factors