Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models

Abstract

In this paper Efficient Importance Sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure for the Monte Carlo (MC) evaluation of high-dimensional interdependent integrals. It can be used to carry out ML-estimation of SV models as well as simulation smoothing where the latent volatilities are sampled at once. Based on this EIS simulation smoother a Bayesian Markov Chain Monte Carlo (MCMC) posterior analysis of the parameters of SV models can be performed. --Dynamic Latent Variables,Markov Chain Monte Carlo,Maximum likelihood,Simulation Smoother