Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models

Roman Liesenfeld and Jean-Francois Richard

Econometric Reviews, 2006, vol. 25, issue 2-3, 335-360

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.

Keywords: Dynamic latent variables; Markov chain Monte Carlo; Maximum likelihood; Simulation smoother (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1080/07474930600713424

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