Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models |
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Authors: | Roman Liesenfeld Jean-François Richard |
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Institution: | 1. Department of Economics , Christian-Albrecht-Universit?t , Kiel , Germany liesenfeld@statecon.uni-kiel.de;3. Department of Economics , University of Pittsburgh , Pittsburgh , Pennsylvania , USA |
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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. |
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Keywords: | Dynamic latent variables Markov chain Monte Carlo Maximum likelihood Simulation smoother |
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