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Christian-Albrechts-Universität zu Kiel

Institut für Volkswirtschaftslehre - Department of Economics
Economics Working Papers

Economics Working Papers: Abstract 2004-12




Roman Liesenfeld, Jean-François Richard




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.

Keywords: Dynamic Latent Variables; Markov Chain Monte Carlo; Maximum likelihood; Simulation Smoother

JEL classification: C15, C22, C52




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