Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/21987
Authors: 
Liesenfeld, Roman
Richard, Jean-François
Year of Publication: 
2004
Series/Report no.: 
Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2004,12
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.
Subjects: 
Dynamic Latent Variables
Markov Chain Monte Carlo
Maximum likelihood
Simulation Smoother
JEL: 
C22
C15
C52
Document Type: 
Working Paper

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