Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models

Roman Liesenfeld and Jean-Francois Richard

No 2004-12, Economics Working Papers from Christian-Albrechts-University of Kiel, Department of Economics

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)
JEL-codes: C15 C22 C52 (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://www.econstor.eu/bitstream/10419/21987/1/EWP-2004-12.pdf (application/pdf)

Related works:
Journal Article: Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cauewp:2443

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