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Economics Working Papers, Department of Economics, CAU Kiel >
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http://hdl.handle.net/10419/21987
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| Title: | | Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models  |
| Authors: | | Liesenfeld, Roman Richard, Jean-François |
| Issue Date: | | 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 |
| Appears in Collections: | | Economics Working Papers, Department of Economics, CAU Kiel
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