Comparison of MCMC Methods for Estimating Stochastic Volatility Models
AbstractThis article investigates performances of MCMC methods to estimate stochastic volatility models on simulated and real data. There are two efficient MCMC methods to generate latent volatilities from their full conditional distribution. One is the mixture sampler and the other is the multi-move sampler. There is another efficient method for latent volatilities and all parameters called the integration sampler, which is based on the mixture sampler. This article proposes an alternative method based on the multi-move sampler and finds evidence that it is the best method among them. Copyright Springer Science + Business Media, Inc. 2005
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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 25 (2005)
Issue (Month): 3 (June)
integration sampler; Markov chain Monte Carlo; mixture sampler; multi-move sampler; simulation smoother;
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