Comparison of MCMC Methods for Estimating Stochastic Volatility Models

Manabu Asai

Computational Economics, 2005, vol. 25, issue 3, pages 281-301

Abstract: This 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

Keywords: integration sampler; Markov chain Monte Carlo; mixture sampler; multi-move sampler; simulation smoother (search for similar items in EconPapers)
Date: 2005
View list of references

Downloads: (external link)
http://hdl.handle.net/10.1007/s10614-005-2974-4 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:kap:compec:v:25:y:2005:i:3:p:281-301

Access Statistics for this article

Computational Economics is edited by Hans Amman

More articles in Computational Economics from Springer
Series data maintained by Christopher F. Baum ().