Improving efficiency of Bayesian stochastic volatility estimation by non-centering and interweaving

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

This paper considers Bayesian inference for stochastic volatility (SV) models using efficient MCMC inference. Our method is based on the popular approximation of the log chi-squared distribution by a mixture of 10 normal distributions which allows to sample the latent volatilities simultaneously, however, we introduce several improvements. First, rather than using standard forward-filtering-backward-sampling to draw the volatilities, we apply a sparse Cholesky factor algorithm to the high-dimensional joint density of all volatilities. This reduces computing time considerably because it allows joint sampling without running a filter. Second, we consider various reparameterizations of the augmented SV model. Under the standard parameterization, augmented MCMC estimation turns out to be inefficient, especially if the volatility of volatility parameter in the latent state equation is small. By considering a non-centered version of the SV model, this parameter is moved to the observation equation. Using MCMC estimation for this transformed model reduces the inefficiency factor in particular for the volatility of volatility parameter considerably. Finally, we adopt an interweaving strategy outperforming both centered and non-centered parameterizations in terms of sampling efficiency with respect to all parameters.
Original languageEnglish
Title of host publicationProceedings of the 17th European Young Statisticians Meeting (EYSM)
Editors Paulo C. Rodrigues, Miguel de Carvalho
Place of PublicationLisboa, Portugal
PublisherFaculdade de Ciências e Tecnologia Universidade Nova de Lisboa
Pages131 - 135
ISBN (Print)978-972-8893-27-9
Publication statusPublished - 1 Feb 2011

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502025 Econometrics
  • 102022 Software development
  • 101018 Statistics
  • 101026 Time series analysis

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