Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Publication: Scientific journalJournal articlepeer-review

Abstract

We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.
Original languageEnglish
Pages (from-to)905 - 917
JournalJournal of Computational and Graphical Statistics
Volume26
Issue number4
DOIs
Publication statusPublished - 2017

Austrian Classification of Fields of Science and Technology (ÖFOS)

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

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