Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Gregor Kastner, Sylvia Frühwirth-Schnatter, Hedibert Freitas Lopes

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

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 to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit nonidentifiability 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 dataset illustrates the superior performance of the new approach for real-world data. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)905-917
Number of pages13
JournalJournal of Computational and Graphical Statistics
Volume26
Issue number4
DOIs
StatePublished - Oct 2 2017
Externally publishedYes

Keywords

  • Ancillarity-sufficiency interweaving strategy (ASIS)
  • Curse of dimensionality
  • Data augmentation
  • Dynamic correlation
  • Dynamic covariance
  • Exchange rate data
  • Markov chain Monte Carlo (MCMC)

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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