Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

• Main Authors: Kastner, Gregor, Frühwirth-Schnatter, Sylvia, Lopes, Hedibert Freitas
• Format: Others
• Language: en
• Published: WU Vienna University of Economics and Business 2016
• Subjects: Ancillarity-sufficiency interweaving strategy (ASIS) / Curse of dimensionality / Data augmentation / Dynamic covariance matrices / Exchange rate data / Markov chain Monte Carlo (MCMC)
• Online Access: http://epub.wu.ac.at/4875/1/research_report_updated.pdf

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, Journal of Computational and Graphical Statistics, 20(3), 531-570, 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. === Series: Research Report Series / Department of Statistics and Mathematics