Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Gregor Kastner; Sylvia Frühwirth-Schnatter; Hedibert Freitas Lopes

doi:10.1080/10618600.2017.1322091

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arXiv · 1602.08154

Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Gregor Kastner·Sylvia Frühwirth-Schnatter·Hedibert Freitas Lopes

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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, 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.

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BibTeX RIS

Gregor Kastner, Sylvia Frühwirth-Schnatter, Hedibert Freitas Lopes. 2017-07-19. Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models. https://doi.org/10.1080/10618600.2017.1322091

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