Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Kastner, Gregor and Frühwirth-Schnatter, Sylvia and Lopes, Hedibert Freitas (2016) Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models. Research Report Series / Department of Statistics and Mathematics, 128. WU Vienna University of Economics and Business, Vienna.


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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.

Item Type: Paper
Additional Information: This paper is forthcoming in the Journal of Computational and Graphical Statistics: http://dx.doi.org/10.1080/10618600.2017.1322091
Keywords: Ancillarity-sufficiency interweaving strategy (ASIS) / Curse of dimensionality / Data augmentation / Dynamic covariance matrices / Exchange rate data / Markov chain Monte Carlo (MCMC)
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Gregor Kastner
Date Deposited: 26 Feb 2016 10:53
Last Modified: 22 Oct 2019 00:41
FIDES Link: https://bach.wu.ac.at/d/research/results/81367/
URI: https://epub.wu.ac.at/id/eprint/4875


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