Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
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.
Cite
@article{arxiv.1602.08154,
title = {Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models},
author = {Gregor Kastner and Sylvia Frühwirth-Schnatter and Hedibert Freitas Lopes},
journal= {arXiv preprint arXiv:1602.08154},
year = {2019}
}