TY - UNPB
T1 - Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
AU - Kastner, Gregor
AU - Frühwirth-Schnatter, Sylvia
AU - Lopes, Hedibert Freitas
PY - 2016/2/24
Y1 - 2016/2/24
N2 - 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.
AB - 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.
U2 - 10.57938/286b27c0-d49f-4318-851c-690a2ddd7c30
DO - 10.57938/286b27c0-d49f-4318-851c-690a2ddd7c30
M3 - WU Working Paper
T3 - Research Report Series / Department of Statistics and Mathematics
BT - Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
PB - WU Vienna University of Economics and Business
CY - Vienna
ER -