Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Gregor Kastner, Sylvia Frühwirth-Schnatter and Hedibert Freitas Lopes

No 234, Business and Economics Working Papers from Unidade de Negocios e Economia, Insper

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, 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.

Pages: 29 pages
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://repositorio.insper.edu.br/handle/11224/5894 Full text (text/html)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
Working Paper: Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aap:wpaper:234

Ordering information: This working paper can be ordered from
https://repositorio. ... br/handle/11224/5894

Access Statistics for this paper

More papers in Business and Economics Working Papers from Unidade de Negocios e Economia, Insper Contact information at EDIRC.
Bibliographic data for series maintained by Biblioteca Telles ().