Analysis of exchange rates via multivariate Bayesian factor stochastic volatility models

Gregor Kastner, Sylvia Frühwirth-Schnatter, Hedibert F. Lopes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Multivariate factor stochastic volatility (SV) models are increasingly used for the analysis of multivariate financial and economic time series because they can capture the volatility dynamics by a small number of latent factors. The main advantage of such a model is its parsimony, as the variances and covariances of a time series vector are governed by a low-dimensional common factor with the components following independent SV models. For high-dimensional problems of this kind, Bayesian MCMC estimation is a very efficient estimation method; however, it is associated with a considerable computational burden when the dimensionality of the data is moderate to large. To overcome this, we avoid the usual forward-filtering backward-sampling (FFBS) algorithm by sampling "all without a loop" (AWOL), consider various reparameterizations such as (partial) noncentering, and apply an ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation at a univariate level, which can be applied directly to heteroskedasticity estimation for latent variables such as factors. To show the effectiveness of our approach, we apply the model to a vector of daily exchange rate data.

Original languageEnglish (US)
Title of host publicationThe Contribution of Young Researchers to Bayesian Statistics - Proceedings of BAYSM 2013
Number of pages5
StatePublished - 2014
Externally publishedYes
Event1st Bayesian Young Statistician Meeting, BAYSM 2013 - Milan, Italy
Duration: Jun 5 2013Jun 6 2013

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Conference1st Bayesian Young Statistician Meeting, BAYSM 2013

ASJC Scopus subject areas

  • General Mathematics


Dive into the research topics of 'Analysis of exchange rates via multivariate Bayesian factor stochastic volatility models'. Together they form a unique fingerprint.

Cite this