Kastner, Gregor, Frühwirth-Schnatter, Sylvia, Lopes, Hedibert Freitas. 2014. Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models. In The Contribution of Young Researchers to Bayesian Statistics, Proceedings of BAYSM2013, Springer Proceedings in Mathematics & Statistics, Vol. 63, Hrsg. Ettore Lanzarone and Francesca Ieva, 181-185. Switzerland: Springer.

RIS

@INPROCEEDINGS{Kastner2014,
title = {Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models},
author = {Gregor Kastner and Sylvia Frühwirth-Schnatter and Hedibert Freitas Lopes},
publisher = {Springer},
year = {2014},
isbn = {978-3-319-02083-9},
address = {Switzerland},
url = {http://link.springer.com/book/10.1007%2F978-3-319-02084-6},
publisher = {Springer},
language = {EN},
pages = {181-185},
series = {The Contribution of Young Researchers to Bayesian Statistics, Proceedings of BAYSM2013, Springer Proceedings in Mathematics & Statistics, Vol. 63},
abstract = {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.},
}