Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

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

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. More... »

PAGES

181-185

Book

TITLE

The Contribution of Young Researchers to Bayesian Statistics

ISBN

978-3-319-02083-9
978-3-319-02084-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-02084-6_35

DOI

http://dx.doi.org/10.1007/978-3-319-02084-6_35

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042509907


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