TY - GEN

T1 - Analysis of exchange rates via multivariate Bayesian factor stochastic volatility models

AU - Kastner, Gregor

AU - Frühwirth-Schnatter, Sylvia

AU - Lopes, Hedibert F.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-319-02084-6__35

DO - 10.1007/978-3-319-02084-6__35

M3 - Conference contribution

AN - SCOPUS:84893471341

SN - 9783319020839

T3 - Springer Proceedings in Mathematics and Statistics

SP - 181

EP - 185

BT - The Contribution of Young Researchers to Bayesian Statistics - Proceedings of BAYSM 2013

T2 - 1st Bayesian Young Statistician Meeting, BAYSM 2013

Y2 - 5 June 2013 through 6 June 2013

ER -