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.
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References
Chib S, Nardari F, Shephard N (2006) Analysis of high dimensional multivariate stochastic volatility models. J. Econom 134:341–371
Kastner G (2013) stochvol: Efficient Bayesian inference for stochastic volatility (SV) models. R package version 0.6-1
Kastner G, Frühwirth-Schnatter S (forthcoming) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models. Comput Stat Data An doi: 10.1016/j.csda.2013.01.002
McCausland WJ, Miller S, Pelletier D (2011) Simulation smoothing for state-space models: a computational efficiency analysis. Comput Stat Data An 55:199–212
Yu Y, Meng X-L (2011) To center or not to center: that is not the question—an ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC efficiency. J Comput Graph Stat 20:531–570
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Kastner, G., Frühwirth-Schnatter, S., Lopes, H.F. (2014). Analysis of Exchange Rates via Multivariate Bayesian Factor Stochastic Volatility Models. In: Lanzarone, E., Ieva, F. (eds) The Contribution of Young Researchers to Bayesian Statistics. Springer Proceedings in Mathematics & Statistics, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-319-02084-6_35
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DOI: https://doi.org/10.1007/978-3-319-02084-6_35
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