Gregor Kastner (WU Vienna University of Economics and Business): Analysis of Multivariate Financial Time Series via Bayesian Factor Stochastic Volatility Models

In recent years, multivariate factor stochastic volatility (SV) models have been increasingly used to analyze high-dimensional financial and economic time series because they can pick up the joint volatility dynamics by a small number of latent time-varying factors. The main advantage of such a model is its parsimony; all variances and covariances of a time series vector are governed by a low-dimensional common factor with the components following independent SV models. For problems of this kind, MCMC is a very efficient estimation method; nevertheless, it is associated with a considerable computational burden when the number of assets is moderate to large. To overcome this, we avoid the usual forward-filtering backward-sampling (FFBS) algorithm by sampling the latent states “all without a loop” (AWOL), consider various reparameterizations such as (partial) non-centering, 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. Moreover, we use modern supercomputing architecture for parallel implementation. Our algorithm is designed in a way such that existing software crafted for efficient Bayesian univariate SV estimation can easily be incorporated. Finally, to show the effectiveness of our approach, we apply the model to a vector of daily returns.

Joint work with Sylvia Frühwirth-Schnatter and Hedibert F. Lopes