Dynamic covariance estimation using sparse Bayesian factor stochastic volatility models

We address the "curse of dimensionality" arising in time-varying covariance estimation by modeling the underlying volatility dynamics of a time series vector through a lower dimensional collection of latent dynamic factors. Furthermore, we apply a Normal-Gamma shrinkage prior to the elements of the factor loadings matrix, thereby increasing parsimony even more. Estimation is carried out via MCMC in order to obtain draws from the high-dimensional posterior and predictive distributions. To guarantee efficiency of the samplers, we utilize several ancillarity-sufficiency interweaving strategies (ASIS) for sampling the factor loadings. Estimation and forecasting performance is evaluated for simulated and real-world data.