We discuss dynamic factor modeling of financial time series using a latent threshold approach to factor volatility. This approach models time-varying patterns of occurrence of zero elements in factor loadings matrices, providing adaptation to changing relationships over time and dynamic model selection. We summarize Bayesian methods for model fitting and discuss analyses of FX and commodities time series. Empirical results show that interpretable, data-driven dynamic sparsity can reduce estimation uncertainties, improve predictions and portfolio performance.
Keywords: Bayesian forecasting; Latent threshold dynamic models; Multivariate stochastic volatility; Portfolio allocation; Sparse time-varying loadings; Time-varying variable selection.
The research reported here was partly supported by a grant from the National Science Foundation [DMS-1106516 to M.W.]. Any opinions, findings and conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the NSF.