Dynamic Factor Volatility Modeling: A Bayesian Latent Threshold Approach

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 several FX, commodities, and stock price index time series. Empirical results show that the latent threshold approach can define interpretable, data-driven, dynamic sparsity, leading to reduced estimation uncertainties, improved predictions, and portfolio performance in increasingly high-dimensional dynamic factor models.