Bayesian Dynamic Factor Models and Variance Matrix Discounting for Portfolio Allocation

We discuss the development of dynamic factor models for multivariate nancial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalisations of univariate stochastic volatility models, and represent speci c varieties of models recently discussed in the growing multivariate stochastic volatility literature. We also discuss connections and comparisons with the much simpler method of dynamic variance discounting that, for over a decade, has been a standard approach in applied nancial econometrics in the Bayesian forecasting world. We review empirical ndings in applying these models to the exchange rate series, including aspects of model performance in dynamic portfolio allocation. We conclude with comments on the potential practical utility of structured factor models and future potential developments and model extensions.