We discuss the development of dynamic factor models for multivariate financial 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 specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data, and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting and sequential portfolio allocation for a selected set of international exchange rate return time series. Our goals are to understand a range of modelling questions arising in using these factor models, and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models, and on future potential model extensions.

Keywords:

Dynamic Factor Analysis; Dynamic Linear Models; Exchange Rates Forecasting; Markov Chain Monte Carlo; Multivariate Stochastic Volatility; Portfolio Selection; Sequential Forecasting; Variance Matrix Discounting.

The research reported here was partially supported by NSF grants DMS-9704432 and DMS-9707914. The authors acknowledge useful discussions with Jose M Quintana of CDC Investment Management Corporation, Neil Shephard and Hong Chang.

The manuscript is available in either postscript or pdf