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We discuss Bayesian forecasting of increasingly high‐dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business. Novel state‐space models characterizing sparse patterns of dependence among multiple time series extend existing multivariate volatility models to enable scaling to higher numbers of individual time series. The theory of these  dynamic dependence network  models shows how the individual series can be  decoupled  for sequential analysis and then  recoupled  for applied forecasting and decision analysis. Decoupling allows fast, efficient analysis of each of the series in individual univariate models that are linked – for later recoupling – through a theoretical multivariate volatility structure defined by a sparse underlying graphical model. Computational advances are especially significant in connection with model uncertainty about the sparsity patterns among series that define this graphical model; Bayesian model averaging using discounting of historical information builds substantially on this computational advance. An extensive, detailed case study showcases the use of these models and the improvements in forecasting and financial portfolio investment decisions that are achievable. Using a long series of daily international currencies, stock indices and commodity prices, the case study includes evaluations of multi‐day forecasts and Bayesian portfolio analysis with a variety of practical utility functions, as well as comparisons against commodity trading advisor benchmarks. Copyright © 2016 John Wiley & Sons, Ltd.

Dynamic dependence networks: Financial time series forecasting and portfolio decisions