  The Black-Litterman model is a framework for incorporating forward-looking
expert views in a portfolio optimization problem. Existing work focuses almost
exclusively on single-period problems and assumes that the horizon of expert
forecasts matches that of the investor. We consider a multi-period
generalization where the horizon of expert views may differ from that of a
dynamically-trading investor. By exploiting an underlying graphical structure
relating the asset prices and views, we derive the conditional distribution of
asset returns when the price process is geometric Brownian motion. We also show
that it can be written in terms of a multi-dimensional Brownian bridge. The new
price process is an affine factor model with the conditional log-price process
playing the role of a vector of factors. We derive an explicit expression for
the optimal dynamic investment policy and analyze the hedging demand associated
with the new covariate. More generally, the paper shows that Bayesian graphical
models are a natural framework for incorporating complex information structures
in the Black-Litterman model.
