This paper provides a comprehensive evaluation of the short-horizon predictive ability of economic fundamentals and forward premia on monthly exchange rate returns in a framework that allows for volatility timing. We implement Bayesian methods for estimation and ranking of a set of empirical exchange rate models, and construct combined forecasts based on Bayesian Model Averaging. More importantly, we assess the economic value of the in-sample and out-of-sample forecasting power of the empirical models, and find two key results (i) a risk averse investor will pay a high performance fee to switch from a dynamic portfolio strategy based on the random walk model to one which conditions on the forward premium with stochastic volatility innovations; and (ii) strategies based on combined forecasts yield large economic gains over the random walk benchmark. These two results are robust to reasonably high transaction costs.