[This paper provides a comprehensive evaluation of the short-horizon predictive ability of economic fundamentals and forward premiums 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: (1) 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 that conditions on the forward premium with stochastic volatility innovations and (2) strategies based on combined forecasts yield large economic gains over the random walk benchmark. These two results are robust to reasonably high transaction costs.]

We show that a global imbalance risk factor that captures the spread in countries' external imbalances and their propensity to issue external liabilities in foreign currency explains the cross-sectional variation in currency excess returns. The economic intuition is simple: net debtor countries offer a currency risk premium to compensate investors willing to finance negative external imbalances because their currencies depreciate in bad times. This mechanism is consistent with exchange rate theory based on capital flows in imperfect financial markets. We also find that the global imbalance factor is priced in cross-sections of other major asset markets.

We study a large currency cross-section using asset pricing methods that account for omitted-variable and measurement-error biases. First, we show that the pricing kernel includes at least three latent factors that resemble (but are not identical to) a strong U.S. “dollar” factor and two weak high Sharpe ratio “carry” and “momentum” slope factors. Evidence for an additional “value” factor is weaker. Second, using this pricing kernel, we find that only a small fraction of the over 100 nontradable candidate factors considered have a statistically significant risk premium, mostly relating to volatility, uncertainty, and liquidity conditions, rather than macro variables.

This paper provides a comprehensive evaluation of the short-horizon predictive ability of economic fundamentals and forward premiums 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: (1) 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 that conditions on the forward premium with stochastic volatility innovations and (2) strategies based on combined forecasts yield large economic gains over the random walk benchmark. These two results are robust to reasonably high transaction costs. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.

We show that a global imbalance risk factor that captures the spread in countries’ external imbalances and their propensity to issue external liabilities in foreign currency explains the cross-sectional variation in currency excess returns. The economic intuition is simple: net debtor countries offer a currency risk premium to compensate investors willing to finance negative external imbalances because their currencies depreciate in bad times. This mechanism is consistent with exchange rate theory based on capital flows in imperfect financial markets. We also find that the global imbalance factor is priced in cross-sections of other major asset markets.

We assess the properties of currency value strategies based on real exchange rates. We find that real exchange rates have predictive power for the cross-section of currency excess returns. However, adjusting real exchange rates for key country-specific fundamentals (productivity, the quality of export goods, net foreign assets, and output gaps) better isolates information related to the currency risk premium. In turn, the resultant measure of currency value displays considerably stronger predictive power for currency excess returns. Finally, the predictive information content in our currency value measure is distinct from that embedded in popular currency strategies, such as carry and momentum.

We investigate the information contained in foreign exchange (FX) volume using a novel data set from the over-the-counter market. We find volume helps predict next-day currency returns and is economically valuable for currency investors. Predictability implies a stronger return reversal for currency pairs with abnormally low volume and is driven by the component of volume unrelated to volatility, liquidity, and order flow. We rationalize these findings via a simple model, in which FX volume helps reveal the degree of asymmetric information in currency markets. Testing this prediction shows that asymmetric information is uniform across currency pairs but varies across instruments.