The authors decompose foreign exchange (FX) volatility in the US stock market into short- and long-run components by comparing various asset-pricing models. Their findings suggest that it is the long-run aspect of FX volatility that actually has value for asset pricing and is a statistically significant Merton factor in computing that pricing. This research contributes to a new way of looking at the state variables underlying the Fama–French–Carhart factors and has useful implications for international finance as well.

The authors’ evidence supports their hypothesis that the long-run component of FX volatility does have power to explain the cross section of stock returns in the US market. Accordingly, this evidence presents useful information for anyone interested in international finance or empirical asset pricing. For international finance, the research results encourage a stronger emphasis on the second moment of exchange rates when examining the links between FX and equity markets. For empirical asset pricing, the authors’ findings cast a clearer light on the state variables that underlie the Fama–French–Carhart factors.

Being able to reduce noise when assessing volatility and determining asset pricing has considerable value in improving the efficacy of models for researchers and practitioners. If only the long-run component of FX volatility truly matters for asset pricing and has the power to explain the cross section of stock returns, as the authors’ research suggests, then it is helpful to know where to focus attention and what to ignore when conducting analysis.

The authors’ sample includes 34 countries and daily exchange rate data from the Federal Reserve Bank of St. Louis for January 1971, when daily exchange rate data became available, through December 2012. They obtain monthly equity-market data from Kenneth French’s website and CRSP.

The authors determine FX volatility by computing the absolute daily log return for each country on each day in the sample, averaging over all currencies available on any given day and averaging daily values within any given month. They then decompose the FX volatility into short- and long-run components using the Hodrick–Prescott methodology. They also construct volatility innovations by taking the first differences of the FX volatility and its components and by constructing factor-mimicking portfolios.

Four asset-pricing models are compared in order to assess whether the long-run component of FX volatility has the power to help explain the cross section of stock returns: (1) the standard capital asset pricing model (CAPM), (2) the CAPM enhanced with FX volatility, (3) a three-factor model that includes both short- and long-run components of FX volatility, and (4) a two-factor model that incorporates only the long-run component of FX volatility. The evidence suggests that it is the long-run component of FX volatility that has the power to helpb explain the cross section of stock returns in the US market. The authors conduct several robustness checks, all of which bolster the central finding of their research. The long-run component of FX volatility appears to explain approximately 15% of the mean excess returns in the stock portfolios tested.

The authors present their research in a clear, concise, and systematic way that seems compelling. Being able to filter out noise is always useful in constructing asset-pricing models. The results of their tests suggest various practical implications in the areas of asset pricing and international finance. It would be interesting to see where the results of this research could lead.