Exchange Rates Can Forecast Commodity Prices

07/01/2008
Featured in print Digest

Given that commodity prices are extremely volatile and difficult to predict -- and that commodity price futures are notoriously bad predictors of future commodity prices -- this new approach to predicting commodity prices has important potential practical value.

A recent study by co-authors Yu-Chin Chen, Kenneth Rogoff, and Barbara Rossi -- Can Exchange Rates Forecast Commodity Prices? (NBER Working Paper No. 13901) -- demonstrates that exchange rates can be used to help predict commodity prices. This is a quite a surprising and "out of the box" result, Rogoff points out, but it flows naturally from the fact that exchange rates are asset prices that embody expectations of future movements in macroeconomic fundamentals. Given that commodity prices are extremely volatile and difficult to predict -- and that commodity price futures are notoriously bad predictors of future commodity prices -- this new approach to predicting commodity prices has important potential practical value, the authors argue.

The authors also uncover some evidence that commodity prices help to predict exchange rates, but the evidence is much weaker -- the reverse forecasting regression does not survive out-of-sample testing. They argue, however, that it is quite plausible that exchange rates will be better predictors of exogenous commodity prices than vice-versa, because the exchange rate is fundamentally forward looking, whereas asset prices tend to be very sensitive to small perturbations in current demand or supply.

The basic point -- that forward looking asset price models can be inverted to predict fundamentals -- has been developed in earlier papers by Campbell and Shiller (1987) and Engel and West (2005). Those earlier efforts, however, were stymied by the fact that the fundamental variables being used (for example savings, interest rates, outputs, money supplies) are themselves endogenous, making it difficult to draw any structural inferences. In contrast, world commodity prices for the exports of certain small countries can legitimately be considered independent of their exchange rates, making the commodity currencies an ideal testing lab.

In their paper, Chen, Rogoff, and Rossi analyze quarterly data, gathered over one to three decades, relevant to the "commodity currencies" of Australia, Canada, New Zealand, South Africa, and Chile. These countries produce a variety of primary commodity products, from agricultural and mineral to energy-related goods. Together, commodities represent from one quarter to well over one half of each of these countries' export earnings.

Each of the five countries has a long history of market-based floating exchange rates. Because they are relatively small players in the overall global commodity market, these countries are "price takers" for the vast majority of their commodity exports. As such, global commodity-price fluctuations serve as easily observable terms-of-trade shocks to these countries' exchange rates and affect a significant share of their exports.

For each country, the researchers aggregated the relevant dollar spot prices in world commodity markets to construct country-specific, export-earnings-weighted commodity price indexes. In addition to dollar rates, the authors also considered cross rates relative to the Japanese yen and the British pound as a robustness check. In addition, they used the IMF's "All Commodities Index" -- a world export earnings-weighted price index for over 40 commodities traded on various exchanges -- in U.S. dollars to measure movements in the overall aggregate world commodity markets.

Chen, Rogoff, and Rossi add that their results are sufficiently robust to be applied to alternative benchmark currencies, forecast combinations, and long-horizon predictions. "One might eventually extend the approach," they suggest, "to look at countries that have few or no commodities, such as most of Asia, to see if commodity prices affect the value of their currencies, and if their currency fluctuations may offer predictive power for, say, oil prices."

-- Matt Nesvisky