Forecasting Commodity Prices

With commodity prices exhibiting wide fluctations over the past few years, it’s no wonder that many are interested in determining what procedure best forecasts. A recent New York Fed blog post by Jan Groen and Paolo Pesenti tackles this issue. In a horse race between various economic, time series, and futures-based approaches…

there is no obvious winner. Information from large panels of global economic variables can help, but their forecasting properties are by no means overwhelming. It all depends on the choice of the specific index and the forecasting horizon. …

…For example, for one specific commodity price index, PLS regressions provide significantly better predictions than both autoregressive and random walk benchmarks when used to forecast one-month and one-quarter-ahead commodity prices. But when the forecasting horizon is six months or longer, the forecast performance of PLS regressions is no better than the statistical benchmarks. PLS does perform relatively better with aggregate commodity price indexes than with commodity subindexes such as metals or energy.

If we focus on specific subsets of explanatory variables—as emphasized by the “true believers”—we do find some, but not overwhelming, evidence for the notion that commodity currencies are useful predictors. We find even less empirical support for the notion that commodity futures have strong predictive power.

Ultimately, the basic message is one of inconclusiveness. No easy generalization or pattern emerges, and the results look almost random. In fact, we are unable to generate forecasts that are, on average, more accurate and robust than those based on autoregressive or random walk specifications. If a policy lesson can be drawn from our results, it is that one should be very cautious when interpreting the forecast of a forthcoming commodity price surge as an early signal of recrudescence in global headline inflation. As forecasts of commodity prices provide only highly noisy hints about their actual future trajectories and persistence, excessive confidence in such forecasts may bias policymakers’ views and beliefs about future inflation risks in the direction of a premature—and unwarranted—tightening of the global policy mix.

…

The paper upon which the study is based is here.

This study provides an interesting complement to a 2010 study Oli Coibion and I wrote (post here). The Groen-Pesenti study pertained to commodity price indices, while our study pertained to spot prices of individual commodities. We concluded:

Commodity prices have long played an important role in accounting for economic fluctuations. Forecasting changes in commodity prices is therefore an important component for forwardlooking policy-makers. The growing use of futures markets has raised the question of how much information these prices incorporate about future movements in spot prices. We show that while energy futures can adequately be characterized as unbiased predictors of future spot prices, there is much stronger evidence against the null of unbiasedness for other commodities, especially once one explicitly takes into account time-varying heterogeneity in shocks variances. In part, this failure of futures markets for many commodities to satisfy the unbiasedness hypothesis likely reflects the fact that these markets suffered from only light trading volumes. In recent years, as the depth of these markets has increased, we find much weaker evidence against the null of unbiasedness. In addition, futures prices have frequently outperformed random walk predictions since 2003, despite substantial and protracted price changes, and vastly outperform reduced form statistical models of price changes. This result leads us to be cautiously optimistic about the broader use of futures prices as predictors of subsequent spot price movements, particularly for those markets which continue to be actively used by a wide range of financial and real market participants.

This suggests to me one reason why futures might have differing predictive power relative to other approaches, for certain commodity or commodity classes. But that’s just a conjecture.

As an aside, I find it amusing to repeat some conclusions Yin-Wong Cheung, Antonio Garcia Pascual and I came to in our 2005 JIMF paper, regarding exchange rate models:

We re-assess exchange rate prediction using a wider set of models that have been proposed in the last decade: interest rate parity, productivity based models, and a composite specification. The performance of these models is compared against two reference specifications e purchasing power parity and the sticky-price monetary model. The models are estimated in first-difference and error correction specifications, and model performance evaluated at forecast horizons of 1, 4 and 20 quarters, using the mean squared error, direction of change metrics, and the “consistency” test of Cheung and Chinn [1998. Integration, cointegration, and the forecast consistency of structural exchange rate models. Journal of International Money and Finance 17, 813-830]. Overall, model/specification/currency combinations that work well in one period do not necessarily work well in another period.

Forecasting Commodity Prices

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