The authors assess the impact of US short-term interest rates on commodity prices using a quarterly data set running from 1957 to 2008. They apply a structural vector autoregression (SVAR) model to assess the impact of changes in monetary policy on commodity prices. The SVAR model is based on a matrix polynomial with such variables as GDP, consumption, and the exchange rate. The model forecasts changes in commodity prices (impulse response function) for several commodity indexes. The change in price for fuel-related commodities turns out to be quicker than for non-fuel-related commodities, perhaps because fuel-related commodities are more financialized and globalized. There is also a significant impact on the prices of non-fuel-related commodities, although a lag might apply for these assets.

Previous research on the relationship between monetary policy and commodity prices has been from either a theoretical or an empirical point of view. The theoretical view starts with the nonarbitrage assumption, which implies that the change in commodity prices minus storage costs must equal short-term interest rates. Higher interest rates reduce the demand and increase the supply of commodities.

The empirical research has mainly been focused on the relationship between monetary policy and asset prices. A price change in the latter might influence monetary policy to avoid inflation. The relationships among global liquidity, inflation, and commodity prices have also been researched. Although previous research has led to a variety of findings, there seems to be agreement that commodities should be classified in several subsets.

Because they focus on the effect of US monetary policy on commodity prices during the period 1957–2008, the authors exclude the effects of quantitative easing. They use the federal funds rate as a proxy for monetary policy. By using a vector autoregressive model with several endogenous variables (e.g., GDP and the exchange rate), they assess the impact of interest rates on the commodity price. Also, some variables that are expected to react to a change in interest rates with a delay are included with a lag. The International Monetary Fund (IMF) commodity data include the broad commodity price index (both fuel and nonfuel); the nonfuel commodity price index; and the price indexes for food, beverages, agricultural raw materials, metals, and fuel. Macroeconomic data are also derived from the IMF data set. The authors apply a counterfactual series and an econometrical model to verify their results. Finally, they determine whether their conclusions are robust in an environment of quantitative easing.

The authors derive their empirical results using the SVAR model, estimate the median response, and create confidence intervals to assess the variety surrounding the results. They show that a contractionary shock results in a reduction in GDP, consumption, and investment, although all occur with a lag. Furthermore, the price level and liquidity decrease.

Next, they assess the impact of a monetary shock on subsets of the commodity index. They find that food prices are very sensitive to changes in interest rates, whereas beverage prices are less sensitive, perhaps because of the lower price elasticity of food compared with that of beverages. Agricultural materials turn out to be less sensitive than metals, and fuel and energy prices react very strongly to changes in interest rates. The latter might be increased because of speculation in this sector.

The authors then apply a counterfactual series model to assess the impact of shocks in interest rates on commodity prices in different time periods. When interest rates are high, there seems to be another unexpected component. The authors conclude that the monetary expansion between 2001 and 2008 resulted in an increase in metal and energy prices but the effect on other commodities was modest.

To capture four factors of the economy, the authors use an econometric model. They use an identification scheme in which the aggregate commodity index is replaced by a commodity subindex to assess its significance. This method yields comparable results; fuel prices react more strongly than nonfuel prices to changes in interest rates.

They also assess the interaction between interest rates and commodity futures prices using a Datastream international dataset from the first quarter of 1990 onward. Although an increase in interest rates is directly reflected in the overall commodity index, commodity subsets react differently depending on price elasticity, liquidity, and how markets are financialized.

Finally, the authors assess the impact of unexpected monetary policy during the quantitative easing period of 2008–2009. Again, fuel- and energy-related commodities react most heavily to changes in monetary policy.

Investments in commodities have been a profitable venture with the emergence of the BRICs (Brazil, Russia, India, and China). But apart from their speculative nature, commodities have been an interesting alternative asset to provide diversification benefits or to serve as a hedge for inflation. Nevertheless, the interaction between commodity prices and monetary policy has been rather blurred because of the recent popularity of commodities. The authors perform their research in a very well-organized manner and apply several methods to test their conclusions. Because commodities are an interesting investment from speculative and diversification points of view, this article will appeal to a wide range of investment professionals.