Over the last few months, commodity prices have surged across the board. As could be seen from the charts below showcasing the performance of the S&P GSCI index and related subindexes, commodity prices have been soaring, whether you consider Energy commodities, Grains and other Agricultural commodities, Industrial Metals or Precious Metals. While oil prices and the broader energy basket have rebounded from their abrupt Covid-19 slump witnessed a year ago, the price surge has been particularly impressive for agricultural commodities and precious metals.
If we put this rally in historical perspective. We can see that the price surge of industrial metals has started well before 2020 – somewhere around Q1 2019 – and has continued unabated throughout much of the COVID-19 crisis, before reaching a peak in July 2020 , declining from there on. This hints to the fact that industrial metals like iron ore and copper have been in high demand before the outbreak of the pandemic. The global trade war initiated by the Trump Administration has triggered panic buying and hoarding of copper and other industrial commodities by Chinese companies. The price surge in industrial metals could also be related to other political and geopolitical factors such as the repeated strikes and power shortages that have stymied South Africa’s mining sector or the backlash against global mining companies in the Democratic Republic of Congo.
There is an extensive literature dedicated to understanding commodity prices and their determinants – both in the short run and in the long run. These determinants or price drivers might be ranged into three categories: microeconomic drivers, macroeconomic factors and monetary/financial catalysts. As with previous episodes of price surges, the 2020-2021 commodity rally cannot be explained simply by referring it to one set of factors. It owes much to an evolving combination of factors that, so far, have been reinforcing each other and that have kept the rally going.
The relationship between oil prices and the prices of other commodities
Before figuring out how long this upward leg – shall we call it a bubble? – could go before eventually losing its momentum and reversing its course – there will always be some money mongers in the room saying “this time is different”, but these people are either trying to sell something or they are riding the bubble and have an interest to convince other to do alike, in order to keep it going -, it is important to understand how different commodity prices are related to each other, and how they interact as part of what one might call “the commodity compound”.
There is a strong causal link between energy prices and the prices of non-energy commodities as the production process for the latter relies at varying extents – sometimes heavily, as in the case of steel and aluminium – on the former as an input. The chart below reproduced from a study published by IPFRI in 2010 to analyze the food price surge observed in 2008 illustrates very well the complexity of commodity price formation.
The causality goes from oil prices to the prices of other commodities, but the relationship is not as straightforward as one might think. A linear regression taking daily returns on the GSCI Grains Index as the dependent variable and GSCI Energy Returns as the regressor or independent variable yields a positive and statistically significative “beta”. But its explanatory power is rather weak as can be seen from the chart below. The same could be said for the relationship between Energy prices and the prices of Industrial Metals. Therefore, there are many idiosyncratic factors that should be taken into account when studying the price formation of a given commodity. On the other hand, there are also macroeconomic risks to which all the commodities are exposed.
Macroeconomic drivers of commodity prices
Moving to macroeconomic drivers, we examine the relationship between the yearly change of commodity prices – measured at the monthly frequency – and the deviation of yearly global growth from its long term trend, as proxied by our monthly proprietary Global Business Cycle indicator, which is computed from country-based BC indicators for all the G20 economies. A scatterplot shows that there is indeed a statistically significant relationship between the two variables.
We normalise the variables and we perform a linear regression of the former on the latter over the 2000-2021 period with a monthly frequency.
The R2 coefficient of the regression stands around 38% which is quite good. In other words, the global business cycle explains one third of the yearly variation of commodity prices. In this regard, our global business cycle indicator is currently indicates a strong global recovery momentum, despite the new coronavirus surges faced by some major G20 economies (i.e. India, Brazil) and the uneven roll out of vaccinations across different countries.
Adding the US dollar to the equation significantly enhances the explanatory power of the regression as it jumps to 67%. Hence, our results indicate that two thirds of the yearly variation of monthly averaged commodity prices might be explained by the combined effect of the global business cycle and the US dollar.
We have performed dozens other regressions to test the marginal effect of other variables. Replacing the Global BC indicator by the combination of a China Output Factor and a US Output factor, alongside the US dollar improves the overall explanatory power of the regression; The adjusted R2 jumps to 75%. The addition of market-based real interest rates (i.e. 5-years TIPS rates) as an explanatory variables increases the overall explanatory power of the regression but the related coefficient is not statistically significant.