Macro Research
SUMMARY
Mineral producers, policy makers, and investment managers are all trying to better understand commodity prices to make more informed , long-term decisions. This statistical methodology is one of many possible tools that could help support the decision-making process and provide a framework to discuss super cycles in commodities and lend itself to other macroeconomic and financial questions.
To borrow a phrase once used about business cycles, it can be said that "the study of [super] cycles necessarily begins with the measurement of [super] cycles" (adapted from Baxter and King, 1999). This was the lead quote in a 2008 International Monetary Fund journal article introducing the concept of statistically measuring super cycles. Dr. John Cuddington and Dr. Daniel Jerrett utilized band-pass filters to isolate cycles in commodity prices at varying frequencies. With the current re-emergence of the super-cycle discussion, it seems timely to revisit the early work in the field and update the analysis to help inform the current conversation.
In early 2005, former Citigroup Director, Alan Heap, declared that “a super cycle is underway, driven by material intensive economic growth in China” (Heap, 2005). Heap’s analysis suggested that super cycles have two unique features: 1. Prolonged cycles with expansions of roughly 10-35 years (suggesting full cycles of 20-70 years) and 2. Broad-based, affecting a wide range of commodities. Heap said that there had been three super cycles since the late 1800s occurring during the U.S. industrialization, post-war reconstruction in Europe followed by Japan, and the industrialization and urbanization of China beginning in the early 2000s. All demand driven.
It is well known that short-to-medium run supply conditions in commodity markets can be quite constrained due to capacity and time to bring new deposits online. Therefore, it should be evident that a large demand shock such as above-trend economic growth in China could create a supply-demand imbalance that results in large, sustained price increases as seen in the early 2000s.
Cuddington and Jerrett took an agnostic view of super cycles and used a series of statistical techniques to document facts around commodity price behavior. The super cycle, as defined by Heap, had a complete cyclical frequency of 20-70 years. Band-pass filters were used to extract cyclical components as well as the long-run trend from a 150 year data set of real metals prices. The filtering technique found evidence supporting the hypothesis that three super cycles had occurred over the past 150 years and the amplitude of the super cycles was large with variations of 20 to 40 percent above and below the long-run trends. Figure 1 shows the super-cycle component for the real price of copper from the original 2008 analysis.
In addition to extracting the super-cycle component of copper, the analysis was performed on a broader group of metals. Simple correlations of the super-cycle components were large and statistically significant in most cases. Principal component analysis (PCA) was used to measure the amount of co-movement in the group of metals. If a super cycle is being driven by broad-based economic growth, one would expect to see commodity prices moving together. PCA can be used to measure the importance of unobservable common factors affecting the super-cycle components. The first principal component explained 66 percent of the overall joint co-variation in the six metal super-cycle components. It is left to the analyst to then correlate the unobserved factor with something that could be driving prices. In this case, it was assumed that the first principal component was highly correlated with global real GDP, supporting Heap’s hypothesis that super-cycles are driven, in-part, by periods of above-trend economic growth and industrialization and urbanization.
The analysis was extended to look at iron ore, steel, and molybdenum (Jerrett and Cuddington 2008) and to oil prices (Zellou and Cuddington 2012). Super cycles were found to occur during similarly defined time periods in both studies, further supporting the super-cycle hypothesis.
There have been discussions in the past few months regarding commodity prices and the possibility of entering a new super cycle driven, in part, by the ongoing move to green technology as well as supply constraints from a decade of under investment in exploration and production. In addition, many commodities have seen recent price increases which could be a combination of economic recovery from the Coronavirus pandemic, current, low inventories in many commodities, and a weak U.S. dollar. The question is, are these current market forces transitory and more importantly, is the forthcoming technological change a large enough structural driver to affect commodity markets in a similar way to prior super cycles?
No two super cycles are alike and one could assue that with declining commodity intensity in many countries, the continued energy transition, and demographics becoming a headwind for any parts of the work, the look and feel of super cycles and the associated amplitude may be quite different across the entire commodity complex and within individual commodities.
The original statistical analysis was updated through 2020 to determine where the current super cycle is relative to long-run trends. This can help inform the current discussion of whether a new super cycle is emerging. Figure 2 shows the super-cycle component of the real price of copper through the end of 2020. The super-cycle component peaked in 2014, but still remains above its long-run trend suggesting we may still be in the tail end of the super cycle that began in the early 2000s or possibly a new cycle is emerging?
The statistical methodology offers the flexibility to isolate any cyclical frequency from a time series. In addition to isolating the super cycle component, both the intermediate component (8-20 years) and the business-cycle (2-8 years) component can be extracted using band-pass filters.
The intermediate cycle of 8-20 years correlates well with the investment cycle that many commodity producers experience. The timing of this cycle can have impacts on the super-cycle discussion. Using copper as an example, the super-cycle component is still above trend, albeit declining. The intermediate cycle reached a trough in 2017 after a decade of declining, and subsequently, a decade of little to no investment in production and exploration. Figure 3 shows the intermediate cycle for the real price of copper.
LME copper inventories in late 2020 were the lowest since 2007. This corresponds to the timing of significant price increases in the fourth quarter of 2020. With China experiencing early signs of an economic recovery, the supply shortage could keep upward pressure on copper prices in the intermediate term. The question is does this represent the emergence of a new super cycle or is the result of under investment in the industry that will correct in the coming years?
The current discussion of the ongoing transition to green technology does represent a source of continued demand for decades to come. The commitment by many nations to be carbon neutral and less energy intensive by 2050-2060 requires significant infrastructure investment which will be commodity intensive. Structural models of commodity prices have shown that at each major stage of economic development: agricultural, industrial, and service, commodity usage can change, increasing the likelihood of a super cycle in early stages of development. This time may be no different, but raises an interesting question -is the global economy entering a new phase of economic development and technological change that will lead to an increase in material intensity?
Cuddington and Jerrett did not set out to prove or disprove the existence of super cycles in their original 2008 paper. Rather, they wanted to introduce a framework that could inform a broader discussion about long-run price movements and develop a peer-reviewed, statistical methodology to support it. Before one can conjecture about the existence of any economic phenomenon, one must be able to measure it.
The reemergence of the super-cycle discussion has important implications for the global economy and capital markets. Mineral producers, policy makers, and investment managers are all trying to better understand commodity prices to make more informed , long-term decisions. This statistical methodology is one of many possible tools that could help support the decision-making process and provide a framework to discuss super cycles in commodities and lend itself to other macroeconomic and financial questions.
Baxter, Marianne, and Robert G. King. "Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series." Review of Economics and Statistics 81, no. 4 (1999): 575-93.
Christiano, L., and T. Fitzgerald, 2003, ‘‘The Band Pass Filter,’’ International Economic Review, Vol. 44, No. 2, pp. 435–65
Cuddington, John T., and Daniel Jerrett. "Super Cycles in Real Metals Prices?". IMF Staff Papers 55, no. 4 (2008): 541-65.
Cuddington, John T., and Abdel M. Zellou. "A Simple Mineral Market Model: Under What Conditions Will It Produce Super Cycles in Prices?" Colorado School of Mines, 2012.
Heap, Alan. "China - the Engine of a Commodities Super Cycle." edited by Equities Research: Global. New York City: Citigroup Smith Barney, 2005.
Jerrett, Daniel. “Trends and Cycles in Metals Prices.” PhD Dissertation (Mineral and Energy Economics) Colorado School of Mines (2010).
Jerrett, Daniel, and John T. Cuddington. "Broadening the Statistical Search for Metal Price Super Cycles to Steel and Related Metals." Resources Policy 33, no. 4 (2008): 188-95.
Zellou, Abdel, and John T. Cuddington. “Is There Evidence of Super Cycles in Oil Prices?.” SPE Economics and Management 4, no. 3(2012): 171-181.
