Commodities Index Innovation: The Next 30 Years

While 30 years is a lifetime in commodities investing, what more can we expect in the next three decades?

<p style="color:black;">Commodities Index Innovation: The Next 30 Years</p>

Introduction

Contributors
Fiona Boal, Head of Commodities and Real Assets
Jim Wiederhold, Associate Director Commodities and Real Assets

The S&P GSCI paved the way for three decades of index innovation in the commodities market and is the most widely recognized commodities index benchmark, just as when it launched in April 1991. Its broad base and production-weighted approach offers market participants true global commodities market beta. Including the most liquid commodity futures and adapting to changing market dynamics via a rules-based, transparent annual reconstitution, the index is investable and easily replicable.

Beyond the headline S&P GSCI, the S&P GSCI Series plays an integral role in the world’s investment infrastructure, whether it be as benchmarks, defining the investable universe, or measuring the unique return streams of commodities and commodities strategies. Exhibit 1 highlights innovation in the S&P GSCI family over the last 30 years.

While 30 years is a lifetime in commodities investing and there have been a number of marquee index-based solutions brought to market over this time, we anticipate that the next 30 years could bring even more disruptive changes to commodities. In this paper, we look at trends that could become ascendant over the next 30 years of commodities index innovation.

Exhibit 1: 30-Year Performance of the S&P GSCI

Source: S&P Dow Jones Indices LLC. Data from April 1991 to February 2021. Past performance is no guarantee of future results. Chart is provided for illustrative purposes.

The Commodity ESG Conundrum

Investing in commodities poses meaningful challenges for investors looking to incorporate environmental, social, and governance (ESG) metrics into their investment criteria. As a start, commodities are a broad and diverse asset class with different financial, environmental, and social implications.

While it may be possible to apply carbon footprint and ESG risk metrics to underlying commodities, and by association to commodities derivatives, such sustainability metrics have not been developed with these financial instruments in mind (see Exhibit 2).

Exhibit 2: Commodities and ESG – Metrics, Standards and Methodologies

Source: S&P Global Trucost. Data as of February 2021. Chart is provided for illustrative purposes.

Financial participation in commodity derivatives markets does not directly influence a particular company’s actions, as it can with equities or bonds, nor, arguably, does it come with the ability to affect the underlying spot commodity price. This makes applying ESG principles difficult. Moreover, the causal link between commodity derivatives and physical commodity production and consumption remains unclear, given that investing in commodity derivatives does not directly translate to physical commodity ownership (Danielson, 2020).

Indeed, there is also the question of whether commodities prices should be forced higher or lower to encourage production and consumption changes that are considered desirable from an ESG perspective. Commodities investors are passive participants in the ESG ecosystem, albeit ones who, depending on their mandate, have the ability to funnel their investments between different commodities or sectors. They may also have the ability to influence the rules and conventions adopted in the marketplace. For commodity derivatives, this points to engaging directly with the derivative exchanges.

Nevertheless, the main objective of commodity derivative markets is to facilitate risk. This objective aligns with many of the principles of ESG, specifically the need for transparency, risk mitigation, and market access. For centuries, speculators have assumed the price risk of commodity producers and consumers, and the markets themselves have aided the price discovery mechanism.

Financial participation in commodity derivatives markets does not directly influence a particular company’s actions, as it can with equities or bonds.

For those market participants looking to adapt existing ESG metrics to commodities, environmental issues will take center stage as arguably the most pressing and directly relevant ESG pillar. In such cases, the carbon footprint of various commodities can help inform ESG-minded decisions. Using lifecycle analysis (LCA) databases is a relatively straightforward approach to computing carbon footprints of individual commodities, and such metrics can likely be integrated into index methodologies. Exhibit 3 illustrates the estimated greenhouse gas (GHG) footprint associated with a selection of agricultural commodities. While one can lessen the weightings of the worst fossil-fuel-heavy commodities (or exclude them) to create an ESG-screened commodities index, it is not clear that it would result in a suitably diversified commodities index nor one with superior risk-adjusted performance.

As market participants increasingly incorporate ESG metrics into all aspects of investing, it is inevitable that commodities investing would collide with ESG. Since we cannot live without commodities, commodities market participants should advocate for more efficient and sustainable production and usage, but the commodity ESG conundrum will not be an easy one to solve.

Exhibit 3: Estimated GHG Footprint for Select Agricultural Commodities
(million metric tonnes of CO₂E)

692.6

Soybeans

582.2

Corn

185.0

Sugar Cane

281.1

Wheat

21.0

Coffee

Source: CE Delft. The CE Delft study is based on the agricultural phase of each commodity’s life cycle, including on-farm factors such as land, machinery, fertilizer, and water, but excluding processing, retail, transport, and the consumer. Data as of February 2015. Table is provided for illustrative purposes.

Alternative Risk Premia

Investors are becoming well versed in using alternative risk premia to efficiently measure, isolate, and gain access to alternative sources of return through rigorous, liquid, and transparent rules-based indices across asset classes. Risk premia that are economically intuitive, persistent over time, and executed in highly liquid markets are likely to offer the most benefit to market participants. In the commodities market, concepts such as carry have a strong economic rationale, while the underlying commodities themselves exhibit low correlation to traditional asset classes and are traded in liquid derivatives markets. The prevalence of non-profit-seeking participants in the commodities market adds to their attractiveness.

There are three widely accepted risk premia that will continue to be enhanced and applied to the commodities market.

Momentum

Exploits the relationship between a commodity’s return and its recent relative performance history. It is based on the premise that a price trend once established is likely to continue—long (overweight) commodities showing an upward price trend and short (underweight) securities showing a downward trend.

Carry

Goes long (overweight) or short (underweight) individual commodities based on the slope of the futures curve. If the futures curve is upward sloping, it is said to be in contango, and if it is downward sloping, it is said to be in backwardation. Commodities in backwardation (contango) should generate a positive (negative) roll yield, and therefore a positive (negative) excess return when market conditions remain unchanged.

Curve

Gives exposure to the roll yield of an individual commodity futures contract. This could involve optimizing contract positioning on the futures curve to minimize roll cost based on roll yield in a long-only strategy or taking a short position in a nearby contract and a long position in a deferred contract, often referred to as a calendar spread in an absolute return strategy.

Exhibit 4 illustrates the correlation and performance among the headline S&P GSCI and the first two absolute return commodities risk premia indices launched by S&P DJI in February 2021. The S&P GSCI Curve 3 Month performed well during the COVID-19 pandemic risk-off environment, with the majority of commodity futures curves in contango. In contrast, the S&P GSCI Momentum underperformed the headline S&P GSCI over a one-, three-, and five-year period.

Exhibit 4: S&P GSCI Risk Premia Performance
Characteristic	S&P GSCI	S&P GSCI Curve 3M	S&P GSCI Momentum
Correlation to S&P GSCI	1.00	(0.75)	0.10
Annualized Returns (%)
1-Year	8.35	7.42	(3.64)
3-Year	(3.53)	4.27	(14.23)
5-Year	2.61	3.10	(11.57)
Annualized Risk (%)
1-Year	41.88	12.10	13.83
3-Year	28.58	7.12	13.77
5-Year	23.71	5.59	13.50
Risk-Adjusted Returns
1-Year	0.20	0.61	(0.26)
3-Year	(0.12)	0.60	(1.03)
5-Year	0.11	0.55	(0.86)

Source: S&P Dow Jones Indices LLC. Data from Feb. 31, 2016, to Feb. 29, 2021. Index performance based on total return in USD. Correlation based on past five-year monthly returns. Past performance is no guarantee of future results. Table is provided for illustrative purposes and reflects hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-tested performance.

The underperformance of momentum strategies has been evident across asset classes for some time. In the case of commodities, there is little evidence to suggest that this underperformance is a function of market saturation. It would appear that the underlying commodities have simply not displayed strong or persistent price trends (Babbedge & Kerson, 2019). Despite that commodities momentum has been mixed over the past decade, long-term history suggests that momentum remains a systematically harvestable risk premium.

Incorporating the fundamental dynamics of the commodities market presents a challenge and an opportunity for risk premia strategies. Supply and demand levels, and specifically inventory levels, determine spot commodities prices. Having a grasp of commodities market fundamentals is important in the design and execution of risk premia strategies and may assist in the mitigation of potential losses (Hill, 2019).

The broad role of commodities in asset allocation may also be advanced by alternative risk premia. For some market participants, the use of a passive multi-strategy risk premia commodities allocation in a traditionally balanced portfolio may be an attractive alternative to passive long-only commodities exposure (Sakkas & Tessaromatis, 2018). For market participants unable or unwilling to employ absolute return strategies, long-only commodities exposure that is tilted to reflect risk premia such as carry, curve, or momentum can be an alternative.

Tactical Allocations

Tactically allocating to individual commodities when conditions are ripe has historically only been within the realm of a small group of domain expert traders. But as the broad cohort of market participants becomes more sophisticated and access to alternative asset classes improves, it is possible that the tactical allocation to commodities will expand. Commodities can be used as building blocks to express thematic macro plays and can easily be traded due to highly liquid and robust markets. Commodities also benefit from being equally tradeable both long and short. Single-commodity indices tracking front-month futures contacts allow market participants to easily gain exposure on the long or short side.

A number of current market trends may be interesting to see through a commodities lens—the post-COVID-19-pandemic recovery and associated inflationary pressures and the growth in green technology, for example.

Inflation

For many market participants, unprecedented and coordinated fiscal stimulus in the wake of the COVID-19 pandemic has justified concerns over inflation. Historically, commodities, and in particular gold, have demonstrated a high inflation beta and may provide a suitable inflation hedge (see Exhibit 5).

Exhibit 5: S&P GSCI and S&P GSCI Gold Inflation Protection

Source: S&P Dow Jones Indices LLC, Federal Reserve Bank of St. Louis. Data from December 1978 to February 2021. Past performance is no guarantee of future results. Chart is provided for illustrative purposes and reflects hypothetical historical performance. Please see the Performance Disclosure at the end of this document for more information regarding the inherent limitations associated with back-tested performance. Inflation is defined as the year-over-year percentage change in the monthly U.S. CPI. Average year-over-year S&P GSCI and S&P GSCI Gold returns since index inception.

The risk of inflation centers on whether the post-COVID-19-pandemic recovery will be merely reflationary or truly inflationary. Quantitative easing since 2008 has proved inflationary for only paper assets (i.e., equities), but there is an argument to be made that after the COVID-19 pandemic, coordinated, real-asset heavy fiscal spending may prove inflationary. Even though the trajectory of real asset inflation is likely lower due to structural changes in demographics, technology, consumption, and productivity, starting from a low inflation level means even a small increase in inflationary pressure can lead to notable asset repricing. As the last period of prolonged inflation occurred decades ago, most investors have not experienced it. It may be difficult for them to assign a probability to a sustained period of inflation as well as to adapt portfolio construction should the probability be sufficiently high. Investors tend to have short memories.

Historically, commodities have had higher volatility than most other asset classes over the short and long term (see Exhibit 6). It may not take a lot of exposure to either a single-commodity index or a broad-commodities index to reap the benefits of inflation protection.

Exhibit 6: Short- and Long-Term Asset Class Performance and Volatility
Time Frame	Equities	U.S. Govt Bonds	S&P GSCI
Annualized Returns (%)
1-Year	31.29	0.54	8.35
3-Year	14.14	5.03	(3.53)
10-Year	13.43	3.49	(8.01)
30-Year	10.35	5.68	0.07
Annualized Risk (%)
1-Year	23.73	3.88	41.88
3-Year	18.45	3.61	28.58
10-Year	13.54	3.06	21.74
30-Year	14.52	3.58	21.39
Risk-Adjusted Returns
1-Year	1.32	0.14	0.20
3-Year	0.77	1.39	(0.12)
10-Year	0.99	1.14	(0.37)
30-Year	0.71	1.59	0.00
Source: S&P Dow Jones Indices LLC. Bloomberg. Data from February. 28, 1991, to February 26, 2021. Index performance based on total return in USD. Equities are represented by the S&P 500 and bonds are represented by the Barclays US Aggregate Bond Index. Past performance is no guarantee of future results. Table is provided for illustrative purposes.

Green Technology

Technology used to mitigate or reverse the effects of climate change is expected to play a vital role in the near future. Besides rare earth metals, the building blocks for many of these technologies are industrial commodities such as copper, aluminum, nickel, and silver (see Exhibit 7).

According to a recent World Bank Group report, over three billion tons of minerals and metals will be needed to deploy the wind, solar, and geothermal power, as well as energy storage, required for achieving a below 2°C future (Hund et al., 2020).

Historically, technology has worked against commodities, either by encouraging substitution or improving productivity and thereby requiring less of a certain raw material to meet demand. But in the case of decarbonization, the opposite is true; the adoption of green technologies signals strong demand for many commodities.

Exhibit 7: World Bank Low-Carbon Future Scenario Metals
	Wind	Solar Photovoltaic	Concentrating Solar Power	Carbon Capture and Storage	Nuclear Power	Light Emitting Diodes	Electric Vehicles	Electric Storage	Electric Motors
Aluminum	•	•	•	•		•		•	•
Chromium	•			•	•	•
Cobalt				•	•		•	•
Copper	•	•		•	•	•	•		•
Indium		•		•	•	•
Iron (cast)	•		•			•		•
Iron (magnet)	•								•
Lead	•	•			•	•
Manganese	•			•		•	•
Molybdenum	•	•		•	•	•
Neodymium (proxy for rare earth)	•						•
Nickel	•	•		•	•	•	•	•
Silver		•	•		•	•	•
Steel (engineering)	•
Zinc		•				•
Source: World Bank. Data as of 2020. Table is provided for illustrative purposes.

Non-Traditional Factors

The commodities market is ripe with data, from the traditional supply and demand metrics reported by government agencies on a regular (though delayed) basis, to real-time data, such as port inventory that can be computed using satellite imagery. Finding ways to use this largely non-price (or non-financial) data in rules-based, investable indices can present attractive opportunities.

The scale and scope of non-financial data available in commodities markets is unique. Yet processing and modeling that data to produce investable signals with attractive risk/return profiles on a large scale has proven difficult. However, a new generation of commodities indices utilizing supervised machine learning to compute large non-linear datasets to generate investable signals may be on the horizon.

New commodities indices utilizing supervised machine learning to compute large non-linear datasets to generate investable signals may be on the horizon.

This approach could also offer a solution to incorporating ESG data into commodities indices. For example, S&P Global Platts, a sister company to S&P Dow Jones Indices, announced its intention to develop AI-driven physical carbon credit price assessment indices in February 2021. The new price assessments will leverage environmental AI expertise provided by Viridios Capital, which has been trained on over 20,000 data points, representing transactions from across the range of carbon projects around the world.

Cryptocurrencies and Tokenization

Cryptocurrencies and digital ledgers may be both an opportunity and threat to traditional commodities derivatives markets and commodities indexing. As an alternative asset class, certain cryptocurrencies could offer diversification and inflation-protection benefits similar to commodities, particularly gold. As an investment vehicle and store of value, a limited history, extreme volatility, and weak regulation present challenges today, but there is no doubt that cryptocurrencies are beginning to play a more important role in investment portfolios. From an inflation perspective, the narrative centers around the belief that, as privately created assets with finite supply, cryptocurrencies cannot be printed like fiat currencies.

From a utility perspective, commodities have obvious real-world applications; they are the building blocks of the real economy, but digital assets also have an underlying use case aimed at utility. This utility could be privacy, instant settlement, trade finance, or supply chain management. Some cryptocurrencies that currently operate as speculative assets will likely begin to shift to a demand-driven market based on demand for the underlying utility, essentially giving them additional commodity-like characteristics.

Market participants have long eschewed many non-exchange (non-public) investment opportunities because of concerns about liquidity, transparency, and valuation frequency and visibility. Others have allocated to alternatives such as real estate and private equity via specialist vehicles that have traditionally attracted significant management and performance fees. The tokenization of real assets has the potential to address many of these concerns and broaden the potential investor base for alternative assets. Tokenization is a way to securitize real assets through which the asset is divided into shares or tokens that represent a predefined share of the underlying asset. The tokens are secured through the immutability of digital ledger technology and are tradeable via cryptocurrency exchanges or alternative trading systems. Tokens simply act as a means of trading, just as futures and exchange-traded products do for commodities today.

The tokenization of real assets may create further competition for traditional derivatives-based commodities investment instruments. So-called digital gold is already disrupting the retail gold investment market. Digital gold products enable investors to purchase gold directly without the chain of custody and financial product wrappers common with gold exchange-traded products. The underlying assets can be delivered at the request of the beneficiary. Tokenization may also play a role in improving the allocation, measurement, and transparency of non-price characteristics of commodities and real assets, such as carbon intensity.

The proliferation of stablecoins will affect the commodities markets. Stablecoins are cryptocurrencies pegged to fiat currencies or other so- called “stable” or reserve assets that are designed to minimize the price volatility of the stablecoin. Stablecoins are redeemable in the currency, asset, or fiat money on which they are backed. Commodity-backed stablecoins can be redeemed at the conversion rate fixed by the stablecoin (e.g., one gram of gold). Many market participants believe that fiat currency stablecoins will eventually form the basis of all global payment systems. Related to stablecoins are central bank digital currencies (CBDCs). CBDCs would allow individuals and businesses to directly make payments and store value using an electronic form of central bank money (Bank of England, 2020).

Digital ledgers and tokenization may disrupt and potentially improve the efficacy of risk management for physical commodity hedgers. The network of digital ledgers that are used to record transactions could improve the management of price and counterparty risk inherent in commodities production, trade, and consumption. It is anticipated that all trading ecosystems will be digital within the next few decades, including traditional exchanges. The commodity futures contracts that are so ubiquitous today may look quite different or at least incorporate different characteristics and trade in different ways in the future. This will also affect custody and the physical storage of commodities, both on and off futures exchanges.

Conclusion

The first 30 years of commodities index-based investing offered increasingly sophisticated alternatives to broad commodities beta, direct access to the performance of individual commodities, and application of alternative risk premia strategies to the asset class. Looking ahead, we expect commodities investing to merge with a number of powerful trends such as incorporating ESG metrics, greater adoption of commodities risk premia, the tactical use of commodities, the incorporation of non-traditional data, and the likely disruption from digital assets and tokenization.

References & Additional Resources

Learn More

References

Hund, K., La Porta, D., Fabregas, T., Laing, T., Drexhage, J. (2020). “Minerals for Climate Action: The Mineral Intensity of the Clean Energy Transition.” The World Bank Group.
Danielson, G.B. (2020). “Can a Responsible Investor Invest in Commodity Futures?” Global Commodities Applied Research Digest.
Odegard, I., Bijleveld, M., Naber, N. (2015). “Global GHG Footprints and Water Scarcity Footprints in Agriculture.” CE Delft.
Sakkas, A., Tessaromatis, N. (2018). “Factor-Based Commodity Investing.” EDHEC Business School.

Till, H. (2019). “Weathering the Storm in Commodity Risk Premia Strategies.” Global Commodities Applied Research Digest.
Babbedge, T., Kerson, J.S. (2019). “Trend’s Not Dead (It’s just moved to a trendier neighbourhood).” Gresham Investment Management.
Bank of England (2020), Central Bank Digital Currency Opportunities, Challenges and Design, Discussion Paper.

Performance Disclosure/Back-Tested Data

The S&P GSCI Momentum and S&P GSCI Curve 3 Month were launched February 16, 2021. The S&P GSCI Gold was launched May 1, 1991. All information presented prior to an index’s Launch Date is hypothetical (back-tested), not actual performance. The back-test calculations are based on the same methodology that was in effect on the index Launch Date. However, when creating back-tested history for periods of market anomalies or other periods that do not reflect the general current market environment, index methodology rules may be relaxed to capture a large enough universe of securities to simulate the target market the index is designed to measure or strategy the index is designed to capture. For example, market capitalization and liquidity thresholds may be reduced. Complete index methodology details are available at www.spglobal.com/spdji. Past performance of the Index is not an indication of future results. Back-tested performance reflects application of an index methodology and selection of index constituents with the benefit of hindsight and knowledge of factors that may have positively affected its performance, cannot account for all financial risk that may affect results and may be considered to reflect survivor/look ahead bias. Actual returns may differ significantly from, and be lower than, back-tested returns. Past performance is not an indication or guarantee of future results. Please refer to the methodology for the Index for more details about the index, including the manner in which it is rebalanced, the timing of such rebalancing, criteria for additions and deletions, as well as all index calculations. Back-tested performance is for use with institutions only; not for use with retail investors.

S&P Dow Jones Indices defines various dates to assist our clients in providing transparency. The First Value Date is the first day for which there is a calculated value (either live or back-tested) for a given index. The Base Date is the date at which the index is set to a fixed value for calculation purposes. The Launch Date designates the date when the values of an index are first considered live: index values provided for any date or time period prior to the index’s Launch Date are considered back-tested. S&P Dow Jones Indices defines the Launch Date as the date by which the values of an index are known to have been released to the public, for example via the company’s public website or its data feed to external parties. For Dow Jones-branded indices introduced prior to May 31, 2013, the Launch Date (which prior to May 31, 2013, was termed “Date of introduction”) is set at a date upon which no further changes were permitted to be made to the index methodology, but that may have been prior to the Index’s public release date.

Typically, when S&P DJI creates back-tested index data, S&P DJI uses actual historical constituent-level data (e.g., historical price, market capitalization, and corporate action data) in its calculations. As ESG investing is still in early stages of development, certain datapoints used to calculate S&P DJI’s ESG indices may not be available for the entire desired period of back-tested history. The same data availability issue could be true for other indices as well. In cases when actual data is not available for all relevant historical periods, S&P DJI may employ a process of using “Backward Data Assumption” (or pulling back) of ESG data for the calculation of back-tested historical performance. “Backward Data Assumption” is a process that applies the earliest actual live data point available for an index constituent company to all prior historical instances in the index performance. For example, Backward Data Assumption inherently assumes that companies currently not involved in a specific business activity (also known as “product involvement”) were never involved historically and similarly also assumes that companies currently involved in a specific business activity were involved historically too. The Backward Data Assumption allows the hypothetical back-test to be extended over more historical years than would be feasible using only actual data. For more information on “Backward Data Assumption” please refer to the FAQ. The methodology and factsheets of any index that employs backward assumption in the back-tested history will explicitly state so. The methodology will include an Appendix with a table setting forth the specific data points and relevant time period for which backward projected data was used.

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