^{Source: CME Group / Bloomberg daily settlement data; CME Group Economic Calculations.}

• Whichever benchmark and whichever window, the Agricultural complex clusters near the origin. 
• Against the commodity sub-indices, the spread is only slightly wider. 
• Contracts with an energy-linked demand channel, such as Soybean Oil and Corn, sit modestly higher, while the rest of the complex is again difficult to distinguish from zero.

For much of the recent past, the defining feature of markets has been how little of the old playbook has worked. With rates holding higher for longer and stocks and bonds moving together more often than allocators would like, the search for genuinely uncorrelated exposure has widened, and commodities have been a frequent first stop. Broad commodity indexes offer a convenient way in, and for many portfolios, they are a solid foundation. But index-level exposure casts a wide net, often obscuring the fundamentally uncorrelated returns hidden within specific sectors.

This article looks at a different corner of the commodity universe: Our Agricultural futures, examined through the two lenses that matter most to an active portfolio: correlation to traditional benchmarks and the liquidity to trade at size.

A useful way to see where Agricultural markets sit is to plot each contract's correlation to the two anchors of a traditional portfolio. The upper panels of Figure 1 show the daily return correlation of 12 of our Agricultural contracts, and the Bloomberg Commodity Index (BCOM) for reference, against E-mini S&P 500 futures on one axis and 2-Year Treasury Note futures on the other, from January 2020 through July 2026.

The pattern is difficult to miss. BCOM itself carried a correlation of approximately 0.29 to equities over the period; this was the highest of anything on the chart. Every individual agricultural contract came in below it. Several grains and oilseeds - Corn, Soybeans and both Wheat contracts - clustered between 0.01 and 0.07 against equities, effectively indistinguishable from zero. Soybean Meal, among the most actively traded contracts in the group, was slightly negative. Livestock and Soybean Oil ran somewhat higher, with Feeder Cattle at 0.18, yet still comfortably below the broad index. Against the 2-Year Note, no contract in the group exceeded ±0.09 in either direction.

These relationships suggest that holding commodity exposure solely through a broad index may yield more equity co-movement than expected. The drivers of this independence are intuitive: Crop prices respond to weather, acreage, yields, and the rhythm of the growing season; livestock markets answer to herd cycles and consumer demand. None of these move with discount rates or earnings revisions.

Low correlation in a full-period average can conceal variation underneath. Figure 2 takes the same question through time, displaying 252-day rolling correlations for the Bloomberg Commodity Index (BCOM), Corn, Live Cattle and Soybean Meal.

The rolling view accounts for the dynamic relationships between assets, acknowledging that co-movement shifts alongside market regimes and sentiment. To quantify the uncertainty of these relationships at any given point in time, we apply 95% confidence bands around the rolling estimates, which dictate how precisely that correlation is measured.

Through this lens, the contrast becomes much sharper. BCOM's equity correlation was not merely positive on average; it stayed elevated through most of the sample, reaching roughly 0.6 in early 2021 and spending much of the period between 0.1 and 0.45. The agricultural panels tell a different story. Averaged over every rolling one-year window since 2021, Corn's correlation to the S&P 500 rounds to 0.00. Alongside Soybean Meal, Corn’s confidence intervals rarely exclude zero, rendering their returns statistically indistinguishable from zero correlation to E-mini S&P 500 futures.

We performed a similar exercise across several agricultural futures contracts, the results of which are summarized in Table 1. Three distinct metrics translate this rolling historical record into practical inputs for portfolio construction. First, the average 95% confidence interval width defines the precision of the correlation estimate; a tighter interval allows risk managers to model the asset's behavior with greater certainty, whereas a wider band signals less reliable diversification benefits during that window.

Second, the percentage of time the interval includes zero reveals how frequently a contract was statistically uncorrelated to the broader equity market. For allocators, this provides a rigorous test of structural independence, separating assets that offer persistent, foundational diversification from those merely experiencing temporary market dislocations.

Lastly, the standard deviation of the correlation measures the stability of this relationship over time. A lower standard deviation points to a highly predictable correlation profile, signaling to investors that the asset's behavior remains steady across varying market regimes and reducing the risk of sudden, unexpected equity co-movement.

Conventional wisdom suggests that diversification on paper is most useful when it can be put on and taken off at size. So, how accessible are these markets in practice? Table 1 aligns the rolling historical record with the YTD liquidity profile for each contract. The data highlights a distinct contrast: While the broad commodity index shows the highest mean correlation, the largest swings between market regimes and the widest confidence bands, the Agricultural complex pairs near-zero equity correlation with highly stable estimates and deep institutional liquidity.

As detailed in Table 1, the core of the complex trades at an institutional scale, possessing sufficient daily volume and open interest for large allocators to build meaningful, portfolio-level positions. Corn futures averaged nearly 479,000 electronic contracts per day, representing roughly $37 billion in notional open interest, while Soybean futures cleared approximately 293,000 contracts daily.

The livestock sector provides significant operational depth of its own, with Live Cattle and Lean Hogs each averaging nearly 69,000 daily contracts, and Live Cattle alone maintaining $32.6 billion in notional open interest. Connecting this execution capacity back to our statistical results highlights the specific profile of the livestock contracts: despite Live Cattle and Feeder Cattle exhibiting the lowest percentages of time including zero in their confidence intervals, their correlation standard deviations remain low, at 0.07 and 0.06 respectively, pointing to a stable relationship over time despite the lower overlap with zero.

Beyond these flagship contracts, a second tier consisting of Class III Milk and Rough Rice provides a compelling trade-off for market participants whose operational thresholds allow for lower, yet still highly workable, screen liquidity. These two assets maintain active daily electronic volumes of 2,039 and 1,653 lots respectively, alongside their respective average daily notional open interest values. In exchange for measured sizing, traders can access some of the most structurally independent returns in the entire commodity space. 

Both contracts lead the complex in offering true non-correlation to equities, maintaining relationships that effectively round to zero and remain incredibly steady over time, with standard deviations of just 0.034 and 0.048. As these narrow confidence intervals and low standard deviations demonstrate, these specific contracts offer a precise, reliable, and highly stable lack of correlation to the broader equity market without requiring participants to drop into double- or triple-digit daily volume tiers.

Agricultural futures are not a new asset class, but the case examined here is easy to miss inside an index wrapper or static correlation analysis: Contract by contract, our Agricultural markets have historically exhibited equity correlations well below the broad commodity benchmark. In the complex, those correlations remain statistically indistinguishable from zero across most rolling windows, alongside deep electronic liquidity in the core contracts. For portfolios that already hold commodities for diversification, the data suggests the diversification available at the contract level is larger than the index-level number implies. Whether that independence persists as agricultural markets globalize is a question worth revisiting – and one the data will keep answering.

^{1. Shore, M. Uncovering the Hidden Drivers of Commodities. CME Group, 17 March 2026. (cmegroup.com/insights/economic-research/2026/uncovering-the-hidden-drivers-of-commodities.html)}

^{2. Spilker, G. Beyond the Benchmarks: Exploring Diversification Within Commodity Markets. CME Group. (https://www.cmegroup.com/articles/2025/exploring-diversification-within-commodity-markets.html)}

^{Neither CME Group Inc. nor any of its affiliates provide investment advice or recommendations. Past performance is not indicative of future results.}