A Dynamic Commodities Allocation: The Forgotten Diversifier

A Dynamic Commodities Allocation: The Forgotten Diversifier

Commodities are often treated as the forgotten child of asset allocation: an increasingly essential part of the global economy, yet frequently peripheral in strategic portfolio discussions. Today, trade frictions, geopolitical competition, industrial policy, supply-chain insecurity, and the race to build digital capacity are placing new demands on physical resources. Rather than a temporary, tactical patch, this post examines why commodities warrant reconsideration as a strategic source of portfolio resilience, including: 

  • the emerging macro regime and the changing nature of portfolio diversification; 
  • the importance of identifying and translating disparate price drivers into repeatable investment decisions;  
  • and considerations and controls that pertain to a dynamic allocation throughout a cycle. 

 

A World of Constraints

Today’s demands on real assets are mounting amid a macro regime consisting of more frequent economic shocks.1 Demand for physical capacity—and the requisite commodities—can change quickly in this environment. Supply usually adjusts far slower. New mines, pipelines, power projects, and logistics capacity require capital, permitting, labor, and time, constraints that can delay the supply response to higher prices. This asymmetry matters.

The war in the Middle East is the latest reminder that energy disruptions can lift the cost curves of industrial materials and agriculture, while competition for critical minerals can intensify as governments seek more secure supply. Scarcity alone is not an investment thesis, but scarcity paired with durable demand can reshape relative value across a specific commodity complex.

 

Inflation Protection When It May Matter Most

Commodities’ most familiar portfolio role is as a source of inflation protection: price shocks can cause inflation through supply-side channels, not merely as a response to it. Higher energy, metals, or food costs can flow through production costs into consumer prices, positioning commodities early in the inflation cycle.

As a U.S. example, a broad commodities basket is up nearly 40% year-to-date—driven largely by the war’s effects on the energy complex—and U.S. inflation of 3.7% year-over-year has missed the Federal Reserve’s 2% inflation target for more than five years. Furthermore, we see U.S. and non-U.S. developed market inflation remaining above 2% over the coming decade.

In the high-inflation periods highlighted in Exhibit 1, commodities produced materially positive nominal and real returns on an annualized basis, while U.S. equities and Treasuries delivered real returns much closer to zero. Commodities are neither a permanent inflation hedge nor guaranteed to repeat those results, but this history suggests relatively liquid, near-term sensitivity to price pressures that challenge traditional assets during high inflation. Other real assets may respond to inflation on different timelines; for example, real estate rents and replacement costs generally adjust gradually with less liquidity.

 

Exhibit 1: Commodities as a barometer of the new regime of more frequent macro shocks

 

Nominal and real returns in low- and high-inflation periods. Low inflation: U.S. equity 13.6% and 11.7%, U.S. Treasury 5.6% and 3.6%, commodities 0.3% and −1.7%. High inflation: U.S. equity 8.8% and 2.7%, U.S. Treasury 6.7% and 0.7%, commodities 15.3% and 9.2%.
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Source: PGIM, FactSet, and Bloomberg. High-inflation periods consist of inflation above 3%, based on the U.S. Bureau of Labor Statistics’ Consumer Price Index. Based on annualized returns and a quarterly sample period from Q2 1973 to Q3 2026. Historical observations are not indicative of future results. Commodity-related investments involve risk, including the possible loss of capital. Indexes are unmanaged, do not reflect fees or expenses, and cannot be invested in directly. Returns use the S&P 500 Index, Bloomberg U.S. Treasury Index, and Bloomberg Commodity Index from FactSet.
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Nominal and real returns in low- and high-inflation periods. Low inflation: U.S. equity 13.6% and 11.7%, U.S. Treasury 5.6% and 3.6%, commodities 0.3% and −1.7%. High inflation: U.S. equity 8.8% and 2.7%, U.S. Treasury 6.7% and 0.7%, commodities 15.3% and 9.2%.
Source: PGIM, FactSet, and Bloomberg. High-inflation periods consist of inflation above 3%, based on the U.S. Bureau of Labor Statistics’ Consumer Price Index. Based on annualized returns and a quarterly sample period from Q2 1973 to Q3 2026. Historical observations are not indicative of future results. Commodity-related investments involve risk, including the possible loss of capital. Indexes are unmanaged, do not reflect fees or expenses, and cannot be invested in directly. Returns use the S&P 500 Index, Bloomberg U.S. Treasury Index, and Bloomberg Commodity Index from FactSet.

The Changing Nature of Diversification

The traditional equity-bond mix underpins many multi-asset portfolios, but its diversification benefits depend on the shock. High-quality bonds may offset equity weakness during growth shocks, while inflation or supply shocks can pressure both simultaneously. In recent years, commodities’ lower correlation with equities—in comparison to the rising correlation between stocks and bonds observed in Exhibit 2—supports a broader diversification case. Assets sensitive to physical supply and demand can complement those primarily driven by discount rates, earnings, and credit conditions.

Commodities are not one homogeneous trade. Most are not readily substitutable, and their market dynamics differ according to inventories, weather patterns, production economics, policy decisions, and end users. A drought can tighten one agricultural market while leaving another relatively unaffected, and an energy shock can benefit producers while squeezing energy-intensive industries.

 

Exhibit 2: Different correlation directions for equities-bonds and equities-commodities

Correlation of monthly returns, 5-year lookback period

 

Five-year rolling correlation of monthly returns, 2005–2026. BCOM versus S&P 500 correlation declines to about 0.15, while U.S. Fixed Income Aggregate versus S&P 500 correlation rises to about 0.6.
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Source: PGIM and Bloomberg. Reflects correlations of the Bloomberg S&P 500 Total Return Index with the Bloomberg Commodity Index (BCOM) and Bloomberg U.S. Aggregate Bond Total Return (USD Unhedged) Index. Historical observations are not indicative of future results. Commodity-related investments involve risk, including the possible loss of capital. Data as of Sept. 2026. Indexes are unmanaged, do not reflect fees or expenses, and cannot be invested in directly.
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Five-year rolling correlation of monthly returns, 2005–2026. BCOM versus S&P 500 correlation declines to about 0.15, while U.S. Fixed Income Aggregate versus S&P 500 correlation rises to about 0.6.
Source: PGIM and Bloomberg. Reflects correlations of the Bloomberg S&P 500 Total Return Index with the Bloomberg Commodity Index (BCOM) and Bloomberg U.S. Aggregate Bond Total Return (USD Unhedged) Index. Historical observations are not indicative of future results. Commodity-related investments involve risk, including the possible loss of capital. Data as of Sept. 2026. Indexes are unmanaged, do not reflect fees or expenses, and cannot be invested in directly.

From Allocation to Implementation

A static commodity allocation may provide useful exposure during an inflation shock, but commodities have historically tended to lag equities and Treasuries during low-inflation periods. Allocation design should therefore reflect its intended role, risk budget, liquidity needs, existing exposures, and tolerance for cyclicality. It should also consider the evolution of positioning as inflation, inventories, supply conditions, and growth rates change.

Commodity markets also reward a disciplined investment process. Producers and consumers often transact for operational reasons rather than purely for financial reasons, while specialists may concentrate on only a handful of contracts. Price formation is grounded in observable forces—supply, demand, inventories, transport, seasonality, and futures curves—yet the information set is increasingly broad. Combining economic intuition, systematic signals, and newer data can support consistent comparisons across markets. The aim is not to forecast every geopolitical event or weather pattern, but to translate diverse information into repeatable decisions while controlling for concentration, volatility, liquidity, and implementation risk.

For allocators, this distinction matters. Two strategies labeled “commodities” can produce very different outcomes depending on benchmark design, net exposure, contract selection, signal breadth, rebalancing, collateral, and risk controls. Due diligence should look beyond headline exposure to whether the investment rationale is economically intuitive, signals are robust across regimes, risk is well distributed, and implementation is capital efficient. The objective is to understand not only what a strategy owns, but why and under what conditions it is expected to contribute.

 

Building Resilience through Implementation

Diversification within commodities should extend beyond markets to the signals that drive positioning. For example, traditional approaches centered on curve carry can leave investors dependent on a single return driver, which can underperform when stocks and bonds are both down (Exhibit 3)—precisely when allocators rely on differentiated returns. One alternative is weighing commodities based on realized skewness, which has historically performed best when traditional growth-oriented assets struggle, including in periods when both stocks and bonds underperformed.

A robust multi-factor approach seeks to reduce that carry dependence by combining economically distinct signals (including carry and skewness, but also sentiment and fundamentals) rather than relying on any one factor across regimes. The objective is to identify complementary sources of return with disciplined controls on shared exposures, concentration, and implementation risk. This broader, risk-aware approach supports active management without replacing one portfolio vulnerability with another; it neither eliminates losses nor guarantees diversification.

 

Exhibit 3: Diversifying commodity signals beyond carry can contribute to commodities’ diversifying effects

 

Commodity factor returns by stock and bond direction: stocks down/bonds down, skewness 14.1%, curve carry −1.0%; stocks down/bonds up, 15.4% and 1.0%; stocks up/bonds down, 2.5% and 1.4%; both up, 5.6% and 3.5%.
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Source: PGIM, Bloomberg. Based on a quarterly sample period from Q2 1992 to Q3 2026. Based on annualized returns; arrows represent quarters when total returns of stocks and bonds are positive (up) or negative (down). Historical observations are not indicative of future results. Commodity-related investments involve risk, including the possible loss of capital. Indexes are unmanaged, do not reflect fees or expenses, and cannot be invested in directly. Stock and bond returns use the Bloomberg S&P 500 Total Return Index and Bloomberg U.S. Aggregate Bond Total Return (USD Unhedged) Index, respectively. Skewness uses the UBS Commodity Realized-Skew Investment Strategy Excess Return Index; data provided from Bloomberg. Curve carry is based on the Bloomberg Commodity Index 3-Month Forward Total Return Index, subtracted by the Bloomberg Commodity Total Return Index. Curve carry is PGIM's calculation of the return difference between the two indexes shown. It does not represent an investable strategy or PGIM product.
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Commodity factor returns by stock and bond direction: stocks down/bonds down, skewness 14.1%, curve carry −1.0%; stocks down/bonds up, 15.4% and 1.0%; stocks up/bonds down, 2.5% and 1.4%; both up, 5.6% and 3.5%.
Source: PGIM, Bloomberg. Based on a quarterly sample period from Q2 1992 to Q3 2026. Based on annualized returns; arrows represent quarters when total returns of stocks and bonds are positive (up) or negative (down). Historical observations are not indicative of future results. Commodity-related investments involve risk, including the possible loss of capital. Indexes are unmanaged, do not reflect fees or expenses, and cannot be invested in directly. Stock and bond returns use the Bloomberg S&P 500 Total Return Index and Bloomberg U.S. Aggregate Bond Total Return (USD Unhedged) Index, respectively. Skewness uses the UBS Commodity Realized-Skew Investment Strategy Excess Return Index; data provided from Bloomberg. Curve carry is based on the Bloomberg Commodity Index 3-Month Forward Total Return Index, subtracted by the Bloomberg Commodity Total Return Index. Curve carry is PGIM's calculation of the return difference between the two indexes shown. It does not represent an investable strategy or PGIM product.

A Strategic Question for the New Regime

The uncertain timing and magnitude of the next macro shocks reinforce the case for portfolio exposures with different economic drivers than stocks and bonds. Commodities are not a cure-all; allocations should reflect each portfolio’s objectives, liquidity needs, and tolerance for cyclicality. Implementation also matters because narrowly focused strategies can underperform in the regimes when you need them most, strengthening the case for robust multi-factor active management. But in a world where physical constraints may exert greater influence on growth and prices, leaving them out of the allocation conversation may be the bigger strategic risk.

For investors, the opportunity is to move beyond viewing commodities as a single inflation trade and instead consider them as a diverse, evolving opportunity set—one that can contribute inflation sensitivity, differentiated returns, and a more resilient portfolio when thoughtfully constructed and rigorously managed.

 

1 For additional insights see “Real Assets for New Realities: Investing in a World of Constraints,” September 2026, PGIM.com

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Edward J. Tostanoski III
Edward J. Tostanoski III
Principal and Portfolio Manager for PGIM Quantitative Solutions PGIM Quantitative Solutions
Marco Aiolfi
Marco Aiolfi
Head of Multi Asset PGIM Quantitative Solutions
Jeffrey Young
Jeffrey Young
Head of Investment Strategy PGIM Quantitative Solutions

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