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Commodity Futures

Precious Metals

Precious Metals Commodity Prices

A chart comparing the percentage price change of precious metals: Gold, Silver, Palladium, and Platinum.

Base Metals

Base Metals Commodity Prices

A chart comparing the percentage price change of base metals: Aluminum, and Copper.

Energy

Energy Commodity Prices

A chart comparing the percentage price change of energy commodities: Brent Crude, WTI Crude, Gasoline, Heating Oil, and Natural Gas.

Grains & Soybean Products

Grains and Soybean Commodity Prices

A chart comparing the percentage price change of grains and soybean products: Corn, Chicago SRW Wheat, Oat, Rough Rice, Soybean, and Soybean Oil.

Soft Commodities

Soft Commodities Prices

A chart comparing the percentage price change of soft commodities: Cocoa, Coffee, Cotton, Sugar, and Orange Juice.

Dairy Products

Dairy Commodity Prices

A chart comparing the percentage price change of dairy products: Cheese, Butter, Whey, Class 3 Milk, and Non-Fat Dry Milk.

Livestock

Livestock Commodity Prices

A chart comparing the percentage price change of livestock: Lean Hogs and Live Cattle.

Interpretation

The charts above display the prices for different commodities relative to each other. Commodities are predominantly traded as futures contracts. There are a handful of exchanges that cover most of trading volume. They include the Intercontinental Exchange (ICE), the Chicago Mercantile Exchange (CME), the COMEX, the Chicago Board of Trade (CBOT), and the New York Mercantile Exchange (NYMEX). All these exchanges but the first one are owned by the CME Group.

A commodity futures contract is an agreement to buy or sell a predetermined amount of a commodity at a specific price on a specific date in the future. Future contracts are either cash-settled or physically delivered upon the expiry date of the contract. When a contract is cash-settled, the net cash position of the contract on the expiry date is transferred between the buyer and the seller. In physical delivery, the seller is required to provide the asset at the defined time and place — and the buyer must receive it.

However, in order to avoid settlement, most futures contracts are actually offset or rolled-over prior to expiration. When the forward curve is in contango, rolling-over can induce significant cost. For this reason, in order to get exposure to commodities, long-term investors often choose to invest in equities rather than in futures contracts.

Further Information


Correlation Heat Map

A heatmap showing the correlation between various commodity prices. Red indicates a strong positive correlation, while blue indicates a negative correlation.

Interpretation

This heatmap visualizes how different commodities move in relation to one another. Red squares indicate a strong positive correlation (assets moving together), while blue squares show a negative correlation (assets moving in opposite directions). Notice how related commodities, like different types of oil or precious metals, often form distinct, tightly correlated clusters.

Understanding these relationships is key to building a diversified portfolio. By combining assets that don't move in lockstep, investors can reduce overall risk without necessarily sacrificing returns. As Ray Dalio notes in his book Principles, a well-diversified portfolio of 15-20 uncorrelated assets is the "Holy Grail of Investing."

To create this chart, weekly log-returns are calculated for each commodity, and the Pearson correlation is computed for every pair. The heatmap is then organized using hierarchical clustering to group the most similar commodities together, making market patterns easier to see.


Correlation Spanning Tree

A minimum spanning tree visualizing the strongest correlations between commodities. Linked commodities have a strong positive correlation.

Interpretation

The Minimum Spanning Tree (MST) simplifies the correlation matrix by showing only the strongest connections between commodities. If two commodities are linked, they have a strong positive correlation and tend to move in tandem. This helps identify clusters of related assets and is useful for portfolio diversification.

The tree is constructed by converting the correlations into distances and then finding the set of connections that links all commodities with the minimum total distance. As noted by Marti, Gautier, et al. (2017), the optimal Markowitz portfolio is often found at the tree's outskirts, and the tree tends to shrink during a financial crisis.

Data Sources