Analyzing Commodity Futures

Learning Quantitative Trading: 🚛📈 Systematizing Chaos in Barrels & Bushels: Commodity Strategies from Benjamin Hoff —Commodity Futures Surfaces and the Cash-and-Carry Glue (Flirting With Models, S7E17) Here are the most fascinating takeaways I noted 👇 🛢️ Commodities ≠ Other Asset Classes Most people treat crude oil like a stock: But pros like Ben Hoff look at the term structure — how prices for delivery at different times (e.g., Aug vs. Dec) relate. Why it matters: ✔️ Crude in July vs. crude in December is linked by the cash-and-carry arbitrage — if you can store oil and borrow money, you can trade between them. ✔️ But this link can break due to storage limits, credit stress, or physical bottlenecks. 📉 Sparse Info = Rich Signal Environment Commodity markets have slow, irregular data flows. 📊 Corn has a USDA report once a month. 🌦️ Natural gas is driven by seasonal weather. 🧮 Reimagining Style Premia as Derivatives on the Futures Surface Ben presents a brilliant concept: Treat the term structure surface (futures prices over time & tenor) as a 2D mathematical object. 📈 1. Time-Momentum (∂/∂t) If the whole curve is rising, go long; if falling, go short. 🔁 2. Trend-of-Trend (∂²/∂t²) Is the trend speeding up or slowing down? ⛰️ 3. Backwardation Momentum (∂/∂τ) Measures the slope of the term structure. • Long steep backwardated curves (tight supply) • Short contango curves (oversupply) This is a classic QIS factor — also called “roll yield” in many bank products. 📉 4. Carry via Convexity (∂²/∂τ²) This is the heart of commodity carry. Not just slope, but curve shape. • If front rolls down faster than back = profit • Real edge comes from storage risk, seasonality, geopolitical tail risk Ben shows that this convexity drives most of the performance in carry strategies, not just roll yield. 🔄 5. Basis Momentum (∂²/∂t∂τ) How is the slope changing over time? • Steepening = front rising faster → bullish • Flattening = back catching up → neutral/bearish ⏳ This gives you timing for curve shape trades. 🧠 Math Meets Market: Levy Area & Rough Paths To capture lead-lag dynamics, Ben introduces the Levy area: • A nonparametric way to measure if one asset consistently leads another (e.g., gasoline leads crude). • Handles nonlinear, noisy relationships better than correlation or Granger causality. • Used both across assets and along a single curve (e.g., front vs. back contract in copper). 🗓️ Weekly Seasonality is Real Ben’s team found one real pattern: • Thursdays/Fridays show stronger moves in energy futures. • Why? Refiners hedge closer to weekends as they learn the latest weather — which drives demand for heating or driving. 👇 Let’s trade insights in the comments. https://lnkd.in/eh8CyybV #QuantFinance #SystematicTrading #Commodities #CarryTrade #Momentum #FactorInvesting #QIS #MacroTrading #LevyArea #RoughPaths #CommodityCurves #Convexity #AlphaSignals

Mondelez reported Q4 earnings yesterday and one of their comments made me doubt for a minute. They read in the inverted cocoa futures market that the cocoa S&D eventually balances. This is incorrect. An inverted futures curve reflects -most of the time- underlying physical tightness of a commodity. Market participants are prepared, or forced to pay a premium for nearby delivery of the commodity, as apparently they cannot wait for future deliveries. I say most of the time, as especially cocoa futures, but it happens in other commodities, have been squeezed, meaning, participants have taken a dominant long position to corner bonafide short hedgers, with the sole purpose to move the price. If futures prices invert for this reason, without justification from the physical underlying, what then usually happens is that cash differentials collapse (including product ratios), to pull physical cocoa beans to the exchange for physical delivery. This happened in the famous July 2010 squeeze, which eventually failed as there was no physical shortage, and the inverse collapsed shortly after the July delivery. An inverted futures market, meaning lower prices on the deferred, is not a prediction of lower future prices which people sometimes think. These are current prices for deferred delivery, period. Nor is it a prediction for the S&D to become balanced, after three consecutive deficits and potentially a fourth one. It is a reflection of current (extreme) tightness. And if Mondelez, or other chocolate confectionary companies need futures in exchange of products on 2025 positions, it can't cover that with 2026 futures. If the structural supply issues are not being resolved, and demand destruction does not accelerate faster than their reported minus 2% ish, then the futures market should remain inverted, and elevated. Maybe counterintuitive to Mondelez, but if hypothetically the market would price a large surplus next year, the futures curve could flatten, or even move into a carry, which leads to much less deferred downside price pressure than the nearby. For the rest, "emerge stronger" is obligatory and popular management language to engage employees and investors in tough situations. Time will tell. "Closely monitor and remain agile" is also popular management language. It means something like "Yeah yeah, we are in a tough spot, but at the moment we can't do much more than watching the market and hope for the best". I think this applies to many among the chocolate confectioners at the moment. The cocoa S&D will balance, and move to a surplus over time, slowly but surely. That is the purpose of the futures market, by impacting behavior of producers, processors and consumers. It's a slow, painful, and fascinating process. Seatbelts fastened.

Commodity Futures Surfaces and the Cash-and-Carry Glue (S7E17) My guest this episode is Ben Hoff, Global Head of Commodity Strategy and Research at Société Générale. Ben started his career in rates before making the jump to commodities, and that lens – shaped by curve arbitrage, convexity, and carry – colors everything he does. In this conversation, we explore how commodities differ fundamentally from other asset classes: the importance of cash-and-carry economics, the sparse information cadence that rewards technical models, and the physical realities that challenge purely quantitative approaches. We also dive into Ben’s more recent work on the geometry of the futures surface, how convexity and skewness may be misunderstood, and why tools like Lévy area might help uncover non-linear structure in the data. Whether you’re deep in the weeds of term structure trading or just curious about how to systematize chaos in barrels and bushels, this is a conversation you won’t want to miss. 📝 Chapters & Transcript: https://lnkd.in/e6MNrtup Spotify: https://lnkd.in/evaJefE9 Apple: https://lnkd.in/eJjugB_J YouTube: https://lnkd.in/eU9c5PES

📊 Exploring Statistical Arbitrage: A Case Study on Brent and WTI Crude Oil Futures The energy markets are full of opportunities hidden in the pricing dynamics of commodities. Recently, I completed a personal project developing and backtesting a statistical arbitrage strategy for Brent and WTI crude oil futures, exploring how mean-reversion principles can uncover actionable insights. 📈 What This Project Covers: • Leveraging z-scores to detect deviations and trigger trading signals. • Incorporating dynamic risk management with volatility-based position sizing and adaptive stop-loss mechanisms. • Backtesting the strategy exclusively on historical Brent and WTI data, with performance validated through metrics like the Sharpe Ratio. 💡 Disclaimer: This project is for educational purposes only and is based solely on historical data. It is not an investment recommendation but a demonstration of how quantitative techniques can be applied to identify market opportunities. If you're interested in quantitative trading and the commodities market, let’s connect and share ideas! I'd love to hear your thoughts on statistical arbitrage or other trading strategies. #StatisticalArbitrage #QuantitativeTrading #CommodityTrading #BrentWTI #EnergyMarkets #TradingStrategies #QuantitativeFinance #RiskManagement #DataDriven #AlgorithmicTrading #Backtesting #FinancialMarkets #FuturesTrading #MarketInefficiencies

Yesterday, I had the privilege of presenting my working paper "𝗖𝗼𝗺𝗺𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗿𝘀 𝗼𝗳 𝗖𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆 𝗙𝘂𝘁𝘂𝗿𝗲𝘀?" at the University of Basel. This paper, co-authored with Tom Dudda, Duc Khuong Nguyen, and Tony Klein, examines which types of drivers (fundamental, financial, or uncertainty) best predict the co-movement of commodity futures. Our findings show that 𝙛𝙞𝙣𝙖𝙣𝙘𝙞𝙖𝙡 𝙙𝙧𝙞𝙫𝙚𝙧𝙨 have strong short-term predictive power across most commodities. On the other hand, 𝙛𝙪𝙣𝙙𝙖𝙢𝙚𝙣𝙩𝙖𝙡 𝙙𝙧𝙞𝙫𝙚𝙧𝙨 and 𝙪𝙣𝙘𝙚𝙧𝙩𝙖𝙞𝙣𝙩𝙮 𝙛𝙖𝙘𝙩𝙤𝙧𝙨 become more influential over longer horizons, such as on a monthly basis. Interestingly, this lead-lag relationship varies over time. For instance, financial drivers like S&P500 returns or the VIX are especially prominent during the period known as the 𝘍𝘪𝘯𝘢𝘯𝘤𝘪𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘊𝘰𝘮𝘮𝘰𝘥𝘪𝘵𝘺 𝘔𝘢𝘳𝘬𝘦𝘵𝘴, when a large influx of financial hedgers entered the market. These financial hedgers joined commodity markets for rent-seeking and diversification, aiming to complement their stock-bond portfolios. Exchange-traded funds based on commodity indices offered them opportunities for long exposure to commodities. Unlike traditional players, these financial hedgers adjust positions based on stock and bond performance rather than on commodity fundamentals. Our research shows that the growing presence of financial hedgers impacts the predictive power of commodity drivers: 𝘄𝗵𝗲𝗻 𝗺𝗼𝗿𝗲 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗵𝗲𝗱𝗴𝗲𝗿𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝗶𝘃𝗲, 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁, 𝗮𝗻𝗱 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 𝗹𝗼𝘀𝗲 𝘀𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲𝗶𝗿 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵. The working paper is available from here: https://lnkd.in/ez63dDzT Special thanks to Aya Kachi and Pascal Gantenbein for their warm hospitality and to the seminar participants for their insightful remarks and questions. Fakultät Wirtschaftswissenschaften, TU Dresden Utrecht University School of Economics Finance @ Utrecht School of Economics European Centre for Alternative Finance Faculty of Business and Economics, University of Basel