Winter 2024

This paper devises an innovative tactical allocation strategy for commodities that leverages the sentiment extracted from commodity-specific newswires alongside the predictive content of traditional commodity futures characteristics. Implementing the newswire tone-overlay on 26 commodities using an array of commodity futures characteristics, the authors show that significant out-of-sample profitability improvements can be obtained vis-à-vis the “plain vanilla” traditional allocations. The outperformance of the tone-overlay strategy is confirmed by a placebo test, and survives the consideration of trading costs and various choices of hyperparameters in the tone-overlay strategy. Furthermore, a cross-validation process for the hyperparameters bolsters the outperformance of the tone-overlay strategy. The research reveals a temporary overreaction of commodity futures prices to newswire tone as the channel of the outperformance.

This study analyzes the impact of climate change on international commodity prices over the period 1961-2020. The findings show that climate change leads to a persistent upsurge in international commodity prices, with the highest effect on energy prices, followed by fertilizer prices, and with the least impact on food prices. Climate change has also intensified the short-term impacts of the prices of fertilizer and energy on food prices. These findings suggest that climate change may result in monetary authorities encountering a more challenging inflation-output trade-off. Therefore, the effect of climate change on commodities prices creates a new obstacle to monetary policy strategies in ensuring price stability, particularly in countries highly dependent on imported staple commodities and where food items constitute a substantial portion of their consumption basket.

Despite commodity terms-of-trade (CTOT) affecting GDP growth, child mortality rates and public debt, its determinants are underexplored. Employing data from 178 countries (1962 to 2020), the authors examine the short and long-run effects of economic growth, the U.S. dollar exchange rate, stock price volatility and real interest rates on CTOT growth. Results reveal asymmetric effects between exporters and importers and show that the U.S. dollar exchange rate exhibits opposite effects over the short and long-run due to inelastic commodity demand. The authors find that emerging market growth provides the most consistent effect across subsamples, emphasizing the global importance of developing countries.

The authors use prevailing machine learning models to investigate the predictability of futures returns in 22 commodities with commodity-specific and macroeconomic factors as predictors. Out-of-sample prediction errors for the majority of futures contracts are lowered compared with those obtained by the baseline models of AR(1) and forecast combinations. Using Shapley values to explain feature importance, we identify dominant predictors for each commodity. A long–short portfolio strategy based on monthly Light Gradient-boosting Machine predictions outperforms the benchmark linear models in terms of annual return, Sharpe ratio, and maximum drawdown.

In this paper, the authors estimate the skewness of the unconditional distribution of energy returns and test its statistical significance. The authors compare the performance of traditional and robust tests for skewness with those based on the implied unconditional skewness in a TGARCH model with Gram-Charlier (TGARCH-GC) innovations. The paper also analyzes the implications of TGARCH-GC skewness for tail risk through evaluation of Value-at-Risk (VaR) and expected shortfall (ES) accuracy. The results show that Crude oil (Brent and WTI) and Gasoline returns are negatively skewed, while the paper does not find evidence of a skewed distribution for other energy returns such as Heating oil, Kerosene and Natural gas. This indicates that the returns of the former are likely to encapsulate more largely the effect of negative shocks and so present higher tail risk than those of the latter. These results differ from traditional and robust tests for skewness, providing important information on how to improve mean-variance risk management measures. Indeed, the paper finds that the three-moment VaR and ES measures based on the third-order Cornish Fisher (CF3) expansion for the unconditional distribution of returns are considerably improved over their corresponding two-moment ones. The paper adopts CF3 to disentangle skewness effects from kurtosis in tail risk.

This study uses high-frequency stock price data to uncover the factors that drive the returns of companies operating in the electric vehicle and battery supply chain. The study’s statistical approach separates out the influence of systematic risk factors and factors unique to the supply chain. EV and battery stock returns are found to be exposed to a market and a tech factor. There is also evidence for an EV factor, unrelated to the broader market, that generates common variation among these returns. Lithium companies are found to be exposed to their own unique risk factor.

The article analyzes the impact of the financialization of commodity futures markets on informational efficiency. Although the theoretical insights give no clear message about the impact of commodity index trading on informational efficiency, empirical evidence shows that it improved it. These results are robust to the different measures of informational efficiency. It appears also that even non-index commodities have benefited from this improvement. The authors explain this effect through the enhanced liquidity provided by commodity index traders and to the specific information they bring to the market.

Among the many previously unthinkable moments of 2020, one of the strangest occurred on April 20, 2020, when the price of crude oil fell below zero. Then the crude market collapsed completely, falling almost $40 in 20 minutes, to close at –$38. It was the lowest price for oil in the history of the New York Mercantile Exchange—and in all likelihood the lowest price in the millennia since humans first began burning the stuff for heat and light. Over the course of a few hours on April 20, 2020, a guy called Cuddles and eight of his pals from the freewheeling world of London’s commodities markets rode oil’s crash to a $660 million profit.

Tail risk occurs when an unexpected event with large consequences occurs, such as the Pandemic of 2020, the Russian invasion of Ukraine in February 2022, or the financial panic in September 2008 with the very messy and mismanaged bankruptcy of Lehman Brothers and the bail-out of American International Group. This type of tail risk comes under the heading of “surprises” or “black swans”. Tail risk can also occur when there is a known risk around an event or scenario that divides market participants into two camps with widely differing expectations of what might happen. When two scenarios are present, we would argue that the market prices reflect the probability-weighted average of the outcomes associated with the two scenarios. Geopolitical risk and its potential to disrupt oil markets falls into the latter category, where there are two differing scenarios. One scenario sees the potential for widening conflict that could disrupt oil markets while an alternative scenario sees geopolitical tensions not rising to the level to impact oil supply routes or production sites. The paper’s research takes a practical approach to assessing the degree to which market participants are concerned about geopolitical tail risk by studying the options markets for crude oil. Unusually active participation in out-of-the-money oil call options well above the current spot price is taken as an indicator of worries about the price of oil spiking to exceptionally elevated levels, which probably would not occur unless military activity in countries key to global oil markets sharply increased.

Over the past decade, the global economy has witnessed fundamental changes, as well as geopolitical and macroeconomic crises, which have affected not just the economies of individual countries, but also the global economic links between the regions, countries, and various segments of the global supply chains. The magnitude of such impacts differs by individual economic sectors and countries. In this regard, Saudi Arabia and the Kingdom’s relationship to the global oil market present an interesting case study. On the one hand, Saudi Arabia’s diversification efforts have led to increased integration into the global economic system and the rapid development of its domestic markets. The magnitude of oil price swings observed over the recent decade illustrates the increasing vulnerability of the Kingdom’s oil sector to macroeconomic and geopolitical shocks. On the other hand, OPEC, and later the OPEC+ countries, have demonstrated increased coordination in their attempts to successfully stabilize the world oil market. This paper assesses the current relation of Saudi Arabia’s economy, more specifically, its level of GDP and oil production, to various changes in the global economy.

This paper mainly discusses inferring crude-oil-market fundamentals through price-relationship data, largely through the perspective of a commodity futures trader. In doing so, the paper covers (1) the promise of big data; (2) the reality of data “black holes”; (3) the wealth of futures price data; (4) what futures prices reveal about petroleum-complex fundamentals; and (5) caveats on the use of price data. The paper also covers a publicly available report from the U.S. CFTC, from which researchers can draw conclusions about market structure. Even though this paper focuses on using market signals to infer the fundamentals in the petroleum complex, that emphasis is not meant to undercut the need for accurate, politically neutral data in these life-sustaining markets, given how important it is to avoid a messy energy transition and energy poverty. That said, even if official data sources become controversialized, large-scale supply-and-demand shifts leave footprints in futures-price relationships, from which one can arguably infer the market’s fundamentals and thereby make sound decisions regarding these crucial markets, at least in the near term.

Sjur Westgaard is a Professor of Industrial Economics and Technology Management at the Norwegian University of Science and Technology.  His main research interests cover financial risk management and forecasting for both financial institutions and corporates.  Professor Westgaard is an expert on European energy markets and also on commodity trading debacles.  His applied research is informed by industry experience, which includes his role as an investment portfolio manager for an insurance company, as a project manager for a consultant company, and as a credit analyst for an international bank.  He is one of the founders and editors of the Journal of Commodity Markets in addition to serving as Associate Editor of the Journal of Energy Markets.  We are honored to have the opportunity to interview Professor Westgaard for the Winter 2024 issue of the CID.

In the Winter 2024 issue of the CID, we feature an interview with Tom Kopczynski, formerly of Blenheim Capital. For “the first time, commodity market wizard, hedge fund manager and legendary cocoa trader Tom Kopczynski, speaks about his trading career at Blenheim, once the biggest commodities hedge fund in the world.”  In 30 years, Blenheim Capital only posted an annual loss five times, and in that period, the fund twice returned more than 100 percent, and a dozen times more than 20 percent. We are grateful to Strong Source’s Co-Hosts, Martijn Bron and Alexander Sterk, for allowing the CID to reproduce their podcast’s interview with Tom Kopczynski.

In the Winter 2024 issue of the CID, we interview Editorial Board member, Jodie Gunzberg, CFA, on her prescient call on Sky News (U.K.) from ten years ago that crude oil prices could reach $30 per barrel. What led her to accurately make this call? We also ask Jodie Gunzberg on what advice she would give to students or young professionals who are interested in a career in the commodities markets.