Commodities, Volume 4, Issue 2 (June 2025) – 8 articles

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. Full article

Oil inventories are essential in alleviating realized and anticipated supply shocks and represent a key market indicator. This study examines the responses of global and country oil inventories to supply shocks under tight and loose market conditions. We utilize an expanded version of the GVAR model, adding the OECD oil inventories variable, incorporating major oil-producing countries: Iran, Russia, and Venezuela, and extending the coverage period. Our simulations indicate that a negative global supply shock significantly affects oil inventories under “tight” market conditions. The model correctly predicts the trajectory of changes to oil inventories in South Korea following a supply shock to Russian production in tight markets and Iranian output in loose markets. This case also shows that commercial players, using their inventories as a buffer, can negate government attempts to maintain constant levels of reserves. Overall, the response to the oil inventory tends to vary across producing and importing countries and market conditions. Such dynamics highlight potential problems with specific policies, such as using inventories as a buffer to alleviate price fluctuations or disrupting the oil production of individual countries through sanctions, as these measures oftentimes result in unintended consequences due to complex interconnections of the global oil market. Full article

In this study, Gaussian process (GP) regression is used to normalize observed commodity data and produce predictions at densely interpolated time intervals. The methodology is applied to an empirical oil price dataset. A Gaussian kernel with data-dependent initialization is used to calculate prediction means and confidence intervals. This approach generates synthetic data points from the denoised dataset to improve prediction accuracy. From this augmented larger dataset, a procedure is developed for estimating an upcoming crash-like behavior of the commodity price. Finally, multiple data-science-driven algorithms are used to demonstrate how data densification using GP regression improves the detection of forthcoming large fluctuations in a particular commodity dataset. Full article

The exploitation of the Amazon biome in search of net profit, specifically in the production of cocoa (Theobroma cacao) and açaí (Euterpe oleracea), has caused deforestation, degradation of natural resources, and high greenhouse gas (GHG) emissions, highlighting the urgency of improving the environmental, economic and social sustainability of these crops. These species were selected for their rapid expansion in the Amazon, driven by global demand, their local economic relevance, and their potential to either promote conservation or drive deforestation, depending on the production system. This study analyzes the pillars of environmental, social, and economic sustainability of cocoa and açaí production systems in the Amazon, comparing monoculture, agroforestry, and extractivism to support forest conservation strategies in the biome. Analysis of the environmental life cycle, social life cycle, and economic performance were used to determine the carbon footprint, the final point of workers, and the net profit of the activities. According to the results found in this study, cocoa monoculture had the largest carbon footprint (1.35 tCO₂eq/ha), followed by agroforestry (1.20 tCO₂eq/ha), açaí monoculture (0.84 tCO₂eq/ha) and extractivism (0.25 tCO₂eq/ha). In the carbon balance, only the areas outside indigenous lands presented positive carbon. Regarding the economic aspect, the net profit of açaí monoculture was USD 6783.44/ha, extractivism USD 6059.42/ha, agroforestry USD 4505.55/ha, and cocoa monoculture USD 3937.32/ha. In the social sphere, in cocoa and açaí production, the most relevant negative impacts are the subcategories of child labor and gender discrimination, and the positive impacts are related to the sub-category of forced labor. These results suggest that açaí and cocoa extractivism, under responsible management plans, offer a promising balance between profitability and environmental conservation. Furthermore, agroforestry systems have also demonstrated favorable outcomes, providing additional benefits such as biodiversity conservation and system resilience, which make them a promising sustainable alternative. Full article

Debt-for-nature instruments are financial transactions that allow countries to restructure and reduce foreign debt in exchange for investments in environmental conservation measures. Can debt-for-nature instruments attract more capital for biodiversity finance? Debt-for-nature instruments first appeared in the market in the 1980s; however, they have seen a recent surge in popularity, with transactions predominantly focused on marine conservation. These transactions have gained attention for their size, innovative nature, and conservation focus. However, they have also faced criticism surrounding sovereignty, effectiveness, and transaction costs. The descriptive qualitative analysis of a comprehensive and global sample of the eight tripartite type debt-for-nature instruments brought to market since 2015, with a detailed case study of the Belize transaction, indicates that such deals may be costly to negotiate, the use of blue bond labeling can be misleading, conservation benefits are limited, and they have limited replicability. On the positive side, these deals have introduced innovative structures to unlock additional funds for conservation. The best examples are structured with a larger financial commitment to nature and strong enforcement mechanisms. In some cases, the transaction laid the groundwork for future marine conservation funding and commitments. Debt-for-nature instruments are not a silver bullet for either environmental impact or debt refinancing; however, the benefits of recent transactions indicate a role for such innovative instruments in conservation finance. Full article

The COVID-19 pandemic and the Russia–Ukraine war have struck the world’s energy markets. This study analyzed how the recent unstable fossil fuel markets impacted the Japanese electricity contracts, classified as extra-high-, high-, and low-voltage contracts. Multiple structural break tests were conducted to endogenously determine breaks affecting electricity prices during January 2019 to November 2022. By incorporating the effects of these breaks in the autoregressive distributed lag (ARDL) model, the study analyzed the effects of natural gas, coal, and crude oil prices on the types of electricity contract prices. The results of the analyses indicated a surge in electricity prices for low- and high-voltage contracts driven by an increase in natural gas. The results imply the importance of providing proper financial support to mitigate the effects of soaring electricity prices and implementing policies to diversify the electricity generation mix in Japan. Full article

This study examines the volatility and hedging effectiveness of commodities, specifically gold and oil, on the Indian stock market, focusing on both aggregate and sectoral indices. Data have been collected from 1 January 2021 to 31 December 2024 to cover the post-COVID-19 period. Utilizing the Asymmetric Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (ADCC-GARCH) model, we analyze the volatility spillovers and time-varying correlations between commodity and stock market returns. The analysis of spillover connectedness reveals that both commodities exhibit limited and inconsistent hedging potential. Gold demonstrates low and stable spillovers in most sectors, indicating its diminished role as a reliable safe-haven asset in Indian markets. Oil shows relatively higher but volatile spillover effects, particularly with sectors closely tied to energy and industrial activities, reflecting its dependence on external economic and geopolitical factors. This study contributes to the literature by providing a sector-specific perspective on commodity–stock market interactions, challenging conventional assumptions of hedging efficiency of gold and oil. It also emphasizes the need to explore alternative hedging mechanisms for risk management in the post-crisis phase. Full article

This study analyzes the market efficiency of crude oil markets, namely Brent and West Texas Intermediate (WTI), during three different periods: pre-COVID-19, during the COVID-19 pandemic, and during the ongoing Russia–Ukraine military conflict. To evaluate the efficiency of crude oil markets, wavelet entropy is computed from price, return, and volatility series. Our empirical results show that WTI prices are predictable during the Russia–Ukraine military conflict, but Brent prices are difficult to predict during the same period. The prices of Brent and WTI were difficult to predict during the COVID-19 pandemic. Returns in Brent and WTI are more difficult to predict during the military conflict than they were during the pandemic. Finally, volatility in Brent and WTI carried more information during the pandemic compared to the military conflict. Also, volatility series for Brent and WTI are difficult to predict during the military conflict. These findings offer insightful information for investors, traders, and policy makers in relation to crude oil energy under various extreme market conditions. Full article