Commodity shocks can generate material cross-border exposure because energy, food, industrial metals, and critical minerals are essential inputs to production and consumption. This article develops a transparent, margin-based screening framework for eight commodity categories using 2024 WITS/UN Comtrade reporter-to-world top-country data. The empirical design combines export- and import-side concentration, normalized entropy, an explicit non-substitutability scenario parameter, and a dimensionless trade-scale adjustment. A maximum-entropy independence matrix, pijk = eik mjk, is retained only as a null exposure benchmark; because it is rank one and contains no bilateral information beyond the observed margins, the study does not interpret it as a recovered trade network and does not report graph centrality. The revised results identify copper ores as the highest scale-adjusted vulnerability layer, followed by crude petroleum and liquefied natural gas; lithium carbonates remain highly concentrated but rank lower once trade scale is normalized dimensionlessly. Cobalt ores are retained only as a diagnostic illustration of HS-proxy fragility and are excluded from headline country rankings. Country-level tables report observed exporter and importer shares directly rather than redundant composite transmitter and receiver scores. Sensitivity analysis shows that the commodity ordering is robust to uniform and compressed non-substitutability scenarios and to broad parameter perturbations. Historical plausibility checks against the 2021–2022 European gas shock, Indonesia’s nickel-ore export restrictions, and the 2022 wheat disruption are directionally consistent with the screening signals, although they do not constitute causal validation. The framework is therefore intended as an early-warning screening device, not a graph-theoretic propagation model or a macroeconomic-loss estimate.
Phosphate rock is an irreplaceable resource for the production of fertilisers, which are essential for global food security. Its extraction is highly concentrated in a limited number of countries and, together with rising demand and ongoing environmental challenges, creates a supply system vulnerable to geopolitical, regulatory, and logistical disruptions. This study aims to identify the most effective methodology for assessing the risk of a severe disruption in the global phosphate rock supply chain. To this end, three approaches are compared: Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA) and the As Low As Reasonably Practicable (ALARP) criterion. The methodology defines the “top event” as a prolonged disruption to the global supply, which is then decomposed into geological, economic, geopolitical, regulatory, and logistical failure scenarios. The findings indicate that the primary risk does not stem from the geological availability of phosphate rock itself, but rather from the aforementioned external factors, particularly environmental regulation. In this context, the concentration of production in only a few countries acts as a significant amplifier of systemic vulnerabilities. From a methodological standpoint, the integration of FMEA, FTA, and ALARP provides a more robust and comprehensive framework by combining quantitative assessment, causal analysis, and risk prioritisation. This integrated approach therefore constitutes an effective methodology for evaluating complex risks associated with critical minerals.
Commodity prices are notoriously hard to forecast, and whether the returns of commodity exchange-traded funds (ETFs) can be predicted remains an open question. We compare three deep learning models, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, for forecasting the returns of six Deutsche Bank commodity ETFs covering agriculture (DBA), base metals (DBB), broad commodities (DBC), energy (DBE), oil (DBO), and precious metals (DBP). Using daily price data from January 2007 to December 2025, we predict daytime returns (open to close) and overnight returns (previous close to open) separately, over five horizons of 1, 5, 30, 60, and 180 trading days. Each model sees a 20-day window of price-based features, returns, rolling averages and volatilities, momentum, and recent lags, built from all six ETFs. All models are trained on a strict chronological split and judged by two simple, decision-oriented measures: how often they call the direction correctly, and the risk-adjusted return (annualized Sharpe ratio) of a stylized long–short strategy that ignores transaction costs. Formal significance tests with HAC corrections for overlapping targets, bootstrap confidence intervals, and comparisons with ARIMA, random forest, and simpler benchmarks corroborate strong predictability in overnight DBP and daytime DBB at medium horizons. Predictability turns out to be highly specific to the asset, the trading session, and the horizon. Overnight returns of the precious metals ETF (DBP) are by far the most predictable: the correct direction is called 71.6% of the time at 60 days and 76.7% at 180 days, with Sharpe ratios reaching about 15. Base metals (DBB) daytime returns are predictable at 30 days and oil (DBO) daytime returns at 180 days, whereas one-day-ahead forecasts and agricultural returns (DBA) stay essentially unpredictable. The Transformer has a slight edge at longer horizons and the GRU at shorter ones. Key directional accuracy and Sharpe ratio results are confirmed by Newey–West HAC significance tests and Diebold–Mariano forecast comparison tests with the Harvey–Leybourne–Newbold small-sample correction; HAC standard errors at the 180-day horizon exceed naïve OLS errors by a factor of approximately 7.4, and we explicitly flag results that do not survive this correction. A three-fold expanding walk-forward validation scheme corroborates the main findings, with DBP overnight and DBO daytime predictability persisting across all evaluation windows. Deep learning architectures statistically and economically outperform logistic regression, ridge regression, and momentum baselines on the most predictable configurations. An anomalous failure of all models on DBA daytime returns at the 180-day horizon is diagnosed as a regime-driven artefact associated with post-2021 commodity inflation, not a general feature of agricultural return dynamics. The broader lesson is that splitting returns into daytime and overnight components exposes predictable structure that conventional close-to-close returns hide.
This study examines the dynamic relationship between financial development and total energy production in emerging market economies (EMEs) using a balanced panel of 20 countries over the period 2000–2020. Unlike much of the existing literature that focuses on energy consumption or specific energy types, this paper conceptualises total energy production as an aggregate supply-capacity indicator that captures infrastructure investment, capital intensity, and long-run energy system expansion. Employing a panel autoregressive distributed lag model with the Pooled Mean Group (ARDL–PMG) estimator, the analysis distinguishes between long-run equilibrium relationships and heterogeneous short-run adjustment dynamics. The results reveal a stable long-run reciprocal relationship between financial development and total energy production, suggesting that deeper financial systems are associated with higher energy production capacity over time, while expansion in energy production is also linked to financial deepening. Short-run dynamics, however, are asymmetric, indicating the presence of adjustment frictions and investment lags in capital-intensive energy sectors. Robustness checks using a two-step System GMM estimator confirm the qualitative consistency of the main findings after accounting for potential endogeneity and simultaneity. Overall, the results highlight the importance of financial system development in supporting aggregate energy supply expansion in EMEs, while underscoring the need to account for transitional constraints and differing adjustment speeds across sectors and countries. The findings offer policy-relevant insights for aligning financial development with energy infrastructure investment during periods of structural transformation.
Gold mining firms operate in an environment characterized by substantial commodity price volatility, capital intensity, and long investment horizons. Traditional deterministic financial planning frameworks are insufficient to capture the nonlinear and asymmetric risks associated with gold price fluctuations. This study develops a simulation-based scenario planning framework for gold mining firms, integrating deterministic scenario analysis with stochastic price modeling. Using a stylized and benchmark-calibrated financial model intended for methodological illustration rather than firm-specific forecasting, the study evaluates the impact of gold price uncertainty on key financial indicators, including EBITDA, free cash flow, and net present value. Monte Carlo simulations indicate substantial dispersion in financial outcomes, with approximately 28% of simulated realizations producing negative Net Present Value outcomes under baseline assumptions. The results further demonstrate that volatility significantly amplifies downside exposure despite positive expected returns, thereby highlighting the limitations of deterministic planning approaches. The findings suggest that probabilistic scenario-based financial planning provides a more comprehensive framework for evaluating financial resilience and tail-risk exposure in commodity-dependent industries.
Natural gas markets exhibit violent, non-linear volatility regimes. The 2022 energy crisis introduced persistent supply shocks and cross-Atlantic spillover effects. Traditional valuation models assume single-regime environments; consequently, they systematically misprice storage assets during extreme stress. We propose a valuation framework tailored to these specific market volatilities. By integrating a hidden Markov model (HMM) and Diebold–Yilmaz (DY) spillover indices into a stochastic dynamic programming (SDP) engine, the framework isolates the persistence of stressed market conditions. The model captures structural arbitrage opportunities. We demonstrate a 197 percent net present value (NPV) premium over conventional benchmarks using synthetic data. The optimal policy expands inventory holding periods during high spillover intensity. The algorithm executes decisions with sub-millisecond latency. This approach provides a computationally viable tool for high-frequency risk management.
This research examines the clustering structure and volatility spillover among steel-related products in monthly data from July 2004 to September 2025. Using various clustering methods, K-means, hierarchical techniques and market network analysis with correlations, four distinct marketing clusters have been identified: (1) US (United States) steel products, (2) global cyclical raw materials, (3) US iron ore market, and (4) global base metals. The overall volatility spillover index stands at 15.39%, exhibiting significant dynamics that vary over time, driven by major economic events, including the 2008 global financial crisis, the 2015 Chinese currency devaluation, the COVID-19 outbreak, the 2022 Ukrainian conflict, and the 2025 Trump trade tariffs. The primary driver of volatility in global trade is US carbon steel wire prices, while the largest net recipient of volatility shocks is the global copper price. These findings have key implications for understanding the global interconnectedness of steel markets in the current context.
Global supply chain disruptions, most acutely demonstrated during the COVID-19 pandemic, have exposed fundamental tensions between efficiency-oriented design and the adaptive capacity required for resilience. This paper addresses a critical gap in the existing literature: the absence of an integrative, operationalisable framework that treats sustainability and resilience as mutually reinforcing strategic objectives rather than competing trade-offs. Employing a systematic literature review guided by PRISMA protocols, complemented by comparative analysis of documented organisational responses across multiple sectors and commodity markets, the study identifies four primary pathways through which sustainability investments generate resilience: structural diversification, information and visibility, social capital and trust, and adaptive capabilities. The principal finding is that sustainability practices, particularly those enhancing supply network visibility, structural diversification, and workforce stability, create option value that becomes strategically decisive during periods of disruption. A decision intelligence framework is proposed that translates these insights into three managerial tools: a sustainability–resilience assessment matrix, a disruption scenario analysis tool, and a capability development roadmap. The framework challenges the prevailing trade-off assumption by demonstrating that efficiency, sustainability, and resilience can function as complementary dimensions of supply chain performance. Findings carry particular relevance for commodity-dependent supply chains, where price volatility, trade structure rigidity, and resource concentration constitute persistent sources of systemic disruption. Theoretical contributions include the integration of supply chain resilience theory, sustainable operations management, and decision science under deep uncertainty.
This paper investigates whether participation in Zambia’s social cash transfer programme (SCTP) improves household dietary diversity among ultra-poor rural households. While cash transfers are widely implemented across sub-Saharan Africa as social protection measures, empirical evidence regarding their impact on nutritional status remains mixed. This study focuses on dietary diversity, a proxy for nutrition quality, and uses data from the 2015 Rural Agricultural Livelihood Survey (RALS). The analysis employs propensity score matching to control for demographic differences between recipient and non-recipient households, followed by a regression analysis to examine the association between SCTP participation and dietary diversity scores. The findings reveal no statistically significant association between receiving social cash transfers and higher household dietary diversity. In contrast, positive predictors of dietary diversity included household remittances, own production of animal-source foods, and maize sales. Notably, households that relied on foraging exhibited significantly lower dietary diversity, suggesting foraging may be a coping strategy among food-insecure households. These results imply that while the SCTP may enhance household income stability, it does not necessarily translate into improved diet quality. This study contributes to the ongoing policy debate on the effectiveness of cash-based interventions in improving nutrition outcomes. It highlights the need to complement cash transfers with interventions that support food production and access, particularly in rural settings where market and infrastructure limitations persist.
Global critical mineral production patterns differ markedly across the metals needed for advanced energy technologies. This study examines the extraction and processing landscape, in the year 2024, of six key commodities—lithium, cobalt, aluminum, nickel, manganese, and copper—to identify who the major players (countries and corporations) are in the critical mineral space and to understand what they are mining, where they are mining, and where are they sending their ore to be processed. This study aims to provide a snapshot of the critical mineral supply chain that serves as a useful resource for researchers and policymakers seeking to understand and improve the critical mineral supply chain. We analyze company financial filings, government datasets, and other public and proprietary sources for the year 2024. Then, we calculate production volumes and identify geographic and corporate concentration. The results show that copper and aluminum production and processing are relatively diverse, while lithium and cobalt extraction and processing are highly concentrated among a few countries and dominant firms. Nickel and manganese occupy an intermediate position, displaying moderate diversity with emerging signs of consolidation.
Poultry price instability remains a critical challenge for food security in Nigeria. This study examines the relationship between poultry price volatility (PPV), exchange rate (LEXR), and inflation (LCPI) from 1991 to 2024 using the Autoregressive Distributed Lag (ARDL) model. Descriptive results show that PPV had the highest variability (mean 0.65; standard deviation 1.07), while LEXR and LCPI were relatively more stable. Trend analysis indicates that poultry price volatility was high in the early 1990s but declined steadily after 2005, coinciding with persistent inflation and cycles of exchange rate depreciation and appreciation.Unit root and bounds tests confirm that the variables werecointegrated, with an F-statistic of 4.50 exceeding the upper bound at 5 percent significance. The long-run estimates reveal that inflation hada negative effect on poultry price volatility (−0.109), while the exchange rate exerteda positive effect (0.2702). The errorcorrection term (−0.336) indicates a 33.6 percent adjustment to equilibrium each period. In the short run, changes in inflation (0.942) and lagged exchange rate variations significantly influenced poultry price volatility. These findings underscore the importance of stabilizing exchange rates and controlling inflation to reduce price volatility in Nigeria’s poultry sector.
Mexican octopus fisheries play an important role in both domestic and international seafood markets, yet little is known about the determinants of retail price in the national frozen octopus sector. This study examines how trade flows and domestic demand interact to shape price dynamics, providing insights into sustainability challenges. Multiple linear regression was employed to test the influence of economic, production, and trade variables on retail prices, based on annual data from 2010 to 2024. The best-performing model identified average daily salary, apparent consumption and import value as significant determinants, explaining more than 90% of the observed variation. Results show that rising salaries and greater domestic consumption are exerting upward pressure on prices, while imports, although limited, contribute to price moderation. Export values have declined, signaling a weakening role of the international markets. These findings suggest that domestic demand is becoming increasingly important for sustaining value in the sector, but this shift could intensify fishing pressure on wild stocks. Strengthening compliance with management measures and aligning policies with domestic market realities are crucial to ensuring long-term sustainability of the Mexican octopus supply.
The transition from non-renewable to renewable energy sources has emerged as a pressing global issue, driven by concerns over climate change, resource depletion, and sustainable development. This study undertakes a comparative analysis of Canada, a nation rich in energy resources, and Bangladesh, an energy-scarce country, to understand their respective dynamics of energy transition. We examine data on energy production, energy consumption, policy frameworks, resource capacity, and economic impacts, highlighting the energy transition challenges faced by each country using an extensive survey of available literature and both univariate and multivariate time series analysis. Canada, with a diverse energy portfolio of renewable and non-renewable energy resources and with congenial policy implementations, including employment subsidies, feed-in tariffs, and emission reduction targets, exhibits potential for a relatively more straightforward energy transition. It has been making progress in that direction and targets to achieve net-zero emissions by 2050. However, despite progress, Canada faces challenges, including infrastructure limitations, regional disparities, and resistance from established energy sectors, which cause long delays in implementing projects. Bangladesh, with a limited amount of natural gas, relies entirely on imports to meet its energy demand. Its energy resources, both renewable and non-renewable, are minimal. Despite such limitations, it also targets to increase its renewable energy share to 40% by 2041 through targeted promotion of solar energy. However, such a target is more of an illusion than a reality as it has numerous limitations. The unavailability of sufficient natural resources, inadequate infrastructure, and financial and institutional constraints prevent the country from reaping the benefits of energy transition. Despite a preference for clean energy, coal consumption is still increasing. Nonetheless, public opinions in both countries lean towards clean energy and a better environment, but concerns about affordability and reliability persist, particularly in Bangladesh.
We study one of the world’s largest cattle markets by revisiting and extending previous work on the forecasting of Brazil’s Boi Gordo Index (BGI). Using an updated daily dataset (July 2006–September 2025, inflation-adjusted), we evaluate classical and machine learning (ML) approaches for price prediction. Methods include Exponential Smoothing (Simple, Holt, and Holt–Winters), ARMA/ARIMA/SARIMA, GARMA variants, GARCH, Theta, Prophet, and XGBoost; models are compared under a strictly chronological 90/10 holdout (~476 test days) using RMSE, MAE, and MSE, with the AIC guiding within-family selection. Results show that, for the full out-of-sample window, GARMA delivers the best overall accuracy, with ARMA and Holt–Winters close behind, while Prophet and XGBoost perform comparatively worse in this volatile setting. Performance is horizon-dependent: in the first 180 test days, prior to the late-2024 level shift, Holt attains the lowest RMSE/MSE, and XGBoost achieves the lowest MAE. No method anticipates the October–November 2024 exogenous jump and subsequent correction, highlighting the difficulty of structural breaks and the need for timely re-specification. We conclude that GARMA is a robust default for long, turbulent windows, whereas smoothing and ML methods can be competitive on shorter horizons. These findings inform risk measurement and risk mitigation strategies in Brazil’s cattle futures market.
The Trump administration signalled a shift toward protectionism in U.S. trade policy, imposing tariffs on imports from both strategic partners and competitors, which generated renewed uncertainty in international trade relations and the future of existing frameworks such as the African Growth and Opportunity Act (AGOA) and the Generalised System of Preferences (GSP). Earlier analysis has shown that a Free Trade Agreement (FTA) between the Southern African Customs Union (SACU) and the United States can be trade-creating and lead to improved macroeconomic outcomes in SACU countries. However, these positive effects decline over time, with varying impacts across different industries, influenced by initial tariff levels and export orientation relative to the US. This paper examines whether there are economic and strategic incentives for SACU to negotiate a more beneficial agreement than a simple across-the-board elimination of ad valorem import tariffs. Using a dynamic computable general equilibrium (CGE) model, the paper examines the outcomes if cereals, poultry, dairy products, red meat, and sugar products—often classified as sensitive due to their labour intensity, food security implications, and exposure to import competition—were to retain some level of protection under a SACU–US Free Trade Agreement. The results suggest that while the FTA boosts key macroeconomic indicators in the short run, gains taper off over time. Crucially, real wages and employment remain stagnant, and terms of trade deteriorate, raising questions about the inclusivity and sustainability of such a deal. Shielding vulnerable sectors initially enhances SACU’s exports and supports some industry growth, particularly in agriculture. However, without broader reforms and export diversification, long-term competitiveness remains weak. A nuanced FTA design, combined with structural support policies, is essential to unlock lasting and inclusive trade benefits.
Price volatility in the South African fresh produce market poses significant risks to the entire value chain. This study examines the extent of price volatility and spillover effects in these markets to improve price risk management and enhance market stability. Using weekly price data for eight major vegetables (cabbages, carrots, garlic, onions, potatoes, sweet potatoes, spinach, and tomatoes) collected from 19 regional fresh produce markets, volatility patterns were initially assessed with descriptive statistics. Time-varying volatility persistence was modelled using ARCH and GARCH frameworks. The DCC-GARCH framework was used to evaluate spillover effects between markets, and cointegration analysis is employed to determine both short- and long-run interdependencies. The results confirm the existence of spillover effects and patterns of price volatility in the fresh produce markets. We found volatility spillovers between key regional markets. For example, Johannesburg and Tshwane fresh produce markets (large central markets) transmit to several smaller markets, as indicated by significant DCC-GARCH spillover coefficients. Cointegration results show the partial integration of fresh produce markets, suggesting that price movements and volatility are interconnected across regions. This empirical result underscores the importance of understanding price risk management strategies in fresh produce markets and helps value chain decision makers better understand, anticipate, or test the possible effects of price volatility in fresh produce markets at any given time. Policy makers and other stakeholders in the value chain are equipped with knowledge of how best to serve society.
The urgent need to mitigate climate change has elevated green hydrogen as a sustainable alternative to fossil fuels, while green cryptocurrencies have emerged to address the environmental concerns of traditional cryptocurrency mining. This study investigates the dynamic correlation between the green hydrogen market and selected green cryptocurrencies (Cardano, Stellar, Hedera, Algorand, and Chia) from July 2021 to April 2024, utilizing the Dynamic Conditional Correlation GARCH (DCC-GARCH) model with robustness checks using EGARCH and GJR-GARCH specifications. Our findings reveal significant correlations, with peaks reaching up to 50% in 2022, a period likely influenced by the Russia-Ukraine conflict. Subsequently, a decline in these correlations was observed in 2023. These results underscore the interconnectedness of sustainability-driven markets, suggesting potential contagion effects during periods of global instability. The high persistence of correlation shocks (α + β values approaching unity) indicates that correlation regimes tend to be long- lasting, with important implications for portfolio diversification and risk management strategies. Robustness checks using EGARCH and GJR-GARCH specifications confirmed qualitatively similar patterns, reinforcing the validity of our findings into the evolving landscape of green finance and energy.
Industrial bio-inputs can improve commodity farming by replacing the use of agrochemicals. To assess the potential of agricultural bio-inputs to contribute to Brazil’s agro-industrial growth, we analyzed the market share held by domestic companies and the local market created by farmers who adopt bio-inputs. The results revealed that Brazilian companies accounted for 82.8% of the 221 companies with agricultural bio-inputs registered in Brazil by 2024. These domestic companies used technologies available to local investors and developed in collaboration with public innovation centers. Adoption levels among interviewed farmers ranged from 41.7% for biosolubilizers to 88.9% for bionematicides, revealing a large domestic market potential for bio-inputs in Brazil. We conclude that industrial agricultural bio-inputs represent an area of opportunity for Brazilian neo-industrialization based on local competitive advantages, low entry barriers, and domestic and foreign investments that can benefit from the local market for bio-inputs.
Technological advances in laboratory-grown diamonds (LGDs) have eroded the scarcity premium of natural diamonds, raising the question of whether diamonds still function as a safe haven. At the same time, crystalline osmium has become investable for the first time, as crystallization technology enables safe storage, certification, and global trading. Using monthly data from 2017–2025, we form diversified portfolios with and without diamonds and with and without osmium, as well as two-asset combinations with the MSCI World. The results show that diamonds no longer provide reliable stability, while osmium consistently contributes to reducing volatility. For portfolio investors, the key lesson is that traditional safe-haven roles can change; diamonds no longer offer robust protection, whereas crystalline osmium acts as a stabilizing component. These findings illustrate the contrasting effects of technological change: substitution and loss of value for diamonds, usability and stabilization for osmium.
We analyze extreme gold price movements between 1975 and 2025 using Extreme Value Theory (EVT). Using both the Block-Maxima and Peaks-over-Threshold approaches on a daily return basis, we estimate Value-at-Risk (VaR) and Expected Shortfall (ES) for the entire distribution focusing on a long-term view. Our results demonstrate that models based on the standard normal distribution systematically underestimate extreme risks, whereas EVT provides more reliable measures. In particular, EVT captures not only rare losses, but also sudden positive rallies, highlighting gold’s dual function as a risk and opportunity asset. Asymmetries emerge in the analysis: at the 0.99 quantile, losses appear larger in absolute value than gains. At the 0.995 quantile, in some episodes, upside extremes dominate. Furthermore, we find that geopolitical and economic shocks, including the oil crises, the 2008 financial crisis, and the COVD-19 pandemic, leave distinct signatures in the extremes. By covering five decades, our study provides the most extensive EVT-based assessment of gold risks to date. Our findings contribute to debates on financial stability and provide practical guidance for investors seeking to manage tail risks while recognizing gold’s potential as both a safe haven and a speculative asset.
