Search Results (646)

As a strategic material second only to grain, cotton serves both as a vital agricultural commodity and a key industrial crop. With the increasing frequency of global shocks and the deepening financialization of commodity markets, price linkages among major international cotton futures markets have strengthened. Consequently, in addition to fundamental supply and demand factors, cross-border price transmission has become a significant determinant of cotton pricing. This study employs daily closing prices of China’s cotton futures, cotton yarn futures, and U.S. cotton futures from 1 September 2017 to 31 March 2025 to examine the spillover effects among these three futures markets using time series models. The results reveal that U.S. cotton futures have dominated the Chinese cotton-related futures markets even prior to the onset of trade tensions, with strong domestic market comovements. However, both the U.S.-China trade war and the COVID-19 pandemic significantly weakened price co-movements while intensifying volatility spillovers. Although these external shocks enhanced the relative independence of China’s cotton yarn futures and modestly increased China’s pricing influence, U.S. cotton futures have consistently maintained their central role in price discovery. Full article

Futures and forward contracts together offer farmers of all sizes important tools for shifting and managing production risk. This risk shifting is particularly apparent in the U.S. grain complex, where the United States also has a significant export position. Because of this international reach, we argue that the futures and forward markets play a critical role in reducing world food insecurity and thus contribute to satisfying Sustainable Development Goal #2: Zero Hunger. We further argue that the presence of investors willing to take the opposite side of the farmers’ natural short hedge helps futures markets perform their key functions of price discovery and risk management. In addition to these roles, futures markets also enable farmers to finance their crops more efficiently over the production cycle, supporting operational stability. Finally, we highlight that agricultural markets in the United States are supported by significant regulation at the county, state, and federal levels. These farming regulations, coupled with federal oversight of agricultural futures markets, provide sufficient confidence that the goal of Zero Hunger is being pursued in an appropriate and effective manner, reinforcing the case for agricultural futures as a meaningful component of a broader sustainable investment strategy. Full article

In this study, we investigate the return propagation mechanism, hedging effectiveness, and portfolio performance across several common agricultural commodities, crude oil, and S&P 500 index, ranging from July 2000 to June 2024 by using a time-varying parameter vector autoregression (TVP-VAR) connectedness approach and three common multiple assets portfolio optimization strategies. The empirical results show that, the total connectedness peaked during the 2008 global financial crisis, followed by the European debt crisis and the COVID-19 pandemic, while it remained relatively lower at the onset of the Russia-Ukraine conflict. In the transmission mechanism, commodities and S&P 500 index exhibit distinct and dynamic characteristics as transmitters or receivers. Portfolio analysis reveals that, with exception of the COVID-19 pandemic, all three dynamic portfolios outperform the S&P 500 benchmark across major global crises. Additionally, the minimum correlation and minimum connectedness strategies are superior than transitional minimum variance method in most scenarios. Our findings have implications for policymakers in preventing systemic risk, for investors in managing portfolio risk, and for farmers and agribusiness enterprises in enhancing economic benefits. Full article

This paper develops a novel entropy-based framework to quantify tail risk and detect speculative bubbles in financial markets. By integrating extreme value theory with information theory, I introduce the Tail-Weighted Entropy (TWE) measure, which captures how information scales with extremeness in asset price distributions. I derive explicit bounds for TWE under heavy-tailed models and establish its connection to tail index parameters, revealing a phase transition in entropy decay rates during bubble formation. Empirically, I demonstrate that TWE-based signals detect crises in equities, commodities, and cryptocurrencies days earlier than traditional variance-ratio tests, with Bitcoin’s 2021 collapse identified weeks prior to the peak. The results show that entropy decay—not volatility explosions—serves as the primary precursor to systemic risk, offering policymakers a robust tool for preemptive crisis management. Full article

The simulation of wind power, electricity load, and natural gas prices will allow commodity traders to see the future movement of prices in a more probabilistic manner. The ability to observe possible paths for wind power, electricity load, and natural gas prices enables traders to obtain valuable insights for placing their trades on electricity prices. Since the above processes involve a seasonality factor, the seasonality component was modeled using a truncated Fourier series, and the random component was modeled using stochastic differential equations (SDE). It is evident from the literature that all the above processes are mean-reverting processes; thus, three mean-reverting Ornstein–Uhlenbeck (OU) processes were considered the model for wind power, the electricity load, and natural gas prices. Industry experts believe there is a correlation between wind power, the electricity load, and natural gas prices. For example, when wind power is higher and the electricity load is lower, natural gas prices are relatively low. The novelty of this study is the incorporation of the correlation structure between processes into the mean-reverting OU process using a copula function. Thus, the study utilized a vine copula and integrated it into the simulation. The study was conducted for the Texas energy market and used daily time scales for the simulations, and it was able to conclude that the proposed novel mean-reverting OU process outperforms the classical mean-reverting process in the case of wind power and the electricity load. Full article

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most widely used predictive models, particularly LSTM, GRU, XGBoost, and hybrid deep learning architectures, as well as key evaluation metrics, such as RMSE and MAPE. The findings confirm that AI-based approaches, especially neural networks, outperform traditional statistical methods in capturing non-linear and high-dimensional dynamics. However, the analysis also reveals several critical research gaps. Most notably, current models are rarely embedded into real or simulated trading strategies, limiting their practical applicability. Furthermore, the sensitivity of widely used metrics like MAPE to volatility remains underexplored, particularly in highly unstable environments such as crypto markets. Temporal robustness is also a concern, as many studies fail to validate their models across different market regimes. While data covering one to ten years is most common, few studies assess performance stability over time. By highlighting these limitations, this review not only synthesizes the current state of the art but also outlines essential directions for future research. Specifically, it calls for greater emphasis on model interpretability, strategy-level evaluation, and volatility-aware validation frameworks, thereby contributing to the advancement of AI’s real-world utility in financial forecasting. Full article

Increasing the shelf-life of fruits and vegetables using edible natural substances after harvest is economically important and can be useful for human health. Postharvest techniques help maintain the quality of edible tissues resulting in extended marketing periods and reduced food waste. The edible coating on perishable commodities is a common technique used by the food industry during the postharvest supply chain. The objective of this research was to study the effect of edible oil to minimize the loss of postharvest physio-chemical and nutritional attributes of bananas. The study selected two banana cultivars (Musa, ‘Cavendish’ and ‘Milk’) to conduct this experiment, and two edible oils (olive oil (Olea europaea) and moringa oil (Moringa peregrina)) were applied as an edible coating under two different storage conditions (15 and 25 °C). The fruit’s physio-chemical properties including weight loss, firmness, color, total soluble solids (TSS), pH, titratable acidity (TA), TSS: TA ratio, and mineral content were assessed. The experiment lasted for 12 days. The physicochemical properties of the banana coated with olive and moringa oils were more controlled than the non-coated (control) banana under both storage temperatures (15 °C and 25 °C). Coated bananas with olive and moringa oils stored at 15 °C resulted in further inhibition in the ripening process. There was a decrease in weight loss, retained color, and firmness, and the changes in chemical parameters were slower in banana fruits during storage in the olive and moringa oil-coated bananas. Minerals were highly retained in coated Cavendish bananas. Overall, the coated samples visually maintained acceptable quality until the final day of storage. Our results indicated that olive and moringa oils in this study have the potential to extend the shelf-life and improve the physico-chemical quality of banana fruits. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency’s deep financial system integration with important inflation implications for monetary policy. Full article

The stock price of PT Indika Energy Tbk (INDY) reflects the dynamics of Indonesia’s energy sector, which is heavily influenced by global coal price fluctuations, national energy policies, and geopolitical conditions. This study aimed to develop an accurate forecasting model to predict the movement of INDY stock prices using a hybrid machine learning approach called CNN-BiGRU-AM. The objective was to generate future forecasts of INDY stock prices based on historical data from 28 August 2019 to 24 February 2025. The method applied a hybrid model combining a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an Attention Mechanism (AM) to address the nonlinear, volatile, and noisy characteristics of stock data. The results showed that the CNN-BiGRU-AM model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) below 3%, indicating its effectiveness in capturing long-term patterns. The CNN helped extract local features and reduce noise, the BiGRU captured bidirectional temporal dependencies, and the Attention Mechanism allocated weights to the most relevant historical information. The model remained robust even when stock prices were sensitive to external factors such as global commodity trends and geopolitical events. This study contributes to providing more accurate forecasting solutions for companies, investors, and stakeholders in making strategic decisions. It also enriches the academic literature on the application of deep learning techniques in financial data analysis and stock market forecasting within a complex and dynamic environment. Full article

Crop production in Kazakhstan is characterized by vast resources, including over 200 M hectares of farmland and more than 23 M hectares of arable land located mainly in the arid zone with a short growing season. In 2023, the five most important crops in the country were spring wheat (12.5 M ha), spring barley (2.42 M ha), sunflower (1.13 M ha), flax (0.73 M ha), and winter wheat (0.59 M ha). Diverse agroecological conditions and low input farming represent good opportunities for the more sustainable use of resources through organic production. However, the area falling under certified organic farming recently varied from 0.1 to 0.3 M ha with wheat, flax, soybean and soybean meal, peas and lentils serving as the main commodities exported to Europe. Several factors limit organic farming development in the country, including the certification system, marketing, and the availability of crops, cultivars, and technologies. The current review summarizes the main organic agronomic practices and bioresources applicable in Kazakhstan into four main themes: crops and cultivars’ diversification; tillage systems for organic crops; crop nutrition; and protection. The technologies developed for organic farming in similar ecologies globally are highly relevant to Kazakhstan and need to be tested and adopted by producers. The lack of targeted cultivars and technology development for organic production in Kazakhstan impedes its progress and requires a longer-term producer-focused framework to extend related research. Full article

In a dynamic global market, platinum-group metals (PGMs), particularly palladium, are in high demand across various industries due to their unique properties. Palladium plays a crucial role in environmentally friendly technologies, such as catalytic converters, which mitigate harmful automotive emissions. Additionally, it is essential for clean energy production, particularly in hydrogen generation, which makes palladium a critical resource for building a sustainable and secure supply chain. This study evaluates the prospects of the palladium market through strategic analysis, focusing on the Russian mining and metals company PJSC MMC Norilsk Nickel. The research employs strategic and industry analysis methods to examine palladium production, market dynamics, and technological advancements, as well as emerging applications in the context of a green economy. The article analyzes the economics of palladium production, including price volatility driven by stringent environmental regulations and the rising adoption of electric vehicles. The palladium market faces challenges such as a constrained resource base, supply disruptions due to sanctions, price instability, and growing demand from key sectors, particularly the automotive industry. Nevertheless, innovation-driven trends offer promising opportunities for market growth, aligning with sustainable development principles and the transition toward a green, low-carbon economy in both established and emerging markets. As a key scientific contribution, this study proposes a modified methodological approach to industry analysis, enabling the assessment of a mining and metals company’s competitive sustainability in the palladium market over the medium and long term. Furthermore, the research models the life cycle of palladium as a commodity, considering evolving market trends and the rapid development of new industries within the green economy. Full article

Navigating the complexity of petroleum futures markets—marked by extreme volatility, geopolitical uncertainty, and macroeconomic shocks—demands adaptive and risk-sensitive strategies. This paper explores an Adaptive Risk-sensitive Transformer-based Deep Reinforcement Learning (ART-DRL) framework to improve portfolio optimization in commodity futures trading. While deep reinforcement learning (DRL) has been applied in equities and forex, its use in commodities remains underexplored. We evaluate DRL models, including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Deep Deterministic Policy Gradient (DDPG), integrating dynamic reward functions and asset-specific optimization. Empirical results show improvements in risk-adjusted performance, with an annualized return of 1.353, a Sharpe Ratio of 4.340, and a Sortino Ratio of 57.766. Although the return is below DQN (1.476), the proposed model achieves better stability and risk control. Notably, the models demonstrate resilience by learning from historical periods of extreme volatility, including the COVID-19 pandemic (2020–2021) and geopolitical shocks such as the Russia–Ukraine conflict (2022), despite testing commencing in January 2023. This research offers a practical, data-driven framework for risk-sensitive decision-making in commodities, showing how machine learning can support portfolio management under volatile market conditions. Full article

This paper investigates the link between commodity price volatility and stock market indices in Nigeria, Ghana, and Côte d’Ivoire, focusing on commodities such as oil, cocoa, and gold over a daily period from 2 January 2020 to 31 December 2021. In order to conduct this study, the BEKK-GARCH process is applied to test the volatility transmission across commodity and stock markets, while focusing on the asymmetry in the conditional variances of these markets. The analysis reveals a 30% increase in volatility spillovers during the COVID-19 period, highlighting significant asymmetry in conditional variances between African stock markets and global commodity markets. Furthermore, the findings demonstrate that conditional variances in stock and commodity markets are asymmetrical. This study advances the literature on volatility transmission by providing novel evidence on asymmetric spillovers between African stock markets and global commodity prices, particularly during COVID-19. It offers insights into the unique role of emerging African markets in global financial interconnectedness. Full article

Agent-based models are a flexible and scalable modeling approach employed to study and describe the evolution of complex systems in different fields, such as social sciences, engineering, and economics. In the latter, they have been largely employed to model financial markets with a bottom-up approach, with the aim of understanding the price formation mechanism and to generate market scenarios. In the last few years, they have found application in the analysis of energy markets, which have experienced profound transformations driven by the introduction of energy policies to ease the penetration of renewable energy sources and the integration of electric vehicles and by the current unstable geopolitical situation. This review provides a comprehensive overview of the application of agent-based models in energy commodity markets by defining their characteristics and highlighting the different possible applications and the open-source tools available. In addition, it explores the possible integration of agent-based models with machine learning techniques, which makes them adaptable and flexible to the current market conditions, enabling the development of dynamic simulations without fixed rules and policies. The main findings reveal that while agent-based models significantly enhance the understanding of energy market mechanisms, enabling better profit optimization and technical constraint coherence for traders, scaling these models to highly complex systems with a large number of agents remains a key limitation. Full article