Search Results (361)

The phenomenon of rising forward price volatility, both historical and implied, as maturity approaches is referred to as the Samuelson effect or maturity effect. Disregarding this effect leads to significant mispricing of early-exercise options, extendible options, or other path-dependent options. The primary objective of the research is to identify a practical way to incorporate the Samuelson effect into the evaluation of commodity derivatives. We choose to model the instantaneous variance employing the exponential decay parameterizations of the Samuelson effect. We develop efficient calibration techniques utilizing historical futures data and conduct an analysis of statistical errors to provide a benchmark for model performance. The study employs 15 years of data for WTI, Brent, and NG, producing excellent results, with the fitting error consistently inside the statistical error, except for the 2020 crisis period. We assess the stability of the fitted parameters via cross-validation techniques and examine the model’s out-of-sample efficacy. The approach is generalized to encompass seasonal commodities, such as natural gas and electricity. We illustrate the application of the calibrated model of instantaneous variance for the evaluation of commodity derivatives, including swaptions, as well as in the evaluation of power purchase agreements (PPAs). We demonstrate a compelling application of the Samuelson effect to a widely utilized auto-callable equity derivative known as the snowball. 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

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

The ecological region and training system are critical in determining an orchard’s microclimate and, ultimately, the quality and yield of the fruit produced. However, few studies have addressed the effects of their interactions on the commodity properties preferred by consumers, including appearance, flavor, and nutritional components. This study was conducted in distinct ecological regions at the county scale, with two classic cultivation modes (a traditional freestanding system with natural grassing and fruit without bagging and a flat-type trellis system with floor covering and fruit bagging) used for investigation and testing in 2020 and 2024, respectively. Significant differences in internal and external quality attributes were observed between the two groups. A sensory analysis showed that an increase in the soluble solid content and a better fruit appearance were strongly associated with higher purchase intentions. By integrating meteorological parameters, it was also found that temperature and air humidity during the month before harvest were associated with the pear phytochemical and metabolomic profiles. Planting site had a particularly notable effect on quality attributes and sensory experience, with low-latitude-harvested samples under cultivation mode 1 clustering together and showing higher overall scores, while cultivation mode 2 may be more suitable for high-latitude areas. Our results pave the way for making precise recommendations for the selection of suitable planting sites and optimum cultivation modes in Jiangxi Province to achieve high-quality ‘Cuiguan’ pears and fully exploit their planting potential. 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

Natural gas with a high carbon dioxide (CO₂) content presents significant operational and environmental challenges when used as a fuel. A high CO₂ content lowers the calorific value of natural gas, reducing its fuel efficiency and increasing the risk of corrosion in pipelines and processing equipment. Consequently, such natural gas must be purified to reduce the CO₂ content to acceptable levels before it can be effectively used as a fuel. Various technologies for natural gas purification are currently employed, primarily focusing on CO₂ removal. This research explores an innovative approach where highly contaminated natural gas is utilized to synthesize hydrogen for subsequent methanol production. Methanol synthesis necessitates both hydrogen and CO₂, integrating the use of by-products effectively in the production chain. Following the production of methanol, it is then converted into dimethyl ether (DME), a compound with considerable value as a clean fuel alternative due to its lower emissions when burnt. The open-source COCO simulator was used to model and simulate these processes, allowing for the creation of a detailed process flowsheet. The simulation covered four main stages: (1) purification of the natural gas to remove excess CO₂, (2) production of hydrogen, (3) synthesis of methanol using the hydrogen and captured CO₂, and (4) conversion of methanol to DME. This integrated approach mitigates the issues associated with high CO₂ content in natural gas and leverages this component as a valuable feedstock, demonstrating a comprehensive use of all extracted compounds. The proposed process illustrates a promising route for utilizing highly contaminated natural gas, potentially transforming an environmental liability into valuable chemical commodities. Full article

The relationships between mycotoxins content in food commodities or feedstuffs and the foodborne diseases is well known. So far, the available data mainly include chemical methods of mycotoxins decontamination for agricultural commodities or raw materials, including mycotoxin binders. Therefore, the possible use of some natural and cost-effective supplements such as herbs, fungi, microorganisms, or plants with powerful and safe protection against mycotoxin-induced health ailments is the main subject of this review paper. Various antagonistic microorganisms or yeast with fungicidal properties, as well as some herbs or plants that suppress fungal development and the subsequent production of target mycotoxins and/or have protective effect against mycotoxins, are deeply studied in the literature, and practical suggestions are given in this regard. The protection by degradation, biotransformation, or binding of mycotoxins by using natural additives such as herbs or plants to feedstuffs or foods has also been thoroughly investigated and analyzed as a possible approach for ameliorating the target adverse effects of mycotoxins. Possible beneficial dietary changes have also been studied to potentially alleviate mycotoxin toxicity. Practical advice are provided for possible application of the same natural supplements in real-life practice for combating mycotoxin-induced health ailments. Natural feed supplements and bioactive compounds appeared to be safe emerging approaches to preventing health ailments caused by mycotoxins. However, the available data mainly address some in vitro studies, and more in vivo experiments are necessary for introducing such approaches in the real-life practice or industry. Generally, target herbal supplements, antioxidants, or polyenzyme complements could be used as powerful protectors in addition to natural mycotoxin binders. Bioactive agents and enzymatic degradation are reported to be very successful in regard to PAT and OTA, whereas antagonistic microorganisms/fungi/yeasts have a successful application against AFs and PAT-producing fungi. Full article

The accelerating energy transition, coupled with increasing market volatility and computational advances, has created an urgent need for sophisticated decision-making tools that can address the unique challenges of energy finance—a gap that reinforcement learning methodologies are uniquely positioned to fill. This paper provides a comprehensive review of the application of reinforcement learning (RL) in energy finance, with a particular focus on option value and risk management. Energy markets present unique challenges due to their complex price dynamics, seasonality patterns, regulatory constraints, and the physical nature of energy commodities. Traditional financial modeling approaches often struggle to capture these intricacies adequately. Reinforcement learning, with its ability to learn optimal decision policies through interaction with complex environments, has emerged as a promising alternative methodology. This review examines the theoretical foundations of RL in financial applications, surveys recent literature on RL implementations in energy markets, and critically analyzes the strengths and limitations of these approaches. We explore applications ranging from electricity price forecasting and optimal trading strategies to option valuation, including real options and products common in energy markets. The paper concludes by identifying current challenges and promising directions for future research in this rapidly evolving field. Full article

This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes. Full article

Natural gas, as a vital component of the global energy structure, is widely utilized as an important strategic resource and essential commodity in various fields, including military applications, urban power generation and heating, and manufacturing. Therefore, accurately assessing energy consumption to ensure a reliable supply for both military and civilian use has become crucial. Traditional methods have attempted to leverage long-range features guided by prior knowledge (such as seasonal data, weather, and holiday data). However, they often fail to analyze the reasonable correlations among these features. This paper proposes a natural gas consumption forecasting model based on feature optimization and incremental LSTM. The proposed method enhances the robustness and generalization capability of the model at the data level by combining Gaussian Mixture Models to handle missing and anomalous data through modeling and sampling. Subsequently, a weakly supervised cascade network for feature selection is designed to enable the model to adaptively select features based on prior knowledge. Finally, an incremental learning-based regression difference loss is introduced to promote the model’s understanding of the coupled relationships within the data distribution. The proposed method demonstrates exceptional performance in daily urban gas load forecasting for Wuhan over the period from 2011 to 2024. Specifically, it achieves notably low average prediction errors of 0.0556 and 0.0392 on the top 10 heating and non-heating days, respectively. These results highlight the model’s strong generalization capability and its potential for reliable deployment across diverse gas consumption forecasting tasks within real-world deep learning applications. Full article

The relationships between the prices of major energy commodities have been a widely discussed topic in energy market analyses. This study examines whether the substantial changes observed in recent years have influenced the price linkages between coal and natural gas. By comparing selected price indices from European and Asian markets, we assess the evolving interdependencies between these fuels. The results indicate that the most significant changes in price linkages have occurred in European markets. Both VAR and ARDL model-based tests reveal a shift in the direction of causal relationships. Between 2006 and 2011, coal prices significantly influenced natural gas prices, with no strong evidence of reverse causality. However, in the more recent period (2018–2023), the relationship reversed—natural gas prices now have a significant impact on coal prices, while the reverse linkage has weakened. In Asian markets, the changes were less pronounced, particularly for Japanese import gas prices based on lagged average formulas. However, in the most recent period, a notable influence of Indonesian import gas prices on Australian coal prices emerged, mirroring trends observed in Europe. These findings highlight the increasing role of natural gas in shaping energy commodity prices, especially in Europe, where its growing importance in power generation has contributed to this shift. Additionally, the post-2018 period has been marked by significant supply disruptions, particularly in Europe, with geopolitical factors playing a crucial role in amplifying the importance of natural gas prices. Full article

Araecerus fasciculatus (De Geer, 1775) is an important stored-product pest worldwide. In this study, the development time, survival rate, oviposition, and digestive enzyme (α-amylase, cellulase, pepsin, and lipase) activities of A. fasciculatus fed on five commodities (coffee, jujube, maize, wheat, and kansui) were investigated. Our results showed that the developmental duration of A. fasciculatus from egg to adult was shortest on coffee beans (51.41 days) and longest on kansui (69.65 days). The survival rate of A. fasciculatus adults was lowest on kansui (42.22%) and highest on coffee beans (63.33%). Significant differences in fecundity were also observed, with the greatest number on coffee beans (80.78 eggs/female) and the lowest on kansui (50.43 eggs/female). Araecerus fasciculatus showed the greatest intrinsic rate of natural increase (r_m) on coffee beans (0.141), followed by jujube (0.129), maize (0.117), wheat (0.105), and kansui (0.097). The net productive rate (R₀) showed a similar trend to r_m, with values of 48.42, 42.53, 35.39, 27.53, and 21.47, respectively, on these stored products. Although no significant differences were observed in the lipase activities when A. fasciculatus was fed on different stored products, activities of α-amylase, pepsin, and cellulase were highest on coffee beans and lowest on kansui. The variation in the population development of A. fasciculatus associated with different foods may be related to its digestive enzyme performance. These results indicated that coffee beans were the most suitable host food, while kansui was the least suitable for the development of A. fasciculatus. Full article

Trichothecene type A mycotoxins, such as T-2, HT-2, and diacetoxyscirpenol (DAS), are known to induce cytotoxicity and apoptosis in different cell types. As all three Fusarium toxins may occur concomitantly in a given food or feed commodity, there is growing interest in the effect of such mycotoxin mixtures. This study aimed to identify the toxic interactions among T-2, HT-2, and DAS in a human Jurkat cell model. As a first step, an MTT assay was used to assess cytotoxicity after 24 h of cell exposure to individual mycotoxins and their mixtures. The results were used to calculate the combination index (CI), which indicates the nature of the mycotoxin interactions. In Jurkat T cells, the toxicity ranking for the individual mycotoxins was T-2 > HT-2 > DAS. The CI values of the dual and triple mycotoxin combinations calculated from the results of the MTT and reactive oxygen species assays showed synergistic effects at low concentrations and an apparent antagonism at very high concentrations for all combinations. The additional cytometric analyses confirmed the synergistic effects, as expected, following co-exposure to the three tested trichothecenes. As the lower toxin concentrations investigated reflect natural contamination levels in food and feeds, the synergistic effects identified should be considered in risk characterization for trichothecene exposure in humans and animals. Full article

This study assessed the cytotoxicity of the individual and combined exposure to the fungicide azoxystrobin (AZX) and the three common mycotoxins found in food: ochratoxin A (OTA), deoxynivalenol (DON), and T-2 toxin. Cytotoxic effects were evaluated using the resazurin and MTT assays in human hepatocarcinoma (HepG2) cells after 24 h of exposure, and the type of interaction between the compounds was determined using the isobologram method. Results showed that T-2 was the most cytotoxic compound, followed by DON, OTA, and AZX. The compound ratios in the mixture were calculated using three sublethal concentrations (IC_50/2, IC_50/4, and IC_50/8) to achieve equal toxicity for each compound. Interaction analysis revealed that the nature of the interaction varied across components and concentrations. The AZX and DON mixture produced an antagonistic effect at all the analyzed effect levels. AZX and OTA or T2 mixtures, and tertiary combinations displayed antagonism at low effect values but additivity at high effect levels. Importantly, the quaternary mixture demonstrated synergism at all the effect levels. These findings highlight that the co-occurrence of fungicides and mycotoxins in food commodities can lead to complex exposure scenarios that may result in combined toxic effects on the organism. Full article