This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.

This paper develops a set of innovative text-based indices capturing oil market sentiment and oil price uncertainty. The textual analysis includes over 6 million news items spanning from January 1982 to June 2021. The evidence shows that sentiment indicators readily react to economic and geopolitical events that impact oil prices, thereby enabling these indicators to predict oil prices accurately. In contrast, uncertainty measures have inherent weaknesses, thus yielding unreliable oil price forecasts. This research yields a novel and robust text indicator that provides valuable insights for predicting the intricate dynamics of crude oil prices, particularly excelling in short-term forecasts and during periods of economic recession.

Sectoral Islamic finance and uncertainties: The role of different market condition

This paper will assess the economic sustainability of economic transformation in Saudi Arabia with regard to the liquidity constraints, dependence on Saudi Aramco and geopolitical risk at Vision 2030 level. The work assumes a mixed-methodology approach to the research onion paradigm since the research relies on secondary data analyzed in 2016-24 to understand the tendencies in GDP growth, oil revenue reliance, credit growth and investment requirements. The findings indicate that despite the diversification programs, with non oil GDP increasing with an average of 4-6 years, the economy still remains dependent on oil revenue and is contributing around 65-70 percent of the government revenue to the economy. The paper concludes that the liquidity constraints are critical that are fuelled by the high level of investment requirements estimated to be USD 1 trillion or more, the rise in the interest rates and the inability to finance the investments at the domestic level. Saudi Aramco has stayed at the centre of the supply in the aspect of fiscal assistance, whereas the dependence is a risk that exposes the market to the oil market volatility. Other than geopolitical risks, including the interruption of main trade routes, particularly the Strait of Hormuz, great impact on the volumes of exports, increase the logistics price by 20-25 percent, and slows down the project implementation. All these influence financing and economic growth. It concludes in the paper that economic sector and source financing diversification is to be adopted to achieve sustainable GDP growth of 5 7 per cent. Suggestions on the policy will include empowering the capital markets, strengthening foreign direct investment, promoting green financing, and breaking the links with reliance on the oil revenues. The study fills the body of literature by using one analytical approach on economic transformation in resource-dependent economies by including the financial, structural, and geopolitical levels.

The global edible oil market is consistently at risk of economically motivated adulteration, underscoring the necessity of robust analytical methods essential for authentication. Among various phytochemicals, phytosterols have emerged as powerful diagnostic markers and compositional indicators for verifying the botanical origin, purity, and quality of edible oils. This review summarizes recent advancements in phytosterol analysis, highlighting its application in detecting adulteration in high-value oils such as olive oil, tea seed oil, and sesame oil. We discuss the approaches of multiple chromatographic and mass spectrometry techniques (GC-MS, LC-MS) with chemometric analysis of novel markers like fatty acyl sterol esters and sterol degradation products. Furthermore, we discuss significant challenges, including the need for comprehensive databases, the identification of complex sterol compositional profiles, and the limitations of current standardized methods. The advancement of phytosterol-based authentication increasingly depends on the development of rapid, high-throughput, and non-targeted sterol profiling approaches, supported by artificial intelligence and bioinformatics, to ensure vegetable oil authenticity and safeguard market integrity.

ABSTRACT As the most heavily traded commodity market globally, the efficiency of the crude oil market has far-reaching implications for crude oil export and the economic development of oil-producing countries. This paper employs Data Envelopment Analysis (DEA) to measure the market efficiency of the world’s five major crude oil-producing countries. The results reveal significant fluctuations in market efficiency across these countries. Saudi Arabia exhibits the highest market efficiency with a score of 0.818, while Russia and Brazil show relatively low efficiency levels, with the lowest recorded at 0.221.Following the efficiency evaluation, the paper adopts a mediation effect model to investigate the influence of market structure on market efficiency, incorporating crude oil prices as a mediating variable. The findings are as follows: (1) Crude oil market concentration exerts a significant positive effect on market efficiency, with higher market concentration correlating with greater efficiency. (2) The mediating effect analysis demonstrates that crude oil prices positively mediate the relationship between market concentration and market efficiency. Specifically, higher market concentration leads to increased crude oil prices, which in turn enhances market efficiency. This study provides theoretical and practical insights for improving crude oil market efficiency and supporting sustainable development of the global oil economy.

ABSTRACT Employing the dataset from 1 November 2017 to 2 July 2024, this study utilizes the frequency-domain TVP-VAR framework and the quantile coherency method to investigate the dynamic time-frequency connectedness of oil price shocks on energy cryptocurrency returns. The research reveals the cross-market connectivity among selected series, and the short-term connectivity far exceeds the long-term connectivity. Major emergencies significantly enhanced internal connectivity and altered the direction of net connectedness. Oil shocks have varying impacts on energy crypto returns, where the connectedness between demand/risk shocks and energy cryptocurrencies is stronger. The linkage becomes stronger under extreme market conditions, and the hedging potential provided to the crude oil market varies across different types of shocks. These findings offer a new perspective for understanding the dynamic interactions between the crude oil and the crypto markets, holding potential value for academia and market participants.

Abstract In the competitive CPG industry, especially within edible oils, understanding factors influencing retail product choices is crucial for brand growth. As consumer preferences evolve and retail environments grow complex, strategic retailer engagement and targeted marketing are vital. Idhayam, known for premium sesame oil, has built a strong reputation on trust and quality, but sustained leadership depends on retailer perceptions and stocking decisions. Retailers act as key intermediaries, influencing product visibility and sales based on factors like brand perception, product quality, profit margins, and social influences. While existing research offers insights, there’s a need for context-specific understanding within the edible oil segment, where traditional preferences, health concerns, and loyalty heavily impact decisions. This study explores these core factors affecting retailer choices regarding Idhayam sesame oil to help optimize marketing and retail strategies. Keywords: Oil market, Product quality, Brand awareness, Brand loyalty, Reference groups

Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems.

This paper investigates extreme risk spillovers in global energy markets using the enhanced high-dimensional time-varying parameter vector autoregressive spillover (HD-TVP-VAR-SP) model. We employ the Long Short Term Memory (LSTM) model to develop an energy risk warning system, identifying key factors in risk contagion. Our findings reveal robust connectivity in global energy market risks, characterized by high-dimensional complex networks with marked temporal variations. The Americas region emerges as the leading contributor to systemic risk shocks, primarily through positive spillovers in its energy markets. The LSTM model demonstrates superior extreme risk prediction compared to other machine learning models like Gradient Boosting Machines, Random Forest, and Decision Trees. The oil market is identified as a critical driver of risk contagion in the energy sector. These insights provide valuable guidance for effectively identifying and managing global energy market risks and enhancing risk warning systems.

Our research addresses the increasing concern of interdependency in the global financial markets, especially the stock market of G20 nations (Australia, Argentina, China, France, Japan, and the USA) and the WTI (West Texas Intermediate) oil market between January 2017 and September 2025. The dynamic connectedness approach and quantile VAR are a strong check as we use them to determine the spillover effect in various market conditions. We find that the spillover effect in bullish and bearish market conditions is strong with the Total Connectedness Index (TCI) of 71.98% and 70.25%, respectively. In the normal case, the highest connectedness effect is achieved at tau=0.5, and this result is consistent with the dynamic TCI of 39.12%. Based on the varying market conditions, USA, Australia, and France are strong net transmitters, but WTI is a net receiver in a bullish and bearish market situation, then Argentina, China and Japan, respectively depending on the return spillover effects. The USA, France, and Australia can strengthen their leading position as a net transmitter in the volatility spillover. Japan is a net transmitter in bearish market conditions only in the volatility series, and it is a net transmitter in bullish market conditions only in the return series. This became particularly clear during the global epidemic, the war between Russia and Ukraine, and the ongoing tariff war. In general, our study points to the increased role of the USA stock market in world markets concerning WTI. These findings can be used by investors and policymakers to maximize returns and ensure market stability.

ABSTRACT The establishment of the Roundtable on Sustainable Palm Oil in 2004 has brought the sustainability practice in palm oil industries into the spotlight, triggering the focus of various stakeholders on the possible opportunities implicated by it. This study addresses the sustainability by using bibliometric and content analyses to provide a comprehensive synthesis of palm oil‐related papers published between January 1970 and November 2024. We limit the search of the Web of Science Core Collection database to business economics studies. Drawing on 573 publication records sourced from the database, this study shows that most published palm oil research supports the achievement of Sustainable Development Goals for “ No Poverty ”, “ Zero Hunger ”, and “ Climate Action ”. Our aim is not only to pinpoint significant authors, notable journals, leading countries, and influential articles within the palm oil area, but also to intellectually identify research streams in the area over the past 54 years. Our analysis reveals three prominent research streams of palm oil sustainability: (1) connection between palm oil production and environmental development; (2) connection between palm oil price and economic fundamental; and (3) connection between palm oil trading and market transition. In addition, the study develops the integrative conceptual framework for future research in economics and finance, which devotes to ecological finance, asymmetric price transmission, and futures market dynamics. By consolidating five decades of palm oil review research into a single, this study bridges academic inquiry with the insights necessary to enhance the scholarly discourse on palm oil's role in delivering policy‐relevant solutions aligned with global sustainability goals.

The predictive content of U.S. Energy Information Administration oil market forecasts

Crude oil prices play a pivotal role in macro-financial conditions, shaping inflation dynamics, policy uncertainty, and risk management decisions for energy-intensive industries and financial institutions. Using a diagnostics-first framework, this study evaluates the practical forecasting value of ARIMA as an interpretable baseline and clarifies its limitations. Monthly WTI and Brent spot prices are modelled following the BoxJenkins procedure, and performance is assessed via rolling out-of-sample forecasts, supported by systematic checks of residual behaviour and parameter stability. The results indicate that ARIMA can capture short-horizon mean dynamics in relatively stable regimes; however, its reliability weakens as volatility clustering, heavy-tailed innovations, and regime shifts become more pronounced. Diagnostic evidence explains why a plain ARIMA specification often produces miscalibrated prediction intervals and adjusts slowly around turning points, implying elevated model risk during periods of market stress. Beyond headline accuracy, the proposed approach improves transparency and provides a principled basis for model selection and risk assessment under uncertainty. Overall, prioritizing diagnostics yields a clearer and more actionable framework for robust forecasting, supporting more resilient decision-making amid oil market uncertainty.

Dynamic Interactions in Futures Markets: Exploring Transitory and Persistent Intraday Volatility Linkages among Oil, Gold, Stocks, and Forex Markets

We present a new approach to commodity pricing that enhances accuracy by integrating four distinct risk factors: the spot price, stochastic volatility, convenience yield, and stochastic interest rates. We build on Yan [Valuation of commodity derivatives in a new multi-factor model. Rev. Deriv. Res., 2002, 5, 251–271], the only model to our knowledge that incorporates all four sources of risk, and extend it by adding a more flexible correlation structure that captures state-dependent co-movements and time-varying risk premia. A further contribution is the explicit inclusion of the stochastic interest-rate factor within a unified Kalman-filter framework, which allows us to jointly filter the state variables and estimate model parameters using both commodity and bond market data. An empirical analysis of crude-oil futures shows that our four-factor model captures the complex dynamics of the futures term structure and consistently outperforms existing benchmarks.

This paper tests the adaptive market hypothesis for the Saudi Arabian stock market. By applying the automatic portmanteau and variance ratio tests, the degree of time-varying return predictability of market and sectoral indices are evaluated, in comparison to that of the oil market. Using daily data from 2010, we find that the Saudi Arabian stock market has been mostly efficient over time, showing a low degree of return predictability. Furthermore, we find that this degree of efficiency is closely related to that of the oil market. However, in contrast to the stock market, the oil market is found to be relatively inefficient with a higher degree of return predictability. Finally, we find that the degree of stock return predictability is partly driven by macroeconomic variables such as inflation and interest rates.

The aromatic genus Mentha (Lamiaceae), which includes about 25-30 species and many hybrids, is widely distributed across temperate and subtropical regions globally. Owing to their diverse bioactive constituents, Mentha species hold significant commercial value. The global peppermint oil market has exceeded USD 220 million with menthol, its core active component, accounting for approximately 30% of the global flavor and fragrance market. Research indicates that menthol and flavonoids collectively confer multiple pharmacological activities, including antioxidant, antimicrobial, anti-inflammatory, and neuroprotective effects. These properties underpin the industrial applications of Mentha in fields such as food, pharmaceuticals, and modern agriculture. The increasing demand for menthol poses sustainability challenges for traditional production methods. Exploring the use of synthetic biology to produce menthol, in order to address resource and environmental constraints, meet the expanding market demand, and unlock its potential in novel pharmaceuticals and functional foods, represents a pivotal direction for overcoming resource limitations.

Abstract Detecting correlation from records is a basic task in data science. Currently, the researcher tries to estimate it from an objective perspective, i.e., it tries to keep out of the investigated system and adopt a set of measures representing precisely and completely all the agents, to answer the questions of ”how often”, ”how many” or ”what is the relationship between variables”. In reality, the researcher is usually an agent in the investigated system, it designs by itself the measures for the system’s state, to answer the questions of ”how” and ”why”, i.e., the researcher conducts the investigation from a subjective perspective. Stimulated by the qualitative research in social science, in this paper we proposed a scheme to investigate complex systems from a subjective perspective. Technically, one decomposes the researcher’s output time series into intrinsic mode functions to represent its specific features. Projecting all the agents’ time series onto the phase space spanned by this set of specific features, one obtains the coordinates of the agents, from which the similarity network is constructed. The properties of the network are then used to give a portrait of the system’s correlation from the researcher’s perspective. As typical examples we investigated three theoretical models including the Lorenz system, the R ö ssler system, and their coupled systems. Though the Takens’ theorem tells us the equivalence of the variables in reconstructing the dynamical behaviors, we find significant differences between them. We also investigated a financial system composed of 22 stock markets, one crude oil market and one gold market. The similarity networks from the perspectives of the markets distributed in the USA, Europe, and China turn out to be much homogenous and dominated by star patterns, while those from the perspectives of the markets distributed in Russia, Brazil, India and South Africa behave non-homogenous and are dominated by cluster patterns.

This study aims to examine the effect of liquidity and profitability on stock returns of oil and gas subsector companies listed on the Indonesia Stock Exchange during the 2022–2024 period. Stock return is a crucial indicator for investors in evaluating investment performance, particularly in the oil and gas subsector, which is characterized by high volatility due to fluctuations in global energy prices, macroeconomic conditions, and geopolitical dynamics. Liquidity in this study is proxied by the Current Ratio (CR) and Quick Ratio (QR), while profitability is measured using Return on Assets (ROA) and Return on Equity (ROE). This research adopts a quantitative approach using the Structural Equation Modeling–Partial Least Squares (SEM-PLS) method and is analyzed with SmartPLS software. The research sample consists of 10 oil and gas subsector companies selected through purposive sampling based on consistent listing status and the availability of complete financial reports throughout the observation period. The analysis results indicate that liquidity has a negative and significant effect on stock returns, suggesting that excessively high liquidity may reflect inefficient utilization of current assets in generating shareholder value. Meanwhile, profitability shows a positive but insignificant effect on stock returns, indicating that higher profitability does not necessarily lead to higher stock returns. Simultaneously, liquidity and profitability explain 45.1% of the variation in stock returns, while the remaining proportion is influenced by other factors outside the research model. These findings imply that stock returns in the oil and gas subsector are not solely determined by internal financial performance but are also strongly affected by external factors such as global oil price movements, macroeconomic conditions, and market sentiment. This study is expected to contribute empirical evidence to financial literature and provide practical insights for investors in making informed investment decisions.