Oil Market Research Articles

Quality Assessment of Edible Plant-Based Fixed Oils Using Different Analytical Techniques and Machine Learning Approaches

Plant-based carrier oils are constantly gaining popularity for their beneficial health effects on human organisms, thus shifting consumers’ preferences to alternative options in the oil market. The purpose of the study was to evaluate the quality characteristics of twenty-six edible plant-based fixed oils via chromatographic, chromatic, and spectroscopic techniques, suggesting their potential use as complementary edible oil sources. Palmitic, stearic, oleic, linoleic, and a-linolenic acids were found in considerable proportions, whereas the majority of oils possessed unsaturated (UFA)/saturated (SFA) fatty acid ratio greater than 1.6, suggesting their function in lowering blood pressure and preventing cardiovascular disease. Linseed, chia seed, macadamia, and canola oils provide a balanced intake of n-6 and n-3 polyunsaturated fatty acids (PUFA) within the range of 1:1 to 5:1. Oxidative stability was inversely related to oils’ PUFA content, with linseed, chia seed, pine cone, and walnut oils being the least stable oils against oxidation. Chlorophyll content in all oils was below the limit (50 mg/kg), preventing oxidation in the presence of light, whereas the highest values of b-carotene were noticed in soybean, linseed, and canola oils (61.18, 60.42, and 60.12 ppm, respectively). The application of machine learning algorithms for analyzing ATR-FTIR band intensities and FA proportions via discriminant analysis succeeded in discriminating pulp from seed oils, with a classification accuracy of 96.0% and 88.0%, respectively.

Transformation of European Oil Market: Prospects and Trends

Transformation of European Oil Market: Prospects and Trends

How volatility in the oil market and uncertainty shocks affect Saudi economy: a frequency approach

In this study, spectral Granger causality analysis is employed to investigate the spectral dynamics of uncertainty transmission and its impact on economic growth and financial development in Saudi Arabia from 1993 to 2020. We examine the relationships between crude oil volatility, geopolitical risk, global economic uncertainty, financial development (measured by market capitalisation, MCGDP, Financial Institutions Access Index, FIAIX) and economic growth. The empirical results show that negative shocks in crude oil volatility have a significant impact on financial development as measured by the MCGDP, with capital flight and reduced domestic investment playing key roles, despite the effective contribution of increased government spending to mitigate these effects. In this study, spectral Granger causality analysis is employed to investigate the spectral dynamics of uncertainty transmission and its impact on economic growth and financial development in Saudi Arabia from 1993 to 2020. We examine the relationships between crude oil volatility, geopolitical risk, global economic uncertainty, financial development (measured by market capitalisation, MCGDP, Financial Institutions Access Index, FIAIX) and economic growth. The empirical results show that negative shocks in crude oil volatility have a significant impact on financial development as measured by MCGDP, with capital flight and lower domestic investment playing a key role, despite the effective contribution of increased government spending in mitigating these effects. However, looking at the FIAIX, crude oil volatility has a different impact, with positive shocks having no significant impact on financial development. In contrast, negative shocks show long-term causal effects, which emphasises the vulnerability of the banking sector to oil price fluctuations. Geopolitical risk has a significant long-term impact on the MCGDP due to uncertainty shocks caused by regional and global geopolitical measures. For geopolitical risk, the results are mixed with a significant causality of negative shocks on the FIAIX over certain frequencies, emphasising the sensitivity of the banking sector to geopolitical tensions. In contrast, the world economic uncertainty has only a limited direct impact on the FIAIX, indicating the resilience of the Saudi financial sector to fluctuations in global uncertainty. In terms of economic growth, positive shocks have a greater impact than negative shocks due to the volatility of crude oil. However, shocks resulting from geopolitical risks, whether positive or negative, have little effect on economic growth. The industrial production index, a measure of the resilience of the Saudi economy, indicates that it is susceptible to positive shocks from fluctuations in oil prices over a range of time periods. Our study’s findings can assist Saudi Arabia’s authorities in fortifying the country’s financial and economic resilience against the spread of uncertainty. For strong financial development and sustainable economic growth, these uncertainty elements must be well monitored.

Extreme comovements and downside/upside risk spillovers between oil prices and exchange rates

Abstract This paper examines the dependence structure and risk spillovers between oil prices and exchange rates in both oil-exporting and oil-importing countries. Using a flexible dependence switching copula model, we analyze both positive and negative dependence and transitions between the dependence regimes. Additionally, we investigate the directional risk spillovers between oil and currency markets in both their downsides and upsides. Based on empirical data from 1999 to 2024 for major oil-exporting and oil-importing countries, we find that oil price-currency dependence is predominantly positive for oil-exporting countries, with infrequent transitions, but mainly negative for oil-importing countries, with frequent transitions between the two dependence regimes. These transitions often occur around crisis or war times. Furthermore, we observe that during downturns in the oil market, tail dependence between oil prices and currencies becomes more pronounced than during upturns. Our results indicate the presence of risk spillovers between oil and currency markets, with the downside spillover effects outweighing the upside ones. Moreover, we find that risk spillover is stronger from oil markets to currency markets than the reverse direction. These insights substantially enrich the existing literature and would offer valuable implications for effective risk management strategies and policymaking.

Asymmetric relationship between crude oil price and remittance inflows in a small island economy: Evidence from non-linear ARDL approach

Sri Lanka's economy heavily depends on remittance inflows to finance its import of essential and intermediate goods. A decline in remittances could severely limit the country's import capacity, affecting overall economic growth and development. This study investigates the impact of crude oil prices on Sri Lankan remittance inflows from 1980 to 2020, while accounting for per capita GDP, financial development, and exchange rates as additional explanatory variables. Employing a nonlinear autoregressive distributed lag (NARDL) model, the analysis reveals significant asymmetric effects of oil price shocks on remittance inflows. In the short run, positive oil price shocks lead to increased remittance inflows, whereas negative shocks reduce them. However, in the long run, both positive and negative oil price shocks have an adverse impact on remittance inflows. Furthermore, changes in per capita GDP, financial development, and exchange rates also play a crucial role in influencing remittance inflows. The findings highlight Sri Lanka's vulnerability to global oil market fluctuations and suggest policy measures to mitigate these risks. Key recommendations include diversifying labor export markets, enhancing financial infrastructure, and reducing dependence on remittance inflows to sustain import capacity and promote long-term economic resilience.

Forecasting of oil and gas stock market in major US

Abstract. Oil and gas are two crucial resources in modern life and the development of technology. Investors are not only concerned about their own price, but their stock price is also an important part of formulating investment plans. This article will focus on the adjusted closing price since it consists of adjustments for splits, dividends, and capital gain distributions, which can demonstrate the situation of oil and gas in the stock market better. After the occurrence of COVID-19, the economic market of the whole world including the US. encountered great changes in many aspects. Thus, this article considered applying the model with the best performance from ARIMA, exponential smoothing, and Holt-Winters method to forecast the future adjusted closing prices of two large and representative companies in oil and gas from the US. According to the predicted results, the US oil and gas market is likely to experience a small but steady increase.

Time-Varying Income and Price Elasticities of Oil Demand in OECD Countries

This study examines the long-run income and price elasticities of oil demand in 21 OECD countries using quarterly data from 1980:Q1 to 2021:Q3. We find that oil demand is inelastic with respect to both income and prices at 0.117 and −0.179, respectively. The cointegration tests reveal instability in oil price elasticities over time. The time-varying panel data estimates support these findings, showing significant variations in elasticities influenced by oil market dynamics and global events. Income elasticities reached their highest levels during the COVID-19 pandemic, while price elasticities ranged from −0.396 to 0.275. Significantly, the sign of oil price elasticities shifted from negative to positive after 2015, contrary to the law of demand, probably because of declining oil prices during that period. The largest positive and statistically significant price elasticity occurred in early 2020, which can be attributed to the COVID-19-induced decline in oil prices. Overall, this analysis contributes to understanding oil demand dynamics and highlights the impact of economic and oil market factors on elasticities.

Oil price shocks and bond risk premia: Evidence from a Panel of 15 Countries

We study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as unspanned factors in an affine term structure model based on the representation of Joslin et al. (2014). This is done for a total of 15 countries. Unspanned factors are responsible for most of the variability in bond risk premia for short holding periods, while spanned factors dominate the variance decomposition for longer holding periods. In both cases, global oil supply and global economic activity are clearly the most important unspanned shocks. A historical decomposition around the outbreak of the COVID-19 crisis shows the clear influence of global economic activity shocks during the months of February and March 2020, increasing bond risk premia significantly.

Dynamic Interconnectedness and Portfolio Implications Among Cryptocurrency, Gold, Energy, and Stock Markets: A TVP-VAR Approach

This article assesses the temporal and dynamic interconnectedness of cryptocurrency, gold, energy, and stock markets, essential for portfolio diversification. Using a TVP-VAR model, we analyze the return and realized volatility from November 11, 2013, to August 22, 2022. The study focuses on Bitcoin, gold, and renewable energy dynamics. Findings show that volatility shocks are most significant in the crude oil market, while Bitcoin's relationship with other assets is weak during non-crisis periods. Gold and Bitcoin's connection is less pronounced during crises. These results provide insights for portfolio optimization in both crisis and non-crisis periods.

The global oil market with a price ceiling for one of the producers

Subject. The article considers the functioning of the oil market under a price ceiling set for one of the producers. Objectives. The aims are to develop tools to study the global oil market under sanctions restrictions, define the characteristics of sustainability of such a market when applying sanctions to one of the producers, analyze economic consequences of setting a price ceiling for the purchase of oil, explore the possibilities of achieving equilibrium states. Methods. The main research tool is mathematical modeling of the market. The research methodology rests on the study of changes in market characteristics caused by the introduction of non-market methods of its regulation. Results. I built a game-theoretic model suitable for studying the global oil market with sanctions restrictions, defined critical parameters of the market caused by the introduction of a price ceiling, determined the ways to stabilize the market with this ceiling. Furthermore, I examined the current state of the market and determined the consequences of changing the characteristics of sanctions restrictions. Conclusions. The model is suitable for studying the current state of the global oil market. The introduction of a price ceiling, in contrast to a complete embargo, allows the market to remain stable within certain limits. To stabilize the market, it is also possible to conclude private contracts at discounted prices. Current trends in the development of the global oil market require the inclusion of oil importing countries in the set of players in the developed model.

Assessing statistical neural networks potentiality to distinguish PDO Kalamata and Molaoi olive oil varieties

Several regional areas in Greece produce high quality olive oil by cultivating certain varieties. Olive oil varieties of Kalamata and Molaoi are of special interest, since they produce extra virgin olive oil. Concretely, Kalamata is a city located in southwestern Greece. Intuitively, Molaoi is a town located in southeastern Greece. Subsequently, both geographic locations are known for their famous olive oil quality. Continually, Protected Designation of Origin (PDO) Kalamata olive oil, established by Council regulation (EC) No 510/2006, is considered an exceptional extra virgin olive oil variety. Specifically, there is a need to distinguish PDO Kalamata olive oil from other olive oil varieties is Greece such as the Molaoi olive oil, since PDO Kalamata olive oil is the main variety exported in the global olive oil market. Distinguishing PDO Kalamata from Molaoi olive oil is possible by incorporating statistical neural networks. Concretely, applying neural network experimentation enables differentiation between variations of certain chemical characteristics observed in certain geographic locations of Greece. In this paper, we use statistical neural networks to distinguish the geographical origin of PDO Kalamata olive oil compared with Molaoi olive oil based on synchronous excitation−emission fluorescence spectroscopy of provided olive oils samples evaluated in the chemical laboratory. Evaluation based on certain experimentation phase and subsequent data visualization of the adopted statistical neural networks are promising for distinguishing the samples of PDO Kalamata olive oil with high values of prediction accuracy. Such ability enables olive oil industry to assess extra virgin olive oil profitable potentiality in global market.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

The European energy crisis and the US natural gas market dynamics: a structural VAR investigation

The Russian invasion of Ukraine triggered severe disruptions in the European energy market, causing also significant shifts in global natural gas flows. We investigate how this European shock has affected the dynamics and altered the estimates of the elasticities on the US natural gas market. We use the Bayesian Structural Vector Autoregression framework proposed by Baumeister and Hamilton (Am Econ Rev 109(5):1873–1910, 2019, BH) for the crude oil market and applied by Rubaszek et al. (Energy Econ 103:105526, 2021, RSU) to analyze the dynamics of the US natural gas market. By extending the RSU model for natural gas exports and imports and deriving model’s posterior using most recent data, we approximate the impact of the European energy crisis on the US natural gas market. We show that the estimates of the US natural gas market elasticities change due to our modification, while simply updating the same prior beliefs with most recent data impacts the posterior estimates to a very limited extent. We also find that a shock even as major as the European energy crisis has only marginally affected the US natural gas market, thus confirming the results from the literature that the EU and US natural gas markets evolve independently.

A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis

The crude oil market occupies a crucial place in the composition of the global economy, however, the high level of uncertainty in crude oil prices makes it a challenging task to construct reliable forecasting techniques to obtain reliable expected results. Therefore, to effectively cope with the high uncertainty and the technical limitations of existing forecasting studies, this study constructs a hybrid forecasting system from the dual perspectives of deterministic forecasting and uncertainty analysis. With the concept of “denoising-integration” as the core, the system integrates several denoising techniques, econometric methods, and artificial intelligence methods, and establishes a hybrid forecasting model with high accuracy and robustness with the assistance of multi-objective optimization theory. Furthermore, by introducing fuzzy theory onto the basis of deterministic prediction results, an interval forecasting model with a narrower interval width and better comprehensive performance is constructed under the premise of ensuring that the coverage of prediction intervals meets the requirements of the confidence level. In this study, the forecasting system is applied to four global core crude oil futures markets, and the empirical study finds that the performance is improved by more than 50 % compared with the benchmark model, which has an impressive performance advantage.

Research on the Correlation Between Carbon Emission Rights and Energy Market: A Case Study of EUA and Oil Market

This study focuses on the importance of the relationship between carbon and oil in the context of global climate change and energy transition. In particular, this paper explores the link between fluctuations in the price of Brent crude oil in the United Kingdom and changes in the European Union Emission Allowances (EUA), which has significant implications for insight into energy market dynamics and policy making. This study uses an autoregressive (VAR) model to analyze the dynamic relationship between the European Union Emission allowances (EUA) market and the oil market from 2018 to 2024. Through Augmented Dickey-Fuller (ADF) stability test, the VAR model is established, the optimal lag order is determined, and the impulse response and Variance decomposition are analyzed. The results show that the EUA market has a short-term positive impact on the response of the oil market. But this effect has diminished over time, indicating the market's ability to self-regulate and stabilize. These findings provide new perspectives for understanding the interaction between global energy markets and environmental policy tools. The findings highlight the importance of considering these market dynamics when developing market strategies and environmental policies.

ANALYZING THE IMPACT OF INDONESIAN SUSTAINABLE PALM OIL (ISPO) CERTIFICATION AND THE FACTORS THAT INFLUENCE CRUDE PALM OIL (CPO) EXPORTS VOLUME

This study examines the impact of Indonesian Sustainable Palm Oil (ISPO) certification on Indonesian Crude Palm Oil exports and other factors, such as commodity production and prices, exchange rates, and production levels. The data covers total CPO volume (HS codes 15111000 and 15119000) from January 2006 to December 2022, focusing on ISPO certification for large State-owned and Private Enterprises in palm oil cultivation and processing. The analysis employs the Difference-in-Differences (DID) method and multiple linear regression (MLR) to identify the linear relationship between the dependent and independent variables. The DID analysis revealed that Indonesian CPO production and global CPO prices significantly affected CPO export volumes, while soybean oil prices and effective exchange rates were not statistically significant in the regression model. The results provide valuable insights for policymakers and industry stakeholders in developing strategies that support sustainable growth, enhance competitiveness, and maintain Indonesia's leading position in the global palm oil market. Policymakers should focus on encouraging broader adoption of ISPO among palm oil producers through incentives, training, and technical support, especially for smaller producers.

Food-fuel nexus beyond mean-variance: New evidence from a quantile approach

This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.

Oil and Gas Industry of India: Russian Aspect

Rather high rates of GDP growth (compared to the world average and other Asian countries) and real incomes of the population, combined with ongoing policies of industrialization and urbaniza-tion, make India one of the most attractive markets for commodities. The country needs natural resources, mainly oil and gas. Among the key areas of development of the national economy are the oil and gas producing and processing industries. Cooperation in the energy sector between Russia and India is developing steadily and progressively, important joint projects are also being implemented. The Russian Federation exports all types of raw materials, with the exception of pipeline natural gas. In 2023, oil imports from Russia, according to the Ministry of Commerce and Industry of India, amounted to 82.21 million tons (about 1.64 million barrels per day), which is 2.61 times higher than in 2022. During the same period, 9.09 million tons of Russian oil prod-ucts were delivered (+2.39 times to the level of 2022), more than 70 % of which were fuel oil. India is also among the largest importers of Russian coal and СNG. In 2023, India imported about 23.21 million tons of Russian coal (+28.8 % to the level of 2022) and 0.41 million tons of CNG (equal to the level of 2022). As you can see, Russia is interested in increasing the export of commodities to India due to the fact that the latter is an emerging economy and acts as an ex-panding market for natural fuels for the needs of households and industry. In addition, owing to the sanction restrictions imposed on Russian companies and the need to diversify supplies, in terms of foreign economy Russia takes particular interest in cooperation with Indian partners. Let’s study India’s energy policy, especially its oil and gas market, below.

Changes in Fuel Prices and the Use of Public Transport: Insights from the European Union following the Invasion of Ukraine

Rising fuel prices are known to affect public transport (PT) demand. However, the impact of the oil market inflation and subsequent energy crisis following the 2022 Russian invasion of Ukraine on mobility behaviour in Europe has received little attention. This study examines shifts in fuel prices and PT mobility across 25 Europe-an Union countries between February and May 2022. Weekly variations in PT mobility were correlated with diesel and gasoline prices, and a multiple linear regression was conducted to assess the influence of contextual factors in explaining variations in PT usage. The results indicate a significant increase in PT mobility across all countries after February 2022, which was positively associated with rising fuel prices. Approximately 84% of the variance in mobility was explained, with more pronounced increases observed in countries characterised by lower telecommuting prevalence, higher housing costs relative to household income, greater price differentials between gasoline and diesel, lower adoption of alternative fuels, and higher motorisation rates. These findings suggest that the surge in fuel prices, driven by the 2022 energy crisis, may have stimulated an increase in transit ridership.

Lavender essential oil market integrity: A comprehensive study of commercial Lavandula angustifolia essential oils adulteration and assessment of industrial standards

Adulteration of essential oils (EOs) is a common and unethical practice that affects both consumers and honest suppliers and harms market integrity. Despite the awareness of this problem, scarce scientific studies are described in the scientific literature. Therefore, 55 samples of lavender EOs were purchased and extensively analyzed to the trace levels, including chromatographic profile, quantitative analysis, and chiral profile. Of the 51 samples labeled as Lavandula angustifolia, 51 % were genuine, 6 % were cheaper lavandin EO, 14 % were fortified with synthetic additives and over 29 % of the samples were adulterated with both lavandin EO and synthetics. Usually, adulterated EOs were cheaper than genuine products, still some expensive products were also falsified. The most common synthetic adulterants were synthetic linalool and linalyl acetate, 3,5,5-trimethylhexyl acetate, α-terpinyl acetate, and dipropylene glycol. Only four of the studied samples were diluted with a medium not fully visible in the recorded GC profile. Industry standards were assessed to eliminate adulterated EOs and ISO 3515 was found to be impractical and too strict, but Ph. Eur. demonstrated its efficiency in eliminating adulterated samples without rejecting many genuine ones.PCA enabled the differentiation of lavender EOs containing synthetics from those of purely natural origin, and in the latter group, to distinguish L. angustifolia from lavandin. Some samples had an unusual composition suggesting that they were artificial blends of synthetics and some terpene fractions of non-lavender origin.This is the first such extensive study of adulteration of lavender EOs, regarding both the number of commercial samples as well as implications on their biological activity. The adulteration via dilution influenced the antimicrobial activity of the essential oils. In conclusion, adulteration is a significant problem in the market, and a large share of products on the market is somehow counterfeited, not only the cheapest products.