Oil Market Research Articles

Uncertainties and oil price volatility: Can lasso help?

In this study, we examine the predictive ability of G7 economic policy uncertainties (EPU) on oil market volatility using simple autoregressive and LASSO models. The out-of-sample empirical results show that the EPU of France is helpful for predicting crude oil volatility (WTI and Brent). More importantly, the LASSO model including the G7 uncertainties can exhibit the best predictive performance compared to other competing models based on different tests and during COVID-19.

Gas Diversification as a Way of Strengthening the National Security of the Republic of Serbia

The increasing importance of energy supply, particularly gas, has transformed it from solely an energy and political concern to national security. This shift has been particularly evident following the Russian Federation's military aggression against Ukraine in recent years. This paper supports the hypothesis that gas diversification in the Republic of Serbia is crucial for enhancing the country's energy security index and strengthening its national security and state sovereignty. Serbia can safeguard its interests by reducing the political influence exerted by the Russian Federation, which is a direct result of its dominant position in the gas and oil market. To substantiate this hypothesis, the article will utilise well-established and widely used indices for calculating energy security, such as the Shannon-Wiener and Energy Security Index. Moreover, it will demonstrate the interdependence between non-compliance with sanctions imposed on the Russian Federation due to energy dependence and the hindrance of Serbia's European integration progress, explicitly concerning the non-opening negotiation chapters relating to foreign and security policy. The data used in the paper will be derived from the Law on the Availability of Data of Public Importance and official documents of the Republic of Serbia, ensuring a reliable and evidence-based analysis.

Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes

We examine extreme connectedness between the artificial intelligence (AI) tokens, artificial intelligence ETFs, and other asset classes, using the quantile VAR approach of Ando et al., (2022). We find moderate connectedness levels at mean and median quantiles, with AI ETFs (AI tokens) acting as strong (weak) net emitters (receivers) of return spillovers. Findings, confirmed by alternative testing, suggest that, during normal market conditions, AI tokens may offer utility as diversifiers for portfolios of traditional assets. However, at both lower and upper quantiles, connectedness levels increase, consistent with AI tokens and ETFs being sensitive to extreme shocks. Results suggest that AI tokens and ETFs do not diversify the risk of other assets during extreme market conditions. Finally, AI tokens, especially, may offer effective hedging at low cost for traditional assets (gold, equity, real estate, bonds, and currency), except for the oil and cryptocurrency market. Investors including AI assets in portfolios need to diligently monitor for changing market conditions.

Physicochemical Characteristics and Nutritional and Biological Properties of Fish Oils in Cameroon: An Overview

Due to their significant health benefits, fish oils have garnered increasing interest in recent decades. However, Cameroon’s fish oil market remains insignificant, and the few available products are imported, despite the country’s abundant marine resources. Additionally, research on Cameroonian fish oils is relatively recent and scarce. Therefore, this manuscript provides an overview of research on fish oils in Cameroon, focusing on their physicochemical characteristics, as well as their nutritional and biological properties. As of March 2023, 26 studies on fish oils in Cameroon have been published, with a focus on 23 species collected in the littoral, far-north, and west regions of Cameroon. Filets were the main parts used, and the Bligh Dyer and Soxhlet methods were the primary oil extraction techniques. Depending on the species, tissues, and extraction methods, oil contents ranged from 4.57% to 32.10% dry matter or yielded 0.36% to 66.83% wet weight. These oils generally meet recommended standards for markers of acidity and oxidation. Fatty acid profiles from 16 species showed a total of 48 fatty acids, including those that are beneficial to human health. Oils from eight species were found to significantly reduce weight, hyperlipidemia, hyperglycemia, hepatomegaly, and adipomegaly, while four species showed activity against bacteria responsible for food poisoning. Future work should include all fish species found in Cameroon, with a focus on by-products, and explore the physicochemical, functional, nutritional, and biological properties of these oils.

Does oil price volatility matter for the US transportation industry?

Although the US transport sector is one of the major users of fossil fuel (e.g., crude oil), the impact of energy price volatility on transport stock sector indexes remains under-researched. The present study addresses this research void by investigating the impact of energy implied volatility on transportation stock returns in the US. Using the crude oil volatility index (OVX), as a proxy of energy price volatility, and three Dow Jones indexes tracking the performance of the airlines, marine and trucking stock subsectors, we employ a GARCH-jump model. The main results show that the oil market sends volatility to the US transport subsector stock indexes, suggesting that oil implied volatility plays a role in pricing US transport stocks. The impact of OVX shocks is asymmetric, indicating that increases and decreases in oil implied volatility have a heterogeneous impact on the transport subsector stock markets. Jumps are significant in the three transport subsector stock indexes, and are time-dependent. Notably, the three transportation subsector stock indexes are more sensitive to OVX shocks than the S&P 500 index. These results have important implications for investors, policymakers, academics, and managers of the US transportation industry.

Unraveling Turkish agricultural market challenges: Consequences of COVID‐19, Russia–Ukraine conflict, and energy market dynamics

AbstractFor a significant global segment, the volatility in grain prices presents a substantial menace to food accessibility and security. In the global pandemic and the Russia–Ukraine conflict (RUW), numerous nations were caught off guard, exacerbating this predicament and leading to instances where citizens faced purchasing restrictions on sunflower oil. This study employs the VAR (1)‐Asymmetric BEKK‐Generalized Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) model to assess the extent of risks stemming from the pandemic and RUW in critical Turkish agricultural commodity markets: namely, wheat, barley, corn, and sunflower oil. The analysis reveals that the conditional variances of their return series are positively influenced by short‐ and long‐term uncertainties. With the escalations in the global oil market, the enduring hazards within these selected markets in Türkiye have intensified concurrently. The COVID‐19 pandemic has induced a decrease in long‐term uncertainty within wheat and barley markets, wherein noteworthy spillover risks in the barley, corn, and sunflower oil markets have exacerbated risks in the corn market. Empirical findings imply that COVID‐19 and RUW disrupt the agricultural supply chain, leading to impediments in food provisioning and security. The outcomes provide valuable insights to fortify policies, guarantee consistent access to plant‐based protein, and address nutritional insecurity within the nation. These policy measures align with the initiatives undertaken by the United Nations and Türkiye, which actively engage in establishing a grain corridor to facilitate Ukraine's grain exports, thereby ensuring food security and safeguarding agricultural lands. [EconLit Citations: A1, E3, G1, Q0, Q1, Q2, Q4].

The Impact of The Conflict Between Russia and Ukraine on China's Crude Oil Market

Following the commencement of hostilities in Ukraine in 2022, western nations decided to take the measure of imposing a number of sanctions on Russia. The sanctions placed on Russian companies that export crude oil have, among other things, had a significant impact on the crude oil market in China. China is significantly dependent on the exportation of crude oil by Russia because China is an oil importer. As a result, the investigation into how the conflict between Russia and Ukraine affected China's crude oil market has a significant bearing on the growth of China's manufacturing sector as well as the economy overall. By examining both policy and academic literature on the topic of export limitations of Russian crude oil in a variety of countries, this piece of writing performs an analysis of the influence that the start of war in Ukraine has had on the global crude oil market as well as the changes that are expected to take place in the Chinese crude oil market. In conclusion, it has been discovered that China's price of crude oil would rise in tandem with the price of crude oil on the world market in the near future. It is anticipated that China would, over the course of time, become less reliant on crude oil imported from Russia and other nations, gradually shifting its energy market in the direction of more environmentally friendly practices.

A simple model of global fuel consumption

We present an SVAR model of the global oil market that utilizes information on global oil consumption in the form of fuels. Under mild identifying assumptions, data on global fuel consumption provides comparatively sharp insights into structural parameters of the global oil market. The estimated short-run global fuel demand elasticity with respect to crude oil prices is around −2%, which is considerably more inelastic than estimates of local fuel demand elasticities based on disaggregated data. Our framework provides new evidence on the drivers of oil-market dynamics and on the effects of climate change policies that act through the price of oil.

Prospect forecast for drilling ultra-deep wells in difficult geological conditions of western Turkmenistan

The relevance of the study is related to the growing need for efficient production of hydrocarbons in the difficult geological conditions of western Turkmenistan to ensure energy security and economic development of the region in the changing global oil and gas market. The purpose of the study is to conduct a comprehensive analysis of the geological, technical, and economic aspects of drilling ultra-deep wells in difficult geological conditions of western Turkmenistan to assess their potential feasibility and contribution to the energy infrastructure of the region. The study used analytical, functional, and statistical methods, classification, and synthesis. The prospects for drilling wells in difficult and extreme geological conditions of western Turkmenistan were identified. The analysis of the current state of prospecting and exploration in this region was carried out, while the main attention was paid to the investigation of the main difficulties faced by drillers when working with deep horizons of the reedbeds. As a result of the conducted research, general principles and recommendations aimed at overcoming these problems were developed. Despite the successful introduction of modern technical and technological innovations that meet international standards when drilling deep and ultra-deep wells in the West Turkmen depression, the study emphasized that the existing drilling problems have not yet been eliminated. This study will provide valuable recommendations and information for making informed decisions when developing and implementing projects for drilling ultra-deep wells in difficult geological conditions, contributing to effective resource management and development of the oil and gas industry in western Turkmenistan.

Assessing the linkage of energy cryptocurrency with clean and dirty energy markets

The primary objective of this study is to explore the dynamic connectedness that exists among energy cryptocurrencies, clean energy metals, and conventional dirty energies. Moreover, we aim to investigate the time-varying effects of uncertainties on the connectedness that exists among these asset classes. We uncover heterogeneous reactions among energy cryptocurrencies and other assets. Common positive co-movement clustering is revealed mostly during the periods of the cryptocurrency crash and boom in 2018 and 2020, and the oil market crash, respectively. However, a constant weak and strong co-movement over shorter and longer horizons is observed throughout the sample period for all pairs analysed, with varying outcomes for longer horizons. Combining VIX and US EPU as measures of financial and economic uncertainty, we contend that uncertainty significantly affects energy cryptocurrencies, clean and dirty energies, and clean energy metals, with relatively weak impacts or influences observed for longer investment horizons. Our findings are highly beneficial to cryptocurrency investors, alternative asset investors, and policy makers, especially during periods of market turmoil.

The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models

This study investigates the effectiveness of sparse regression models with their diverse specifications and the impulse indicator saturation (IIS) method in forecasting crude oil prices and their returns in China. The empirical findings showed that the IIS approach and sparse regression models are more effective than the benchmark of the no-change model. The IIS approach consistently outperformed the competing models and produced reliable results in terms of both ROS2 statistics and success ratio, as it is invariant to window size and time horizon. The results suggest that sparse regression models exhibit a notable decline in accuracy compared to the IIS approach over the long horizon. However, the selection of window size in the rolling window method plays a crucial role. Meanwhile, the sparse regression models are quite sensitive to window size; with a long horizon forecast and a small window size, the accuracy of the results is far less than with a broader window size. In contrast, the IIS method provides quite accurate forecasts regardless of window size and time horizon. The elastic smoothly clipped absolute deviation (E-SCAD) and elastic minimax concave penalty (E-MCP) remained more successful across the sparse models, possibly as a result of the high correlation among the set of predictors.

Heterogeneity of agents, transaction costs and oil price dynamics

ABSTRACT In this study, we model the dynamics of oil prices based on a dynamic heterogeneous agent model. Two types of agents including fundamentalists and chartists trade in the market, and the transaction costs are taken into consideration. The empirical results show that it is useful to improve forecasting performance if the model allows the agents to learn from their past performance and considers the transaction costs. Examination of the agents’ dynamic weights shows that fundamentalists and chartists co-existed in the oil market with almost equal weights, and the fundamentalists dominated the market when the oil prices had large deviations from the fundamental level.

Price Competition and Shifting Demand: The Relation between Palm and Coconut Oil Exports

Despite having a strong production capacity for fresh coconut, Indonesia has a certain deficiency in coconut processing, particularly coconut oil products, which is reflected in the declining market demand rates. The skyrocketing price of palm kernel oil (PKO) had been observed to impact the shifting demand for coconut oil. The cross-price competition with PKO was estimated to uncover the potential market demand for Indonesian coconut oils, especially during the periods of price discrepancies between 2020 and 2022. Thus, our study aimed to analyze the Indonesian coconut oil and palm kernel oil (PKO) market relationship as the markets reacted during the period of price volatility. This study is essential for Indonesian market evaluation, as both commodities are considered to be perfect substitute goods and are similar substances that contain high levels of lauric acid called “lauric oils”. We deployed an ARDL analysis utilizing secondary data from 1964 to 2022, focusing on the cross-price elasticity between coconut oil and PKO prices with the addition of prominent concerned variables. In the long-term estimations, the observational results indicated that the coconut oil and PKO prices had distinctive impacts on Indonesian coconut oil exports of −1.85% and 1.88%, respectively. In the short-term estimations, we found inverse values: the coconut oil price had positive impacts in the short-term period of 1.29% (D1.) and 2.35% (LD.), while the PKO had a negative impact on Indonesian coconut oil exports of −2.17%. This indicated that a PKO price reduction would increase the demand for Indonesian coconut oil exports due to the PKO price volatility and uncertainty perceived by the buyers in the short term. Our study also observed that rival producers, such as the Philippines, had a negative impact (−1.80%), representing the intense competition between Indonesia and the Philippines. Therefore, the Indonesian government has to elevate its coconut oil competitiveness to acquire the potential to expand the market and compete with other major coconut-producing countries.

Spillovers and hedging effectiveness between oil and US equity sectors: Evidence from the COVID pre- and post-vaccination phases

We examine the return and volatility transmission between oil and US equity sectors during three periods: the pre-COVID-19 phase, COVID-19 pre-vaccination phase, and COVID-19 post-vaccination phase. We show that the return and volatility spillovers between oil and US equity sectors vary across these three phases. The highest return connectedness is observed during the post-vaccination phase. The volatility spillover is highest from the majority of sectors to oil market in the COVID-19 pre-vaccination phase. The volatility transmission from oil to US equities during COVID-19 is weak. Moreover, our optimal weights estimations suggest that investors should decrease their exposures in US equities for the portfolio of oil-equity sectors in the COVID-19 post-vaccination phase. Finally, hedging cost is lowest, and hedging effectiveness is highest during the post-vaccination phase. Our findings provide valuable insights to portfolio managers and investors regarding portfolio diversification, hedging, and forecasting during health crises and subsequent recovery phases.

Macroeconomic Impacts of Global Oil and Commodity Price Shocks on the Economy of Pakistan

The effects of oil and commodity price shocks on Pakistan's real exchange rate, inflation rate, government spending, money market rate, and industrial output are examined using monthly data from 2000-2017. Using the vector autoregressive (VAR) paradigm, we do an empirical investigation. Impulse response functions and generalized prediction variance decompositions are used to analyze the effect that fluctuations in oil and commodities prices have on Pakistan's economy. When the world oil price (LWOP) causes shock in the global oil market, the real exchange rate (LRER) shows a negative impulse response function. As a result of the rising cost of crude oil throughout the globe, inflation has been on the rise. Industrial output falls, the real exchange rate rises, and interest rates and inflation go up as a result of shocks. Despite increased industrial production, commodities prices have seen repercussions. Pakistan's interest rate follows shocks by rising for a prolonged period, much as the money market rate (MMR)or interest rate. After oil and commodity price shocks, governments tend to increase their spending. The real effective exchange rate is the primary source of economic volatility, as shown by generalized impulse response functions. Pakistan's economy has been hit hard by the shocks, especially the aftershocks in the currency rate. A depreciating currency rate is being seen in Pakistan. The real effective exchange rate is the main cause of fluctuations in the economy, as demonstrated by tools called generalized impulse response functions.

Resource Potential of the Oil and Gas Complex of the Republic of Sakha (Yakutia) and Its Development Prospects

Abstract. In recent years, the Russian oil and gas complex has been undergoing sanctions and restrictions related to the political situation in the country and in the world, which negatively affects the economic development of the country. The consequences of sanctions include: disruption of logistics of export supplies of oil and gas raw materials, replacement of Russian hydrocarbons with supplies from other countries, reduction of foreign investment and participation of foreign companies in Russian oil and gas projects, restriction of imports for the supply of equipment and technologies, etc. First of all, these problems cause the need to expand sales markets and build new production and logistics chains, replacing imported equipment and technologies with domestic ones. In order to solve the difficult economic relations that have arisen with the EU countries, it is proposed to redirect the market for oil and gas raw materials towards Asian countries. In this regard, the study of oil and gas resources of the Far East, including the Republic of Sakha (Yakutia) is a very relevant task. The purpose of this article is to identify the level of availability of mineral resources of the oil and gas complex. The object of the study is the oil and gas complex of the Republic of Sakha (Yakutia): industrial balance reserves and forecast resources of oil and gas fields in the region, and the main subsurface users of oil and gas fields. The subject of the study is the analysis and assessment of the state of the mineral resource base of the oil and gas resources of the region and the prospects for its development. Based on the analysis of the current state of the potential of the oil and gas resources of the Republic of Sakha (Yakutia), as well as a review of the global oil and gas market, promising directions for the development of the regional oil and gas complex are identified in the article.

Перспективы технологического развития нефтесервиса для освоения нефтегазовых месторождений арктического Севера

The article discusses the key challenges associated with the economic development of the oilfield service industry within the climatic, geological, and infrastructural conditions of the Arctic. The evaluation of the Arctic's contribution to the total hydrocarbon production in Russia is an important research problem, which is particularly relevant amidst the current international political tensions. The study aims to unveil aspects of research and development (R&D) prospects of oilfield services in Russia's Arctic regions and adjacent territories. The research identifies characteristic features of economic development associated with economic activity risks. These include infrastructure and technological constraints linked to the distance from federal R&D and industrial centers; insufficient financial resources and experience for large project implementation by local authorities; the intricacy and labor intensity of work, coupled with a shortage of specialists possessing the necessary skills. The study establishes that the complexity of developing Arctic oil and gas fields is mitigated by the application of innovations and highlights the vulnerability of domestic companies to import supplies, which are currently inaccessible due to Western sanctions. The results presented offer a comprehensive view of the promising facets of the innovation-driven and technological development of the oilfield services industry in the Arctic. They propose specific measures geared towards enhancing production efficiency in the oil and gas and oilfield services sectors for implementation by the government. The study's conclusions constitute its scientific novelty and affirm that the sector under consideration is characterized by the following aspects: 1) the demanding nature of work necessitating substantial financial assets and professional competencies of often scarce labor market participants, alongside the utilization of innovative technologies to curtail production costs; and 2) a constrained pool of oil and gas market participants diminishing competition in the oilfield services market and enabling vertically integrated oil companies to dictate prices to service organizations, thus limiting opportunities for the industry's innovation-driven and technological development. The study's conclusions hold significant practical potential and are recommended for incorporation into territorial, regional, and federal programs for the social and economic development of Arctic regions. These findings contribute to the innovation-driven and technological revitalization of the oilfield services industry, an integral element in developing the resource base of Northern territories. Additionally, they provide avenues for further scientific exploration, delving into logistic, financial, technological, corporate, and other economic aspects specific to participants in forthcoming development processes.

Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models

The increasing Russia-Ukraine crisis is without a doubt Europe's most prominent conflict since World War II, changing the dynamics of the oil and other key markets. Because the oil market has traditionally interacted with other financial and commodity markets, it will be intriguing to examine how it interacts with substantial financial assets amid market volatility induced by a conflict. The goal of this study is to propose a fuzzy time series (FTS) model and to compare its competitiveness to existing fuzzy time series (FTS) models, Autoregressive Integrated Moving Average (ARIMA) model and some machine learning methods i.e. Artificial Neural Networks (ANN), Support Vector Machine (SVM) and XGBoost models. We considered changes in the partitioning universe of discourse, optimization of parameters method(s), and interval estimation to make the forecast accuracy more precise forecasting than traditional methods via MAPE. The event-based data results show the proposed fuzzy time series model is outperforming all the competitive methods in the study. Furthermore, the proposed model forecasting shows a future decline tendency in WTi market crude oil prices (US$/BBL) after being at the record highest level, which is good news for the worldwide economy.

Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models

Investor emotions can significantly impact the perceived value of financial assets, and sentiment analysis technology can accurately model daily responses and views of investors. In this study, we analyze investor remarks from one of the most popular web economic areas to investigate the effects of investor emotions on the price of crude oil in China. We construct investor emotions indices for China's crude oil futures based on specific economic factors and employ five machine learning technologies, including the AR approach, SVR approach, MLP method, neural networks with a recurrent units model, a recurrent unit with a gate approach, and LSTM model, to predict one-day-ahead prices. Our research offers insights into the effectiveness of different machine learning technologies and how specific economic factors contribute to sentiment analysis accuracy. We discuss the natural language processing techniques and tools used to extract sentiment from financial forum comments, along with the measures taken to ensure the accuracy and reliability of sentiment scoring. Our study highlights the advantages of the LSTM model, paired with the compound sentiment index, in the context of crude oil price forecasting and specifies the accuracy levels achieved by this model in comparison to others. We also discuss potential limitations and challenges in the long-term application of sentiment analysis for price forecasting and propose possible solutions. Our findings have practical implications for investors, financial analysts, and policymakers involved in China's crude oil market. We explore patterns or trends in investor emotions in our analysis and discuss their correlation with major economic or geopolitical events in China and globally. Our study contributes to a deeper understanding of investor emotions' impact on crude oil prices in China and provides valuable insights for various stakeholders in the dynamic and speculative energy oil industry.

Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

China is a large oil-consuming country, and sharp fluctuations in oil prices are bound to be detrimental to the stable growth of its economy. Therefore, accurately grasping the impact of the oil market on China’s sectors is the key to ensuring its healthy economic development. The article aims to explore the extreme risk transfer link of the oil and China’s sectors system, focusing on uncovering the risk spillover mechanism of the oil and providing early warning on it. We apply the TENET method to discuss risk propagation relationships within the oil and sectors system at three levels. The TVP-VAR model is brought in to recognize the factors affecting risk spillover in the oil market from the network correlation perspective. Finally, early warning of oil risk spillover is provided by incorporating the influencing factors into a machine learning model. The outcomes indicate that the risk connectivity of the oil and China’s sectors system is highly correlated with extreme events. There are variations in the spillover effects of oil market risk on different sectors, with Telecommunication Services, Utilities, Financials and Major Consumer sectors being the main bearers of the oil risk shocks. Overall, oil risk spillovers are mainly driven by economic policy and geopolitics, but oil price uncertainty is found to have a persistent impact on oil market risk spillovers in the dynamic analysis. Random forest model can provide effective early warning of oil risk spillovers. In addition, the significance analysis shows that oil price uncertainty and inflation are important factors affecting oil risk spillovers and are nonlinearly correlated with them.