Oil Market Research Articles

Market competitiveness of sunflower seed and oil products

Abstract Sunflower seed is one of the most widely traded products in the world, with total trade amounting to 20.36 million metric tons of total vegetable oil production in 2023. Sunflower seed is usually used as a material for cooking oil and food products, biofuel, cosmetics, and other derivates. Due to climate change and the fossil fuel energy crisis in Europe, the need for vegetable oil has increased; one is sunflower oil. Therefore, this research aims to identify the global sunflower market from its largest producing countries. This study uses primer data from interviews in some of the largest producer countries: the Netherlands, Romania, and Poland. Secondary data was also obtained from export data for sunflower-exporting countries worldwide from 2020 to 2021. The Boston Consulting Group (BCG) matrix is used for the data analysis to assess the performance of sunflower products in various major exporting countries worldwide. The results of this study show that Romania took the star position with the highest sunflower seed export, around 19.8% of the total economic value of 4.74 billion USD. Ukraine took the cash cow position in sunflower oil, and Turkiye took the cash cow position in the refined sunflower oil market. These positions indicate the dominance of the global market.

On the partial impact of uncertainties on the nexus between macroeconomic fundamentals in West Africa

We investigate the degree of interconnectedness between stock returns and exchange rate returns, and the influence of some selected global uncertainty indices on such a relationship within a time-frequency domain in West Africa through the bi and partial wavelet approaches. The analysis was based on monthly observations from February 2013 to June 2023. The results highlight a negative correlation between stock return and exchange rates. The partial wavelet analysis evidence a significant effect of the global economic policy uncertainty, the implied oil market volatility, and the United States volatility index in driving the co-movements observed in the currency and stock markets. We also find a significant impact of the stock market on the currency market, underscoring the need for robust stock market policies. It is recommended that policymakers prioritize strategies aimed at boosting stock market stability and depth which can positively affect the currency markets. The significant influence of global uncertainties or shocks should not be disregarded in the formulation of policies regarding exchange rates and stock return integration at various investment horizons.

A Financial Analysis and Valuation of Chevron Corporation

With the continuous development of the global economy, the demand for energy continues to grow, and the market size of oil and gas as an important source of energy continues to expand, and the growth potential of the oil and gas market in the future is still huge. As an internationally renowned energy company, Chevron occupies an important position in the global oil and gas market. Firstly, the financial indicator analysis method is used to compare the financial data in 2023 with the other three energy companies to obtain its financial strength with strong asset liquidity, high solvency and strong profitability. Secondly, from the aspects of profitability, development ability and valuation, it is concluded that Chevron has strong market competitiveness, large room for stock price growth, and greater investment opportunities. Finally, it is concluded that Chevron has a high investment value, but it still faces certain risks and challenges, and it is necessary to pay attention to the impact of international oil price fluctuations on the company's performance and should pay close attention to its risk factors and take corresponding measures to face it positively.

Impact of carbon market prices on oil market fear across quantiles

ABSTRACT This paper uses quantile regression to investigate how carbon market prices affect oil market fear. Our results show a significant and strong negative effect of changes in carbon market prices on changes in oil market fear at high quantiles. Notably, this negative effect is driven by the decrease rather than the increase in carbon market prices. We also observe that the COVID-19 pandemic enhances the negative impact of the decreased carbon market prices at high quantiles.

Between Exchange Rate Returns and Crude Oil Returns in Oil-Importing Countries

Fluctuations in the financial markets stem from the reactions of investors to both market activities and more wide-ranging macroeconomic indices. This research examines the interconnectivity between returns on exchange rates and crude oil prices for ten oil-importing countries. Quarterly data spanning the period from 2000Q1 to 2022Q4 was used in the estimation. Returns had to be calculated from raw data, which were exchange rates and crude oil prices. The research methodologies include quantile regression and VAR-GARCH estimations. The study revealed a long-term association between crude oil market returns and foreign exchange markets, with currency fluctuations negatively impacting crude oil returns. The spill-over effect from the currency market to the oil market is weaker than the transmission effect from the oil market to the currency market of oil-importing countries. In particular, it was empirically established that shocks from past volatility in the currency markets of countries that import oil had commensurately lower volatility in the oil market by 6 percent. Whereas, the spillover from the crude oil market to the currency market depicts that increased turbulence on the crude oil market in the current period stimulated 55.66 percent amplified volatility in the currency market for oil-importing nations. Oil returns had a volatility persistence value of 0.1255, ratifying the weak volatility persistence of oil returns. The size of the volatility persistence of exchange rate returns is 0.997, an indication of high volatility persistence for currency returns. The study found the absence of leverage effects for currency values given a positive coefficient with a magnitude of 0.8529. In other words, bad news does not cause higher turbulence in exchange rate returns than good news will. For crude oil returns, the size of the leverage effects term is -0.0529, which is negative and significant (p<.05), implying that bad news in oil-importing economies has asymmetric impacts on the volatility of returns on crude oil prices. In effect, the oil price returns react more strongly to bad news than these returns react to good news. The findings of the study underscore the intricate relationships between local currencies and energy prices within the context of a global financial market, highlighting the significance of understanding these dynamics for effective decision-making and risk management in an increasingly interconnected world. It was recommended, among others, that international collaboration amongst countries is crucial given the global nature of energy markets.

Analyzing Overnight Momentum Transmission: The Impact of Oil Price Volatility on Global Financial Markets

Fluctuations in oil prices substantially impact both the real economy and international financial markets. Despite extensive studies on oil market dynamics and overnight momentum, a comprehensive understanding of the link between oil price changes and energy market momentum, as well as their broader influence on global financial markets, remains elusive. This study delves into the intricate mechanics of overnight momentum transmission within financial markets, focusing on its origin in oil price fluctuations and its overarching impact on market dynamics. Employing the quantile VAR method, we analyze daily market data from 3 January 2014 to 17 January 2024. This study emphasizes the significance of overnight momentum on the transmission of volatility, particularly in the tails of the distribution, and highlights the necessity for efficient strategies to govern financial stability. The shale oil revolution, COVID-19, the Russia–Ukraine war, and the Israel–Hamas conflict have significantly impacted the interconnectivity of financial markets on a global scale. It is crucial for policymakers to give priority to the monitoring of the energy market to reduce risks and improve the resilience of the system.

Hybrid Prediction Model for International Crude Oil Prices Based on EEMD and SE Reconstruction Methods

Accurate prediction of crude oil prices can provide references for the energy economy and valuable guidance for investors and decision-makers in formulating strategies. However, due to the complexity and volatility of the crude oil market, achieving accurate and reliable crude oil price forecasts is challenging. Therefore, this paper proposes a hybrid forecasting model for crude oil prices that combines Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SE) reconstruction. Firstly, the daily closing price data of WTI crude oil futures from January 2, 2018, to March 21, 2024, is selected. The EEMD method is used to decompose the original data, and the decomposed sub-sequences are reconstructed and merged into high-frequency, mid-frequency, and low-frequency sequences using Sample Entropy (SE). Finally, these sequences are input into six machine learning models for prediction and comparison. The research results show that, firstly, by comparing the R2, RMSE, MAE, and MAPE values, it can be seen that the forecasting ability of the EEMD-SE hybrid forecasting model is significantly better than the EEMD hybrid forecasting model, indicating that the combination of SE methods can effectively improve the model s forecasting ability. Secondly, the EEMD-SE-LSTM model has higher prediction accuracy than other models. Finally, the comparison of models reveals that the BP neural network exhibits the greatest improvement after SE reconstruction. Therefore, it is recommended to improve the crude oil futures price forecasting and risk warning system.

Energy market uncertainties and exchange rate volatility: A GARCH-MIDAS approach

In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of 19 dollar-based exchange rate returns based on monthly metrics of oil price uncertainty (OPU), and relatively broader global and country-specific energy market-related uncertainty indexes (EUI). We find that the global EUIs tend to perform better than the OPU, highlighting the need to look beyond the oil market to capture energy related uncertainties. The country-specific EUIs outperform the benchmark in a statistically significant manner for at least 14 currencies across the short-, medium-, and long-term forecasting horizons.

Bootstrap-after-bootstrap for autoregressive models: an application to Indonesian value of export oil and gas

This research focuses on predicting the value of oil and gas exports in Indonesia, employing a hybrid methodology that combines autoregressive models and a bootstrap approach. Specifically, this research applies the bootstrap-after-bootstrap approach to showcase its effectiveness in improving the accuracy of parameter estimates. Analysis results indicate that the autoregressive model with an order of p=2 minimizes the AIC, BIC, and HQ values, yielding AIC=9.833775, BIC=10.03125, and HQ=9.883440, respectively. Consequently, the AR(2) model emerges as the optimal choice for predicting Indonesia's export value of oil and gas. This research utilizes varying numbers of bootstrap replications (B=100, 250, 500, 1000, and 10000) to assess the impact on prediction intervals. Prediction intervals exhibit less smoothness for B=100 and B=250, whereas B=500 and B=1000 result in a considerably smoother pattern. The highest level of smoothness is achieved for B=10000. The findings underscore that bootstrap-after-bootstrap prediction intervals provide the most accurate and conservative assessment of future uncertainty. Moreover, predictive analysis for the upcoming five periods indicates a projected decline in the export value of oil and gas in Indonesia. Overall, this research demonstrates the efficacy of the bootstrap-after-bootstrap approach in enhancing the precision of predictions and providing robust insights into future uncertainties surrounding Indonesia's oil and gas export market.

The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis

This paper presents a comprehensive multiscale analysis of volatility spillover mechanisms among diverse financial markets utilizing a novel combination of Normalized Adaptive Multivariate Empirical Mode Decomposition (NA-MEMD) and Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. Our analysis spans Bitcoin, crude oil, gold, foreign exchange, emerging stock markets, and developed stock markets, examining their respective roles in global volatility transmission. Utilizing the NA-MEMD methodology and a time-varying volatility spillover index, we find that in the short-term, Bitcoin and crude oil primarily act as volatility receivers, while other markets serve as volatility transmitters. Over the long-term, gold and Bitcoin consistently act as volatility receivers, with Bitcoin and gold showing evident safe-haven effects, while the remaining markets primarily function as volatility transmitters. Particularly post-COVID-19, developed stock markets emerge as significant transmitters of volatility, largely directed towards the crude oil market. By evaluating and comparing risk transmission patterns among financial markets before and during the COVID-19 pandemic, our findings offer valuable insights that can assist policymakers and other stakeholders in managing financial uncertainty in times of global crises.

Corrosion behavior of SMSS 13Cr and SS 316L under chloride solution and H2S/CO2 environments

Abstract Corrosion resistant alloys (CRAs) include a range of different stainless steels and nickel alloys that are used in corrosion-aggressive environments encountered in oil and gas operations. Supermartensitic stainless steel (SMSS 13Cr) has been used in the manufacture of steel tubes for use in oil drilling due a more financially viable option to replace the austenitic stainless steel (SS 316L) in the oil and gas market. This present work estimate the effect of the hydrogen sulphide (H2S) on SMSS 13Cr and SS 316L, in saline solution, simulating the operating conditions in the oil and gas industry. Corrosion behavior was monitored using Potentiodynamic-Potentiostatic-Potentiodynamic (PD-PS-PD) technique in order to obtain pitting potential (Ep) or crevice potential (Ecrev)) and repassivation potential (Erp) while the long-term Open Circuit Potential (OCP) technique was applied to estimate the stable corrosion potential (Ecorr*). A Scanning Electron Microscope (SEM) equipped with Energy Dispersive X-ray Spectroscopy (EDS) and Optical Microscopy were used for surface characterization. With all analysis carried out was possible to propose a trend regarding the pitting probability. The pitting increases with increasing H2S concentration on SSSM 13Cr, consequently, large, and deeper pits were observed, making possible to develop into cracks if there are mechanical stresses. On the contrary, the pitting probability for the SS 316L is low (compared with SMSS 13Cr) for all environments reproduced here in this research.

Oil Palm in India: Market Arrivals and Price Movements

Oil palm is native to West Africa. It is one of nine major oils traded in the global edible oil. The study focuses on the trends in oil palm arrivals and market prices in India, particularly in Andhra Pradesh and Telangana states. There was a significant increase in annual oil palm arrivals from in Andhra Pradesh and Telangana, with a percentage change of 92.43% and 84.83% respectively. This increase was statistically significant at a 10% level, R² values are 0.9243 and 0.8483 respectively. A linear trend analysis for prices of oil palm from 2010 to 2022 indicated a consistent rise in oil palm prices in both states, with Andhra Pradesh seeing an annual increase of 737.05 Rupees and Telangana 757.72 Rupees. The percentage change in prices was 59.18% and 66.08% respectively, with R² values of 0.5918 and 0.6608. Positive and significant correlation coefficient is observed in both the states. Seasonal variations in prices were also analyzed, highlighting that oil palm prices were typically drop after harvest it is influenced by peak harvesting periods and imports of crude oil from other countries. In Andhra Pradesh, the seasonal price index for oil palm Fresh Fruit Bunches (FFBs) was highest in May and April months, then decreased to a minimum level due to increase in the oil palm arrivals. Understanding these seasonal patterns is crucial for effective decision-making by farmers, traders, and policymakers.

Exploring the impacts of major events on the global oil and food markets

To reveal the impact mechanism of major events on the fluctuation and spillover effects of the oil market to three food markets (i.e. corn, wheat, and soybean), a research framework integrating the event study method (ESM) and time-varying parameter vector autoregression-based Diebold–Yilmaz (TVP-VAR–DY) model is constructed. Further, the network for revealing the complex conduct path among them was investigated, and the empirical results indicated that (1) the positive and negative expected events (PEE, and NEE, respectively) comprising the first-order lag impacted the returns series in the oil market positively; (2) the impact of major events on the food market exerted a prolonged lag effect that varied with the different varieties grains; (3) the net spillover effect was mostly from oil to grain, and PEE significantly impacted the two spillover effects (from oil to corn and from oil to wheat), but its spillover effect from oil to soybean was insignificant; and (4) the impact of those events on the spillover effect of oil on wheat was transformed from insignificant to significant when PEE or NEE was controlled. These findings will facilitate the understanding of the internal link between the food and oil markets and provide a crucial reference for investors and policymakers.

Assessing the extent and persistence of major crisis events in the crude oil market and economy: evidence from the past 30 years

This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience.

Does climate policy uncertainty drive the extreme spillovers of carbon-energy-shipping markets?

In addressing the ramifications of climate change, the shipping industry, reliant on energy, has been integrated into the Emissions Trading System (ETS). This study utilizes the quantile connectedness model to investigate the information spillover mechanisms and extreme time-varying interconnections among carbon, energy, and shipping markets. Whether climate policy uncertainty drives the extreme interconnections is also discussed during both pre- and post-Paris Agreement periods, by using GARCH-MIDAS model. The empirical findings underscore the following key points: (i) the systemic connectedness is highly sensitive to market conditions and major events, increasing significantly under extreme market conditions; (ii) following the implementation of the Paris Agreement, an elevated level of informational interdependence has manifested between the carbon market and the energy and shipping sectors; (iii) the information transfer mechanism between carbon and shipping sectors creates direct and indirect spillover paths, with crude oil market mediating the indirect path; (iv) climate policy uncertainty greatly affects the extreme time-varying interconnections, and this impact has decreased after the Paris Agreement came into effect. These results offer valuable insights for market policymakers and shipping companies in achieving a balance between carbon emission reduction and shipping business, particularly amidst heightened climate policy uncertainty.

Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation

The oil market is one of the most important markets for the global economy. Often, oil prices influence the financial results of whole countries and sectors. Therefore, the modeling and prediction of crude oil prices are of high importance. Most up-to-date publications have used daily closing rates in crude oil price modeling, not considering the variability in prices during the day. The application of this kind of price representation leads to a loss of information about the range of price changes during the day, which influences the accuracy of the models and makes them useless in short-term course predictions. In this paper, we introduce the concept of a new state model in a binary-temporal representation, which uses trend analysis, which is one of the main methods used in the prediction of the direction of future changes in the course trajectory. The model described in this paper stands as the first tool that allows for predicting course changes in a given range. The presented work also summarizes the research results of modeling crude oil prices from the last six years, which prove the effectiveness of the mentioned modeling method.

Oil market regulatory: An ensembled model for prediction

This study develops an ensemble framework combining phase space reconstruction and support vector machines to predict oil prices, crucial for economic regulation in energy markets. We analyzed five representative crude oils from spot and futures markets. Our method provides reliable 18-day predictions, demonstrating robustness against non-stationary, noisy data. Compared to traditional models, it shows superior performance, enhancing market stability and surveillance. This research offers a valuable predictive tool for policymakers and market participants, supporting informed decision-making in economic governance.

From oil surges to renewable shifts: Unveiling the dynamic impact of supply and demand shocks in global crude oil market on U.S. clean energy trends

Transitioning to renewable energies has become undeniably important in recent decades, especially for the United States. Oil price shocks are prominent determinants of renewable energy. Hence, this study examines the impacts of oil supply, aggregate demand, and oil-specific demand shocks on renewable energy consumption from 1973:01 to 2021:05 using Structural VAR and Time-Varying Parameter VAR models. Based on the results, oil supply shocks generally have a negligible effect on renewable energy, except those that originate from sharp changes in Saudi Arabian oil production and the U.S. recession-induced oil supply deficit. Aggregate demand shocks, such as those caused by the Persian Gulf War and the Asian financial crisis, negatively affect renewable energy by reducing investments and making oil more affordable. Conversely, oil-specific demand shocks, such as the 1979 Iranian Revolution and the 2008 financial crisis, initially reduce renewable energy attractiveness but ultimately lead to increased renewable energy consumption due to soaring oil prices. This study’s unique approach of separately examining global oil market shocks provides new insights, complementing and extending previous research on the relationship between oil prices and renewable energy consumption. Eventually, policy implications for strengthening incentive policies and adding punitive policies to seeking investment in renewable energy are proposed.

Oil Price Dynamics and Sectoral Indices in India – Pre, Post and during COVID Pandemic: A Comparative Evidence from Wavelet-based Causality and NARDL

Due to the COVID pandemic, the stock market has been affected adversely around the globe and investment decisions are now more challenging and riskier. Hence, in this paper, we aim to investigate the impact of oil prices on the Indian stock market and eight sectoral indices for the period of pre, post, and during the COVID pandemic. The maximal overlap discrete wavelet transform (MODWT) is used to decompose and to denoise the original time series data as oil price and market return are found to be noisy. We employ the wavelet-based Granger causality (WGC) and non-linear, autoregressive distributed lag model (NARDL) to investigate the causality in the frequency domain as well as the short-run and long-run asymmetry of oil price impact. Our analysis shows a feedback relation between low frequency (higher investment horizon) and the long-run asymmetric impact of oil prices on all sectors during all three periods. We discuss the dynamic time-varying relationship between the oil price and sectoral return along with the investment implications in detail.

A multiscale time-series decomposition learning for crude oil price forecasting

Crude oil price forecasting is important for market participants and policymakers. However, accurately tracking oil prices is quite a challenging task due to the complexity of temporal oil data and the nonlinear relationships involved in the forecasting task. In this study, a multiscale time-series decomposition learning framework is proposed to deal with this issue. First, a multiscale temporal processing module is designed to capture different frequency time-series patterns in historical data at various scales. Then, a multiscale decomposition technique is applied to decompose historical crude oil data into various temporal modes, involving global shared information across multiple scales, as well as local specific information that varies at each scale. Finally, a multiscale fusion mechanism is employed to combine these information, which are further used as inputs to construct nonlinear and complex predictive models for crude oil prices. A series of experiments conducted on Shanghai crude oil market demonstrate that the proposed approach outperforms several econometric and machine learning models.