Oil Prices Research Articles

Exploring the nexus of oil price shocks: Impacts on financial dynamics and carbon emissions in the crude oil industry

This empirical study delves into the intricate interplay between oil price shocks (OPS) and their dual impacts on both carbon emissions and the financial performance of the crude oil industry. Utilizing a sophisticated econometric approach, the study employs a Nonlinear ARDL model to meticulously scrutinize the enduring and immediate associations between oil prices and key financial indicators such as stock prices, dividends, and profit ratios of oil exploration industries. Given the significant fiscal and ecological transformations in China during this period, the chosen factors provide a robust framework for measuring changes at both microeconomic and macroeconomic levels, contributing valuable insights to sustainable economic development strategies. Our findings elucidate a statistically significant influence of OPS on the financial performance of the oil industry, particularly in the short term. The increase in the volatility level of OPS manifests a discernible decline in stock prices and profit ratios. Intriguingly, the study further uncovers a nuanced facet of OPS, revealing a prolonged and beneficial impact on carbon emissions in the long term. More importantly, heightened oil prices serve as a catalyst, motivating oil exploration companies to intensify production and exploration activities, thereby increasing carbon emissions in a direct relationship. The implications of these findings are pivotal for policy making, underscoring the imperative for decision-makers and industry stakeholders to pivot towards cleaner, more sustainable energy sources. The research augments the ongoing discourse surrounding the intricate nexus between energy costs and carbon emissions, furnishing invaluable insights that could steer the development of policies geared towards fostering sustainability and mitigating the adverse effects of climate change.

Sustainable energy consumption and finance in the presence of risks: Towards a green economy

Moving towards a green economy, sustainable energy consumption and financial development amid mounting risks moved to center stage of policymaking and academia. Strengthening energy consumption from renewable sources is integral to the attainment of the overarching sustainable development goals of the Paris Agreement. The study employs data from 19 OECD economies spanning from 1990 to 2020 to explore the effect of risks on sustainable energy consumption. Using a multifaceted empirical approach -- comprising Panel OLS, MG estimators, quantile regression analysis and local projection approach -- results establish a key role for political, economic and financial risks in determining energy consumption from renewable sources. Results for the impact of finance indicators on sustainable energy consumption provide robust, though mixed, findings on how to bridge the climate financing gap. Our findings underscore the significant positive influence of human capital development, economic growth, economic openness and oil prices on sustainable energy use. It is further shown that decreasing carbon emissions and making sustainable energy more affordable should be on the central agenda of policymaking for promoting environmental sustainability. A multi-lever strategy is needed when making environmental policies while distributional heterogeneity should also be considered.

Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting

Predicting crude oil prices is critical due to their significant impact on the global economy. While numerous studies have focused on time series forecasting and machine learning methods to predict crude oil spot prices, the use of futures prices segmented by the maturity terms as predictors is comparatively underexplored. This study explored a deep learning approach to investigate using futures prices across different maturities to predict spot prices. Specifically, this paper analyzes the predictive power of one-, two-, three-, six-, and 12-month crude oil futures contracts. This study employs multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN), to forecast crude oil spot prices. This research investigates the performance of these machine learning models when exploring the relationship between futures and spot prices of crude oil. In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms.

On the Effects of Physical Climate Risks on the Chinese Energy Sector

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market.

Transitioning to sustainable economic resilience through renewable energy and green hydrogen: The case of Iraq

The study investigates the potential of transitioning Iraq, a nation significantly dependent on fossil fuels, toward a green hydrogen-based energy system as a pathway to achieving sustainable economic resilience. As of 2022, Iraqi energy supply is over 90 % reliant on hydrocarbons, which also account for 95 % of the country foreign exchange earnings. The global energy landscape is rapidly shifting towards cleaner alternatives, and the volatility of oil prices has made it imperative for the country to diversify its energy sources. Green hydrogen, produced through water electrolysis powered by renewable energy sources such as solar and wind, offers a promising alternative given country vast renewable energy potential. The analysis indicates that, with strategic investments in green hydrogen infrastructure, the country could reduce its hydrocarbon dependency by 30 % by the year 2030. This transition could not only address pressing environmental challenges but also contribute to the economic stability of the country. However, the shift to green hydrogen is not without significant challenges, including water scarcity, technological limitations, and the necessity for a robust regulatory framework. The findings underscore the importance of international partnerships and supportive policies in facilitating this energy transition. Adopting renewable energy and green hydrogen technologies, the country has the potential to become a leader in sustainable energy within the region. This shift would not only drive economic growth and energy security but also contribute to global efforts towards environmental sustainability, positioning country favorably in a future low-carbon economy.

Crude oil price behavior and the adoption of sustainable measures in companies before and after COVID-19

The objectives achieved in the Paris Agreement to reduce greenhouse gas emissions and reduce dependence on fossil fuels have caused, in recent years, a growing importance on sustainability in companies in order to reduce Environmental, social and economic impacts. This study is focused on understanding how the variation in West Texas Intermediate crude oil prices affects the Dow Jones Sustainability Index, and therefore the companies included in it, and vice versa. The research aims to examine the statistical properties of both indices, using fractional integration methods, the fractional cointegration vector autoregressive (FCVAR) approach and the continuous wavelet transform (CWT) technique. The results warn of a change in trend, with the application of extraordinary measures being necessary to return to the original trend, while the analysis of cointegration and wavelet analysis measures reflect that an increase in those adopted based on sustainability by the different companies that make up the index imply a drop in the price of crude oil.

Cotton market contagion: analyzing volatility spillovers across borders

PurposeThe paper analyzes the volatility transmission within the cotton markets by utilizing commodity futures prices from the USA, China and India, encompassing important global events that have significantly influenced the global cotton markets, like the China-USA trade dispute, the COVID-19 outbreak and the Russia–Ukraine conflict.Design/methodology/approachThe authors employ a volatility spillover measure developed by Diebold and Yilmaz (2009, 2012, 2014). Additionally, the methodology proposed by Baruník and Křehlík (2018), which divides the overall volatility spillover into short, medium and long-term segments has been used. To investigate the volatility connectedness, weekly (close-to-close) returns of the cotton futures contracts that are traded on the Chicago Board of Trade Dalian Commodity Exchange National Commodity Exchange of India (NCDEX), and Multi Commodity Exchange (MCX) are considered.FindingsThe paper identifies the presence of long-term volatility transmission among the three cotton futures markets. It demonstrates that a global shock like the Russia–Ukraine conflict has a greater impact on volatility in other markets than USA–China trade disputes. It also highlights the weakening role of the US cotton futures markets as a price leader for Indian and Chinese markets.Research limitations/implicationsSince only three major markets have been studied, the future studies can explore the interconnectedness by including other important markets including Brazil, Turkey, Bangladesh, etc. Further, the moderating role of relationship between other important variables such as cotton production, harvest, inventory, exchange rate, oil price, trade policies, etc. can be examined. Furthermore, the interconnectedness with the regional spot markets in India can also be examined to study how the volatility from the futures market can affect the volatility in the spot markets and vice-versa.Practical implicationsThe understanding of domestic food price volatility and its transmission from international to domestic markets is crucial for designing effective policies to address excessive volatility and protect vulnerable groups like producers, consumers, etc.Social implicationsThe findings emphasize on the substantial market dependence with the US and the Chinese markets which have a significant impact on the Indian markets with considerable implications for hedgers, producers and exporters, particularly during periods of higher volatility.Originality/valueThis study assesses the interdependencies among three major cotton-producing countries and the influence of factors like the USA–China trade tensions in 2018, the COVID-19 crisis and the Russia–Ukraine conflict in order to gauge the degree of volatility interconnection among these key players in the cotton market.

From petrodollars to green power: Fiscal stability and green transition in GCC economies

This study examines the influence of fiscal stability, characterized by fiscal buffers, on the green transition in the Gulf Cooperation Council (GCC) economies from 2005 to 2023. We apply two-way fixed effects, system generalized method of moments (GMM), method of moments quantile regression (MMQR), and panel causality analysis to explore these dynamics. Our findings underscore that a positive fiscal buffer resulting from a significant spread between oil prices and fiscal breakeven levels exerts a substantial positive impact on green transition efforts in GCC countries. The results reveal that countries with higher levels of renewable energy production capacity and urbanization demonstrate more pronounced advancements in green transition initiatives. Conversely, countries grappling with unstable macroeconomic conditions, such as high inflation and external debt, face considerable challenges in achieving significant improvements in green transition outcomes. Additionally, our analysis shows that fossil fuel energy fiscal subsidies negatively influence green transition efforts in the GCC region. Our study emphasizes that policymakers in GCC countries should pursue a dual-pronged strategy: leveraging positive fiscal buffers to diversify their economies in light of potential benefits from high oil prices, and channelling oil and fiscal revenues towards enhancing renewable energy production capacities. This approach aims not only to diversify economic foundations but also to strategically strengthen the infrastructure necessary for sustainable renewable energy transitions in the region over the long term.

Optimization of Warehouse Operations for Upstream Service Companies in the Oil & Gas Industry: A Case Study of XYZ Company

The oil and gas (O&G) sector drives various parts of Malaysia's economy and accounts for 19% of government revenue in 2021. The volatility of oil prices and the COVID-19 pandemic problem in recent years has taught O&G enterprises, especially upstream players, to focus on business needs and employ cost optimization techniques to compete and survive uncertain circumstances. This study seeks to analyze and describe the warehouse operations setup of an onshore supply base service organization that manages and maintains offshore O&G facilities. This study examines space utilization variables from several supply chain perspectives, particularly operations performance. Semi-structured interviews, site observations, and documentation review are used to collect data for the single-case study. The study findings suggest the organization under study has a basic warehouse setup with lean operations staff to complete business functions. In general, the warehouse arrangement for the organization under investigation is comparable to a distribution center, where 70% of warehouse activities comprise, material receiving, handling, and distribution (loading) to several offshore locations. Findings also showed that two factors, namely: material handling flow and movement, and inventory movement affect warehouse space utilization. The volume of material handled by the warehouse based on business demand, as well as the seasonal trend from offshore activities contribute to the frequency of material loading and impact the material throughput and transit time, which were found to have an impact on space utilization too.

Investigating the impact of institutional quality under the petroleum price deregulation policy regime on the economic growth of Ghana

Investigating the impact of institutional quality under the petroleum price deregulation policy regime on the economic growth of Ghana

MACROECONOMIC ASPECTS IN INFLUENCING STOCK PRICE MOVEMENT ON THE INDONESIAN SHARIA STOCK INDEX 2018-2022

This study aims to analyze the influence of macroeconomic variables and world oil prices in influencing stock price movements on the Indonesian Sharia Stock Index (ISSI) in 2018-2022. In this case, stock price movements can be influenced by several factors, one of which is macroeconomics. The research methodology used in this study is to use a quantitative research approach and secondary data type, with quarterly samples from each year 2018 to 2022 which amounted to. The research model carried out is log linear regression panel data analysis conducted through the chow test and classical assumptions using Eviews 10 tools. Based on the results of this study, it states that partially shows the variables of Inflation, BI Interest Rate Economic Growth (GDP) and Money Supply. While simultaneously all variables did not have a significant effect on the Indonesian Sharia Stock Index (ISSI).

Cross-Market Volatility Dynamics Among Crude Oil and South Asian Emerging Markets During the COVID-19 Pandemic.

The COVID-19 pandemic had profound impacts on the global economy, initially triggering a severe economic downturn due to widespread lockdowns, this outbreak generated various economic shocks, a major one being the oil price crash. A brief understanding of how this pandemic is affecting the financial markets, empirical analysis is done on three financial markets. Three stock exchange markets are selected from emerging markets in South Asia (Pakistan, China, and India). The world crude oil market is selected as a reference, to how it transmits volatility to South Asian markets during the COVID-19 period. We employ a bivariate BEKK-GARCH model for analyzing the volatility spillover from November 19, 2019, to March 31, 2021. All markets show negative coefficients with the oil market, indicating that the previous day’s shocks in oil prices reduce today's stock market volatility, with significant values for India and Pakistan. China has a negative coefficient while the rest of the two markets show a positive and significant coefficient means past volatilities in the stock market decrease today’s oil market volatility, likely indicating a reactive or adaptive market behavior to stock volatility. Generally, oil price changes have a significant impact on these markets, with both immediate effects (via ARCH terms) and persistent effects (via GARCH terms). The study's findings offer valuable insights for policymakers, as they help identify the direction of spillovers among interconnected economies. Additionally, portfolio investors can use this information to effectively allocate weights to different asset classes when making investment decisions.

DETERMINANTS OF INFLATION IN TURKEY AMID ECONOMIC AND GEOPOLITICAL TURMOIL

This study aims to investigate the factors influencing inflation in Turkey between 2006:1-2023:11, taking into account the structural changes that occurred during periods of significant economic, political and geopolitical turmoils. For this purpose, we employ the Hacker and Hatemi-J (2006) bootstrap causality test along with the Fourier Toda-Yamamoto bootstrap causality to capture structural changes. Our findings indicate that when structural changes are taken into account, the drivers of inflation are monetary factors such as the money supply and exchange rate. Furthermore, an exogenous factor such as the oil prices is not a determinant of inflation, suggesting that inflation is a domestic factor. Given these findings, the study emphasizes the importance of strong and stable monetary policies in Turkey to control inflation and achieve price stability.

Forecasting crude oil prices with global ocean temperatures

This paper explores the role of ocean temperature (OT) in forecasting crude oil prices. Global OT shows statistically and economically significant predictive power, with corresponding in-sample and out-of-sample R2s of 1.81 % and 1.74 %, respectively. Both southern and northern hemisphere OTs can predict future oil prices, and the former is more predictive. Compared to the Atlantic, OTs in the Indian and Pacific Oceans display considerable predictive gains. In addition, we find that OTs have stronger forecasting power for crude oil prices in the recent period, with the in-sample R2 of global OT for the recent decade's sample reaching as high as 2.74 %. Correspondingly, we find that OTs' predictive power in the full sample can be explained through two channels: market risk and investor attention. In the recent period, in addition to these two channels, OTs have also influenced crude oil prices through the oil fundamental channel.

Government Spending and Industrial Sector Performance in Algeria: Using the ARDL Approach

The study aimed to investigate the impact of government spending on Algeria's industrial sector during the period from 1995 to 2022, with a focus on its effect on the dependent variable. To achieve this, competitive industrial performance was used as an indicator to measure the sector's performance. Additionally, independent variables such as inflation, international oil prices, GDP, and the growth of non-hydrocarbon exports were analysed. The study employed the Auto-Regressive Distributed Lag (ARDL) model to conduct the economic analysis, considering the slow time lags of these variables. The study concluded that there was a positive short-term relationship between government spending and competitive industrial performance in the first year. However, it turned into an inverse relationship in the following year and became insignificant by the third year. In the long term, the study found a significant positive impact of government spending on competitive industrial performance. Additionally, the study identified a significant positive relationship between oil prices and competitive industrial performance, while both inflation and non-hydrocarbon export growth had a significant inverse relationship with competitive industrial performance. The study also found no significant relationship between GDP and competitive industrial performance.

Quantifying the spillover effects of U.S. economic policy uncertainty on emerging market economies using GMM-PVAR model

This paper quantifies the spillover effects of economic policy uncertainty (EPU) in the United States on emerging market economies (EMEs). Using a generalized method of moments (GMM) estimation of a panel vector autoregression (PVAR) model on a dataset­ of 39 EMEs from 2005 to 2019, we find that increased U.S. EPU significantly raises the consumer price index (CPI) and negatively impacts the real GDP of these economies. Additionally, heightened U.S. EPU leads to a depreciation of emerging market currencies and a reduction in short-term interest rates. We employ a news-based EPU index developed by Baker et al. (2016) and conduct robustness checks using forward orthogonal transformation, an alternative EPU index, and by addressing the potential endogeneity of the oil price uncertainty (OPU) index. Our findings highlight the adverse effects of U.S. economic policy uncertainty on key macroeconomic variables in emerging markets, underscoring the importance of stable economic policies and robust institutions to mitigate these impacts.

Exploring vehicle preference dynamics: Analyzing the influence of oil price volatility on consumer car purchase decisions - A comparative survey experiment

China's automobile market is continuously expanding, leading to ongoing growth in oil demand. Fluctuations in oil prices will inevitably impact the travel patterns and car purchasing behavior of automotive consumers. This paper analyzes the influence of gasoline price fluctuations on the travel patterns and car purchasing behaviors of auto consumers with varying incomes, employing a dynamic perspective. We conducted a questionnaire survey of auto consumers in four first-tier cities in China and performed a comparative analysis of questionnaire data from 2015 to 2022. The empirical findings are as follows: (1) Under conditions of elevated oil prices, fuel car consumers exhibit an increased susceptibility to oil price fluctuations, which may cause fuel car consumers to curtail their travel activities. (2) The 10 yuan/liter is the threshold for middle - and upper-income consumers to change their car buying or travel habits, which has not been affected by oil price fluctuations. (3) For the cohort considering electric vehicles, gasoline prices stand as the primary determinant influencing their car buying choices. The primary conclusions serve as significant references for the government to craft corresponding carbon emission reduction policies amidst fluctuating oil prices.

Does oil price uncertainty affect corporate total factor productivity? Evidence from China

In the era of high-quality economic development, oil price volatility (OVX) and the total factor productivity (TFP) of corporations are pivotal issues for both policymakers and scholars. This study leverages the implied volatility of oil prices and financial data from Chinese listed industrial companies spanning 2010 to 2022 to investigate the influence of OVX on firms’ TFP and the underlying mechanisms. The findings reveal that OVX substantially dampens firms’ TFP, with corporate leverage and financialization identified as key channels through which this impact occurs. Further heterogeneity analysis indicates that the negative impact of OVX on TFP is particularly pronounced in firms operating in industries with low concentration and among small and medium-sized enterprises (SMEs). The extension analysis suggests a threshold effect in the relationship between OVX and corporate TFP, with the suppressive effect of OVX on TFP intensifying as the level of corporate financialization increases. Consequently, it is imperative for policymakers to closely monitor oil price fluctuations and implement timely strategies to mitigate the risks associated with OVX.

Carbon Price Point–Interval Forecasting Based on Two-Layer Decomposition and Deep Learning Combined Model Using Weight Assignment

With the intensification of global warming, the demand for carbon emissions reduction has gradually increased in various countries. Carbon price is crucial for promoting the activation of the carbon trading market and facilitating emissions reduction. However, the current carbon price has non-linear characteristics, large fluctuations, and high complexity, making accurate predictions challenging. To effectively predict the trends and change of carbon price, this study proposed a hybrid deep learning point–interval prediction model. First, an improved variational mode decomposition–symplectic geometry mode decomposition (IVMD–SGMD) two-layer decomposition model was constructed to decompose the carbon prices into regular subsequences. Then, attention–temporal convolutional network–bidirectional gated recursive unit (Attention-TCN-BiGRU) and Encoder–Decoder long short-term memory (LSTM) combined prediction models were constructed for the prediction of subsequences. The entropy method (EM) was used to assign weights to the predictions of two models to achieve model complementarity and a linear reconstruction of the models’ results. Then the error correction was performed to obtain the final prediction results. This study conducted an experiment on carbon prices in the Guangdong and Shenzhen markets. The mean absolute error (MAE) of the two datasets were reduced by 89.69% and 87.43% lower than that for LSTM. To demonstrate the model's adaptability, prediction experiments conducted on natural gas and crude oil prices were employed, confirming its strong predictive accuracy in energy price forecasting. Based on the point prediction error, the interval prediction using the improved kernel density estimation (IKDE) provides more carbon market information for decision makers. The proposed model aids government energy policy formulation and fosters ongoing efforts to reduce carbon emissions.

Do Supply Chain Pressures Affect Consumer Prices in Major Economies? New Evidence from Time-Varying Causality Analysis

This study explores the dynamic causality between the consumer price index and supply chain pressure index in two large economies. Despite extensive research into the role of supply chains at the firm level, literature on their impact on time-varying inflation and macroeconomic stability is scarce. Using data from the United States and the European Union from January 2000 to December 2021, we employ novel time-varying causality techniques to examine the evolving relationships among inflation, supply chains, real gross domestic product, interest rates, and oil prices. Our findings show that supply chains have a significant dynamic relationship with consumer prices, particularly during economic crises like the 2008 global financial crisis and the COVID-19 pandemic. Robustness tests including impulse response functions and the autoregressive distributed lag model confirm this causality. These results are critical for policymakers, emphasizing the importance of supply chains in controlling inflation and achieving global economic resilience and stability.