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Related Articles This study examines how fuel pricing in Ghana has been used as a political tool to win votes and the government's role in this process. Applying Mile's Law, it explores post‐truth theory through content analysis of secondary data and interviews with energy experts. The findings indicate that political parties gain significant political leverage by promising lower fuel prices during campaigns but fail to deliver once in office. The government's influence on fuel pricing is minimal, largely due to factors beyond its control. To stabilize fuel prices, the study recommends improving fiscal and economic performance to combat currency instability and educating the public on the factors influencing fuel pricing to prevent misinformation.Asiegbu, Martin F., Okey Marcellus Ikeanyibe, Pius Otu Abang, Okwudili Chukwuma Nwosu, and Chuka Eugene Ugwu. 2024. “Natural Resource Fund Governance and the Institutionalization of Rent Seeking in Nigeria's Oil Sector.” Politics & Policy 52(1): 169–95. https://doi.org/10.1111/polp.12579.Ayanoore, Ishmael, and Sam Hickey. 2022. “Reframing the Politics of Natural Resource Governance in Africa: Insights from the Local Content Legislation Process in Ghana.” Politics & Policy 50(1): 119–36. https://doi.org/10.1111/polp.12449.Kuyini Mohammed, Abdulai. 2013. “Civic Engagement in Public Policy Making: Fad or Reality in Ghana?” Politics & Policy 41(1): 117–52. https://doi.org/10.1111/polp.12003. [ABSTRACT FROM AUTHOR]

The study of Ellwanger and Snudden (J Bank Financ 154:106962, 2023) discovers a new and remarkable finding regarding the ability of the random-walk model using the end-of-month price of crude oil to forecast future monthly average crude oil prices out-of-sample. The magnitude and nature of the relative predictive gains lead the authors to question whether any other model can "beat" the end-of-month price random-walk out-of-sample. I make an attempt to do so by relying on plain end-of-month crude oil price autoregressive fractionally integrated moving average (ARFIMA) models. These models are more nuanced and at the same time comprehensively account for one of the most salient features of the price of crude oil, namely, its persistence. Consequently, a forecaster is inclined to believe that they might "beat" the end-of-month random-walk model. However, out-of-sample results demonstrate that a uniform (definitive) conclusion cannot be drawn. On the contrary, conclusions depend heavily on the definition of "beating", i.e. population-level versus finite-sample relative predictability, the forecast horizon, state of the business cycle and the choice of the crude oil price series itself. The decisions, judgments and dilemmas faced by the forecaster are presented and elaborated. [ABSTRACT FROM AUTHOR]

Research has identified volatility transmission from the oil market to the tanker freight market through external shocks. However, in the presence of intricate nonlinear structures, the academic literature often encounters difficulties in identifying cycles and their correlations across various timescales. This paper provides a multi-market analysis to comprehend the information from shock effects on different tanker routes and multi-peak fitting. Under different shock regimes, crude oil market and tanker freight rate shocks could transit bidirectly. When events occur, the crude oil market prices the expectations. However, when the actual performance of the future market differs from the traders' predictions of the future market, a price gap exists. Generally, the trade opportunity is tough to catch up on because only partial information can be found. In this study, we investigate the volatility connection of multi-markets and shock effects to clarify previously undisclosed information using multi-peak analysis. The information gathered and double-checked by cargo markets, crude oil supply-demand balance, and tanker freight prices of various tanker types could assist us in identifying prospective trends and investment opportunities. The volatility of each market, as well as the correlation of multi-markets, gives insights to crude oil dealers, tanker market participants, and energy regulators. [ABSTRACT FROM AUTHOR]

This research incorporates crude oil price series with different time scales, China's dirty energy, new energy and green bond index into a system, so as to investigate the spillover effects during some major periods. The results indicate that the spillover effects of short-term oil price fluctuations are mild, and the green bond market is the least affected. While the long-term oil price fluctuations have profound effects on the aforementioned three markets, the spillovers are especially enhanced by extreme events. Our results are verified by appropriate robustness tests. [ABSTRACT FROM AUTHOR]

In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical results show that the CNN–LSTM model outperforms these models. [ABSTRACT FROM AUTHOR]

This study aims to examine the relationship between crude oil prices, population, exports, inflation, and economic growth in Iraq based on annual time series from 1997 to 2022. As it is well known, crude oil is one of the most important resources for energy production, transportation, and various industries. Moreover, this energy source has a critical importance in terms of the international trade. Therefore, the effects of crude oil in Iraq’s economy is analyzed in this study. To achieve the degree and magnitude of the parameters used in the models, the Ordinary Least Squares method and the Johansen cointegration test are utilized. According to the empirical results of the study, while crude oil prices, population, and exports have a positive impact on economic growth, inflation has a negative impact. Additionally, a long-term relationship is found between crude oil prices, population, exports, inflation, and economic growth.as a result of cointegration test with the VAR system. Based on these results, it can be said that sustainable oil producing is very crucial in Iraq. Since the government income of Iraq relies heavily on revenue generated from oil exports, the improving and regulation activities should be considered in the oil sector. On the other hand, as the oil is finite resource, the local policymakers need to focus on researching alternative sources for exports and research the ways to boost the country’s GDP.

Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning. [ABSTRACT FROM AUTHOR]

Many studies have examined the asymmetric effect of US dollar-denominated crude oil prices on petroleum product prices. The 'rockets and feathers' argument suggests that a crude price increase raises petroleum product prices more than a corresponding decrease in crude prices lowers product prices. However, for the countries that do not use the US dollar as a medium of exchange, petroleum product prices are also affected by the exchange rates. This paper analysed the asymmetric effects of both US dollar-denominated crude oil prices and exchange rates on local currency-denominated diesel prices for 27 European countries in the short run as well as long run. The overall empirical evidence suggests that, in the short run, diesel prices react more to crude oil price increases than to a decrease, parallel to the 'rockets and feathers' argument. However, contrary to that argument, the long-run adjustment is the opposite. As for exchange rate shocks, again the 'rockets and feathers' argument holds and diesel prices respond more to exchange rate depreciation than appreciation in the short and long run. [ABSTRACT FROM AUTHOR]

This study investigates the impact of exchange rate and oil price on economic growth in Nigeria. The study uses growth rate as a proxy for economic growth and combines crude oil price, exchange rate, and interest rate as independent variables. Annual time-series data from 1990 to 2023 was gathered from secondary sources, including the World Development Indicators, Central Bank of Nigeria Statistics, and OPEC database. The data was analyzed using a variety of statistical methods and procedures, including descriptive statistics, unit root tests, correlation analysis, serial correlation tests, heteroskedasticity tests, and normality tests. The results reveal a weakly negative association (-0.0906) between growth rate (GR) and Nigeria’s exchange rate (EXR), indicating that the growth rate tends to decline significantly as the exchange rate increases. Conversely, the strong positive connection suggests that GR in Nigeria is significantly impacted by changes in the currency rate. Additionally, the study finds a weakly positive correlation (0.2221) between the price of crude oil (COPr) and GR, suggesting that the growth rate tends to increase to some extent in tandem with an increase in the price of crude oil. GR and interest rate (INT) also have a 0.1155 weakly positive association. The Vector Error Correction (VEC) model results show that the first and second lags of the exchange rate changes have statistically significant positive effects, while the lags of the commodity price and interest rate changes do not appear to be statistically significant. The study also finds no evidence of serial correlation in the residuals of the VEC model at the tested lag orders. [ABSTRACT FROM AUTHOR]

This paper investigates the transmission of financial contagion from the European to the Russian stock market during the COVID-19 pandemic. Financial contagion refers to the spread of instability and shocks across individual countries, sectors, or markets during a crisis, where the relationship between returns and volatility of different assets goes beyond normal interactions. Using the construction of extended autoregressive models, we test the contagion of the RTSI composite index from the EURO STOXX 50 index, with the US dollar exchange rate and the spot price of Urals crude oil serving as control variables. The calculation of the moving coefficient of variation in assets prices allows us to distinguish the pre-crisis, crisis and post-crisis periods, for which three separate autoregressive models are built. The contagion of the Russian stock market from the European stock market in these models is identified in two ways: 1) based on the growth and significance of estimated coefficient for the tested variable (STOXX 50 index return) during the crisis; 2) through an increase in the contribution of the tested variable to the explained variance of the dependent variable (RTSI return). In addition, we tested contagion based on the method of central co-moments of the distribution of returns, skewness and volatility of the tested and dependent variable. The analysis has convincingly demonstrated the existence of a financial contagion effect from the European stock market to the Russian stock market in the short term — strengthening of the impact of the European STOXX 50 index on the Russian RTSI index in the acute phase of the pandemic. Understanding the factors contributing to the spread of market shocks in the context of financial globalization can help policymakers to implement effective financial regulatory measures and maintain long-term financial stability in line with national interests. For investors, it helps to identify potential risks and opportunities, enabling optimal hedging and diversification response strategies.

The energy revolution in Saudi Arabia has accelerated significantly since 2016, driven by the National Vision 2030. Significant changes to energy subsidies took place, and the renewable energy sector has seen rapid growth. The paper presents an empirical analysis of the Saudi energy transition by emphasizing the drivers of fuel consumption in KSA. It primarily attempts to explore the long-run (LR) connection between oil consumption and several economic variables such as economic growth, crude oil prices, investment, and the labor force in Saudi Arabia (KSA) from 1991 up to 2021. The paper implemented the vector error correction model (VECM) and performed different diagnostic tests to provide more evidence about the validity and robustness of the tests. The empirical findings highlighted how important the labor force, savings, GDP, and crude oil price are in determining oil consumption for KSA. The law of demand is significantly present, which negatively affects oil consumption for KSA as an oil exporting country. The results also supported the existence of a long-term direct correlation between the variables and oil consumption. Furthermore, the short-term estimation highlighted that only saving has a negative impact on oil consumption for a single lagged period. Our findings provide governments and regulators with further incentive to slow the expansion in oil consumption, as a larger labor force is demanding more oil to attain the target, faster economic growth, and increased savings are all contributing factors. Our findings are significant because they can assist policymakers, investors, and regulators in generating more efficient oil substitutes and making them affordable for the economy. [ABSTRACT FROM AUTHOR]

In this paper, we examine the differences between CNY and other major currencies in coherence and the lead–lag relationship across the different time horizons to clarify whether crude oil, monetary factors, or both drive the movement of exchange rates. We employ partial and multiple wavelet coherence analyses to examine oil-exchange co-movement by excluding the influence of Federal Reserve System (FED) monetary policy — namely, the stance and uncertainty of monetary policy — and the difference in domestic and foreign monetary policy rates. Overall, we find that monetary easing by the FED is a major factor driving the co-movement. Specifically, after excluding the possible effects of monetary policy factors, the movement of the Euro exhibits the strongest and the Japanese yen the weakest dependence on crude oil price changes, whereas the British pound shows a moderate dependence. By contrast, the CNY shows strong co-movement with the crude oil price only over the long term implying the low degree of integration with the global markets. Our empirical results provide meaningful information for investors and policymakers. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the effect of oil prices, economic growth and information communication technology (ICT) on investment into renewable energy transition (RET). Design/methodology/approach: Based on six selected African countries (i.e. Algeria, Egypt, Angola, Ethiopia, South Africa and Nigeria), the study uses a nonlinear autoregressive distributed lag model over the period from 1995 to 2020. Findings: The results show that increasing oil prices, by substitution effect, leads to increasing RET investment, while declining oil prices lead to decreasing RET investment in the short and long run. Furthermore, the results reveal that increasing real gross domestic product leads to increased RET investment, while declining real gross domestic product (GDP) leads to decreasing RET investment both in the short and long run. Simultaneously, the study shows that increasing ICT has a significant and positive impact on RET investment, while declining ICT has a significant negative impact on RET investment in the short and long run. Originality/value: The findings of this study have advanced the understanding of which factors significantly influence RET investment and the need to concentrate efforts on strategically addressing those factors. The findings indicate that these countries are at the progressive stage in terms of renewable energy; though increasing oil prices contribute to rising RET investment, the countries can be more proactive by improving the full potential of ICT as well as facilitating the growth of their economies. [ABSTRACT FROM AUTHOR]

The OPEC+, composed of the Organization of the Petroleum Exporting Countries (OPEC) and non-OPEC oil-producing countries, exerts considerable influence over the global crude oil market. However, existing literature lacks a comprehensive application of this factor in oil price forecasting, primarily due to the complexity of measuring such policy evolutions. To address this research gap, this study develops a news-based OPEC+ policy index based on text mining methods for comprehensive analysis and forecasting of the oil price. First, by crawling and mining news headlines related to OPEC+ production decisions, a dynamic and high-frequency (weekly) OPEC+ policy index is established. Second, the linear and nonlinear relationship between the proposed OPEC+ policy index and the WTI crude oil futures price is thoroughly examined, assessing the potential predictive power of the index in explaining the movements of the crude oil price. Third, the forecasting efficacy of the constructed index on the oil price is rigorously evaluated across eight econometric and machine learning models. Key findings include: (1) The proposed weekly OPEC+ policy index demonstrates strong concordance with OPEC+ production change decisions, exhibiting notable peaks and troughs corresponding to OPEC+ Ministerial Meetings. (2) The relationship analysis demonstrates a strong linear and nonlinear association between the proposed OPEC+ policy index and the crude oil price. (3) For oil price prediction, models incorporating our proposed OPEC+ policy index demonstrate superior performance compared to models without this index. In particular, the index exhibits a more significant predictive effect within three-week forecasting horizons and performs exceptionally well during periods of pandemic and the Russia-Ukraine conflict. In addition, the OPEC+ policy index also exhibits a significant predictive effect on the daily crude oil price and natural gas price, further confirming the robust and powerful forecasting capability of this index within the energy system.

Crude oil has important role in global economy, including Indonesia with considerable dependence on crude oil energy consumption. The increase in crude oil prices can be triggered by several factors, one of which is geopolitical conflict that occurred due to Russia's invasion of Ukraine on February 24, 2022. As the result, world crude oil prices rose above US$100 per barrel for the first time since 2014. Therefore, this study uses pulse function intervention analysis approach to evaluate the impact of certain events in predicting data over the next few periods. The pulse function is used because the intervention occurs at the moment t only. The data used starts from June 8, 2020 to September 19, 2022 on weekly basis with the proportion of training and testing data is 90:10. The best intervention model obtained is ARIMA (3,2,0) with b=0, s=1, r=2, and intervention point at T=91. The prediction results for the next 12 periods obtained MAPE value of 2.8982% and MSE of 10.2687. This study is expected to help reduce risks due to uncertainty in world crude oil prices and in line with the goals of the Sustainable Development Goals (SDGs) to ensure access to reliable, sustainable, and affordable energy.

This paper employs various GARCH-type models and the daily data from 3 July 2006 to 30 June 2021 to examine the effect of crude oil prices and stock price index on exchange rates for Indonesia, the largest oil producer in Southeast Asia. Since the share markets and oil prices are very volatile, testing the stability of the parameters or system is desirable. We achieve this by using the Nyblom’s fluctuations test and account for the structural break associated with the fluctuations. Findings reveal that lower oil price return leads the Indonesian currency per US dollar to depreciate. In addition, we find that stock return has negative and significant relation with exchange rates. This lends support to the portfolio balance effect in which a decrease in stock prices leads to a depreciation of Indonesian Rupiah against the US dollar. Evidence from EGARCH model shows that shocks to the volatility of exchange rate have a symmetrical effect. Our results suggest that as lower oil prices and stock prices contributes to depreciation of Indonesia rupiah against USD, an appropriate monetary policy may require adjustment of interest rates to resist the exchange rate fluctuations without being detrimental to the banking system.

This study examines the impact of crude oil and fertiliser price changes on maize crop prices in India using monthly time series data from May 2007 to September 2022. The best‐fitted Autoregressive Integrated Moving Average model with the lowest Akaike's Information Criterion value is selected, and the Box–Ljung test is used to validate the prediction accuracy. Empirical results suggest that maize price is driven by crude oil and fertiliser prices since it has been found that maize prices are highly positively correlated with the prices of crude oil and fertiliser. Also, a strong correlation has been found between crude oil and fertiliser prices. The investigation for forecasting the next 15 months from November 2022 also revealed that maize prices showed no volatility because of a constant trend, but crude oil prices showed a declining trend, while di‐ammonium phosphate prices showed an increasing trend over the period from November 2022 to February 2023. They then declined to June 2023 but again showed an increasing trend from July 2023 to January 2024 and achieved the highest price in December 2023. Because of a constant trend, urea prices showed no volatility over the 15 months. The estimates can aid the government in formulating policies to maintain agricultural crop production and control input price changes to meet the growing population's food demands. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak and the implementation of peak and carbon neutral policies have severely impacted oil price volatility and the industrial sector. Exploring the impact mechanisms between oil prices and industries is particularly important for accurate forecasting of crude oil prices. As one of the world's largest commodity consumers, China's crude oil market is more representative and susceptible to external factors than that of developed countries. In this paper, we propose an analytical forecasting framework based on the causal effects between Shanghai crude oil prices and various industries in China to improve the forecasting accuracy of crude oil prices. Information geometric causal inference (IGCI) is applied to detect causal relationships between 31 different industries in China and Shanghai crude oil prices in the three time periods before, during and after COVID-19, and industries with strong causal information effects on crude oil prices in the long run are screened out as additional features. An oil price forecasting model based on Gray Wolf Optimization and Extreme Learning Machine (GWO-ELM) is proposed. Considering the small amount of data for Shanghai crude oil, this paper proposes a cross-learning data approach to solve the problem. Experimental results show that the GWO-ELM model outperforms RF, LSTM, GRU, and migration learning-based Tr-LSTM and Tr-Adaboost models in the task of Shanghai crude oil futures price prediction, and find that industry characteristics with long-term causal effects on oil prices can improve the model prediction accuracy. Our proposed analytical prediction can capture the oil price trend more accurately through the information of the industry and solve the problem of insufficient training data for the model. The application of this framework is expected to provide new methods and ideas for data mining of crude oil and other futures prices. [ABSTRACT FROM AUTHOR]

This study investigates empirically the long- and short-run impact of crude oil price and financial globalization on economic growth and financial development in selected oil-rich African countries. The data usage covers 1980 to 2021 by applying the autoregressive distributed lag (ARDL) modeling to determine the short- and the long-run estimates, and the ARDL-ECM Granger causality to discover the causalities direction. The empirical results reveal that crude oil price and financial globalization have no significant effect on restructuring the economic sustainability patterns in either the long or the short run. There are various causality directions found for those countries involved in this study within the short- and long-run periods. This study recommends that the Republic of Congo and Nigeria should always maximize oil revenue during periods of oil price boom to offset the economic severity during periods of oil price reduction. Further, Algeria and Nigeria's policymakers should avoid protectionism against financial globalization, economic growth, and trade to mobilize the resources required to be at the fulcrum of future economic restructuring. The empirical findings will be useful for policymakers to design a suitable growth model for African countries that highly depend on crude oil resources as an engine of economic growth. [ABSTRACT FROM AUTHOR]

The crude oil market, as a complex evolutionary nonlinear driving system, is by nature a highly noisy, nonlinear and deterministic chaotic series of price series. In this paper, a computational intelligence-based portfolio model is constructed to forecast crude oil prices using weekly price data of West Texas intermediate crude oil (WTI) crude oil futures from 2011 to 2021. First, the WTI crude oil price series are decomposed using the ensemble empirical modal decomposition method (EEMD) and the set of component series is reconstructed using the cluster analysis method. Second, the reconstructed series are modeled and predicted using neural network models such as time-delay neural network (TDNN), extreme learning machine (ELM), multilayer perceptron (MLP) and the GM (1, 1) gray prediction algorithm and the output of the model with the best prediction effect for each component is integrated. Finally, the EGARCH model is used to further optimize the predictive power of the combined model and output the final predicted values. The results show that the combined model based on computational intelligence has higher forecasting accuracy than single models such as GM (1, 1), ARIMA, MLP and the combined EEMD-ELM model for forecasting crude oil futures prices.

The energy markets, particularly oil and gas, have been significantly affected by the outbreak of the COVID-19 pandemic in terms of price and availability. In addition to the pandemic, the Russia-Ukraine war has contributed to concerns about the reduction in the oil supply. AI techniques are widely employed for prediction oil prices as an alternative to traditional techniques. In this paper, an AI-based optimization model called adaptive fox-inspired optimization (AFOX) model is presented, then recurrent neural network (RNN) is combined with AFOX to form a hybrid model called recurrent neural network with adaptive fox-inspired (RNN-AFOX) model. The proposed model is used to predict Crude Oil Prices. In the proposed model, AFOX is used to find the best hyper-parameters of the RNN and employed these hyper-parameters to build best RNN structure and use it to forecast the closing price of the oil market. The results show that the RNN-AFOX model achieved a high accuracy prediction with very small error and the coefficient of determination (R-squared) equal to 0.99 outperforming the RNN model in terms of accuracy prediction by about 24%, the FOX model by about 20% and the AFOX model by about 14%. Moreover, RNN-AFOX was evaluated under the impact of the COVID-19 pandemic and the Russia-Ukraine war. The results show the efficiency of RNN-AFOX in forecasting the closing prices of oil with high accuracy. In general, the proposed RNN-AFOX model overcomes other studied models in terms of Mean Absolute Percentage Error, Mean Absolute Error, Mean Square Error, Root Mean Square Error, coefficient of determination (R-squared) and consumption time. [ABSTRACT FROM AUTHOR]

The uncertainty in the economic growth had been highly contributed by multiple factors which does not exclude the suggestion on the impact from the crude oil prices. The major impact from the crude oil prices had been strong in influencing the rising prices of the consumer's goods and services which often translate into the rising inflation for the country. With reference to this., the objective for the current study will focus into exploring the investigation on the impact of the crude oil price against the impact on the inflation rate within the economic condition of Malaysia. The quantitative study method had been developed where the results and findings for the study had achieved the outcome identifying the significant positive relationship between the crude oil price in affecting the inflation rate in Malaysia. The outcome for the research had been crucial address towards the achievement in the academic study reducing the gap in literature review as well as developing further reference for the individuals and businesses to plan the financial resources in the future in Malaysia. [ABSTRACT FROM AUTHOR]

Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil.

The energy revolution in Saudi Arabia has accelerated significantly since 2016, driven by the National Vision 2030. Significant changes to energy subsidies took place, and the renewable energy sector has seen rapid growth. The paper presents an empirical analysis of the Saudi energy transition by emphasizing the drivers of fuel consumption in KSA. It primarily attempts to explore the long-run (LR) connection between oil consumption and several economic variables such as economic growth, crude oil prices, investment, and the labor force in Saudi Arabia (KSA) from 1991 up to 2021. The paper implemented the vector error correction model (VECM) and performed different diagnostic tests to provide more evidence about the validity and robustness of the tests. The empirical findings highlighted how important the labor force, savings, GDP, and crude oil price are in determining oil consumption for KSA. The law of demand is significantly present, which negatively affects oil consumption for KSA as an oil exporting country. The results also supported the existence of a long-term direct correlation between the variables and oil consumption. Furthermore, the short-term estimation highlighted that only saving has a negative impact on oil consumption for a single lagged period. Our findings provide governments and regulators with further incentive to slow the expansion in oil consumption, as a larger labor force is demanding more oil to attain the target, faster economic growth, and increased savings are all contributing factors. Our findings are significant because they can assist policymakers, investors, and regulators in generating more efficient oil substitutes and making them affordable for the economy.

This study examines the forecasting power of the gas price and uncertainty indices for crude oil prices. The complex characteristics of crude oil price such as a non-linear structure, time-varying, and non-stationarity motivate us to use a newly proposed approach of machine learning tools called XGBoost Modelling. This intelligent tool is applied against the SVM and ARIMAX (p,d,q) models to assess the complex relationships between crude oil prices and their forecasters. Empirical evidence shows that machine learning models, such as the SVM and XGBoost models, dominate traditional models, such as ARIMAX, to provide accurate forecasts of crude oil prices. Performance assessment reveals that the XGBoost model displays superior prediction capacity over the SVM model in terms of accuracy and convergence. The superior performance of XGBoost is due to its lower complexity and costs, high accuracy, and rapid processing times. The feature importance analysis conducted by the Shapley additive explanation method (SHAP) highlights that the different uncertainty indexes and the gas price display a significant ability to forecast future WTI crude prices. Additionally, the SHAP values suggest that the oil implied volatility captures valuable forecasting information of gas prices and other uncertainty indices that affect the WTI crude oil price. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to investigate the relationship between the price of crude oil and the BRICS countries 04/01/2016 to 05/01/2023, by analyzing the spillover effects and connectedness using the quantile VAR approach. Design/Methodology/Approach: Researchers focused on three quantiles - median, high, and low to capture the connectedness. Findings: The results show first, that there is higher total connectedness in the bearish and bullish market conditions compared with normal conditions. Moreover, the degree of connectedness is even stronger during periods of crises such the case during the Covid-19 pandemic and the Russian-Ukrainian war. This shows that under extreme market conditions, the strength of the connectedness increases with the size of the shock, suggesting a symmetric relationship. Practical implications: The frequency connectedness is divided into high and low-frequency and it is discovered that the short-term TCI had a greater impact on the total TCI than the long-term TCI. Originality value: These findings can be valuable for both international investors and policy makers. [ABSTRACT FROM AUTHOR]

This study scrutinizes the impacts of oil price fluctuations, financial inclusion, and energy consumption on carbon flare-ups in 20 Asian developing nations. For empirical analysis panel data for the period from 1990 to 2020, and the CS-ARDL model is applied. Furthermore, our data confirm the existence of CD), slope parameter heterogeneity (SPH), and panel co-integration among the variables. For the stationarity of variables, this study applies a cross-sectional augmented IPS (CIPS) unit root test. The outcomes of the study depict that the price volatility of oil in the selected countries affects carbon emissions positively and significantly. This is because these nations use oil as a primary source of energy for the production of electricity, for manufacturing activities, and mainly in the transport sector. Financial inclusion helps to mitigate carbon emissions in developing Asian economies by motivating the industrial sector to adopt clean environmentally friendly production methods. Therefore, the study suggests that reducing dependency on oil and promoting renewable energies, and improving access to affordable and financial products will provide a pathway to achieve UN Agenda-13, a clean environment by mitigating carbon emissions in developing Asian nations. [ABSTRACT FROM AUTHOR]

This study investigates empirically the long- and short-run impact of crude oil price and financial globalization on economic growth and financial development in selected oil-rich African countries. The data usage covers 1980 to 2021 by applying the autoregressive distributed lag (ARDL) modeling to determine the short- and the long-run estimates, and the ARDL-ECM Granger causality to discover the causalities direction. The empirical results reveal that crude oil price and financial globalization have no significant effect on restructuring the economic sustainability patterns in either the long or the short run. There are various causality directions found for those countries involved in this study within the short- and long-run periods. This study recommends that the Republic of Congo and Nigeria should always maximize oil revenue during periods of oil price boom to offset the economic severity during periods of oil price reduction. Further, Algeria and Nigeria’s policymakers should avoid protectionism against financial globalization, economic growth, and trade to mobilize the resources required to be at the fulcrum of future economic restructuring. The empirical findings will be useful for policymakers to design a suitable growth model for African countries that highly depend on crude oil resources as an engine of economic growth.

Effective crude oil price forecasting is essential for energy supply stabilization, investment decisions, policy formulation, and economic impact assessment. However, previous studies of crude oil futures price forecasting either consider only a single variable without adequately accounting for the influence of various factors on crude oil prices, or they consider only the same frequency of influences and ignore the importance of mixed frequencies, resulting in forecasts that do not achieve the desired results. To overcome these problems, this paper proposes a new multivariate combinatorial mixed frequency forecasting system to predict the weekly closing price of crude oil futures. The system is divided into four modules: Data denoising, Feature selection, Combined forecasting, and Performance evaluation module. To obtain smooth data, ICEEMDAN is used to denoise the original data. Furthermore, to select appropriate variables and reduce model redundancy, recursive feature elimination is used to select appropriate variables with low frequency. Considering the importance of mixed-frequency data, the mutual information method was used to select appropriate high-frequency variables for modeling the crude oil price forecast model. To overcome the shortcomings of Back Propagation Neural Network, Gate Recurrent Unit, and Radial Basis Function Neural Network models, integrate their advantages, and obtain accurate and stable prediction results, a combined forecasting mechanism based on a Multi-Objective Sparrow Search algorithm was developed to obtain both point and interval forecasting results, and finally, two data sets were selected for empirical analysis. The results show that the mean absolute percentage errors of the point forecast of this model are 1.96% and 1.84%, respectively, about 31% and 15% higher than those of the competing models (mean absolute percentage errors 2.57% and 2.13%, respectively). For interval forecasting, the accumulated width deviation is 0.0037 and 0.002, respectively, about 35% and 25% higher than those of the competing models (accumulated width deviation 0.005 and 0.0025, respectively). Thus, the proposed forecasting framework outperforms all comparative models and can be used effectively for forecasting crude oil prices.

As a globally hard currency, crude oil has influenced all aspects of the world, especially because volatility in its price seriously affect economies, politics and wars. The prediction of oil prices is challenging due to remarkable price volatility, uncertainty, elusiveness, and complexity. There have been many papers adopting traditional machine learning (ML), economic approaches or combinations of the two; however, these papers have rarely focused on the performance of individual models and ignored less popular but complex models. Considering the above difficulties and situations, in this paper, we design an advanced approach for valuable and robust forecasting of crude oil prices to investigate the differences among the three other popular methods. For this purpose, the Random Forest (RF), XGBoost (XGB), and LightGBM (LGBM) are employed in the proposed hybrid method. The merits of this hybridization lie in the fact that the ensemble model is capable of handling volatile features such as nonlinearity, noncyclicity, and market interrelationships existing in oil price time series; incorporating the strengths of the three single models; not easily overfitting; and finally achieving a better performance. The results reveal that the hybrid method achieves the highest Directional Accuracy (DA) and the lowest errors (0.9612, 13.7417, and 0.0368 of DA, MAE, and MAPE, respectively) compared with the results of three other methods-the RF (0.9569, 14.2699, and 0.0385, respectively), XGB (0.9526, 14.5111, and 0.0387, respectively), and the LGBM (0.9526, 14.4022, and 0.0384, respectively)-in experiments on real datasets.

This study investigates the long- and short-run effects of crude oil price (COP) and economic policy uncertainty (EPU) on China’s green bond index (GBI) using the quantile autoregressive distributed lag model. The empirical results show that COP and EPU produce a significant positive and negative influence on GBI in the long-run across most quantiles, respectively, but their short-run counterparts are opposite direction and only significant in higher quantiles. Thus, major contributions are made accordingly and shown in the following aspects. The findings emphasise the importance of understanding how COP and EPU affect China’s green bond market for the first time. In addition, both the long- and short-run effects are captured, but long-run shocks primarily drive the green bond market. Finally, time- and quantile-varying analyses are adopted to explain the nexus between COP and EPU to GBI, which considers not only different states of the bond market but also events that occur in different time periods. Some detailed policies, such as a unified and effective green bond market, an early warning mechanism of oil price fluctuation, and prudent economic policy adjustments, are beneficial for stabilising the green finance market. [ABSTRACT FROM AUTHOR]

We use a semiparametric GARCH-in-Mean copula model to examine the price evolution and volatility dynamics of crude oil, natural gas, and hydrocarbon gas liquids markets using data from January 2002 to December 2021. We find that uncertainty has a positive and statistically significant effect on the returns of crude oil and natural gas, but has a negative and statistically significant effect on ethane returns. We also find that the Frank copula is the best copula to describe the (bivariate) dependence structures between the crude oil, natural gas, and hydrocarbon gas liquids markets, except for the relationship between ethane and butane where the Clayton copula is the most fitted copula. It suggests that weak lower and upper tail dependence exists between the energy returns, and there is statistically significant lower tail dependence between ethane and butane. In other words, extremely low crude oil prices are associated with low prices of natural gas and hydrocarbon gas liquids, and vice versa. When ethane returns go down, there is excess comovement in the returns of butane. Moreover, the tail dependence is strongest between crude oil and natural gas. [ABSTRACT FROM AUTHOR]

US shale oil has created dramatic changes in international oil markets. The United States became the world's biggest producer of crude oil after it overtook Saudi Arabia and Russia and then returned to exporting crude oil after a stop that lasted more than four decades. Its imports of crude oil decreased significantly. US shale oil had a remarkable impact on the structure of international crude oil trade; Other producers, including OPEC, were affected by the decline in their shares in the global oil market and the decline in their financial revenues. Technological development has played an essential role in the success of US shale oil by reducing costs and increasing economic feasibility. Therefore, US shale oil is no longer highly sensitive to lower oil prices, as it was before. Furthermore, the continuous increase in crude oil prices from $24.4 per barrel in 2001 to $97 and $111.6 in 2008 and 2012, respectively, was enough to make shale oil production economically profitable. Now, the United States plays an essential role in the global oil market, as the largest consumer and producer and, behind China, the second largest importer. Shale oil is expected to play a growing role in the US oil sector and the global oil industry in the future.

Crude oil prices, weather changes, and carbon prices are closely related, but little research investigates their dynamic relationship. Studying how crude oil prices and weather changes dynamically affect carbon prices is significant to investors and producers. Taking carbon prices in Hubei and Guangdong as examples, we use the time-varying parameter vector autoregressive (TVP-VAR) model to investigate the time-varying influence of WTI and weather changes (average temperature, sunshine duration, and precipitation) on China's carbon prices. Our empirical results show that: firstly, the impact of crude oil prices and weather changes on carbon prices is obviously time-varying and lagging. Secondly, the short-term impact of crude oil prices and weather changes on carbon prices is higher than the long-term impact. And crude oil price mainly positively affects carbon prices in the short term. Finally, due to the differences in industrial development and weather conditions between Hubei and Guangdong, there are certain regional differences in the impact of crude oil prices and weather changes on carbon prices. Based on these research results, some suggestions are provided for global sustainable development and green transformation of enterprises.

As a key input factor in industrial production, the price volatility of crude oil often brings about economic volatility, so forecasting crude oil price has always been a pivotal issue in economics. In our study, we constructed an LSTM (short for Long Short-Term Memory neural network) model to conduct this forecasting based on data from February 1986 to May 2021. An ANN (short for Artificial Neural Network) model and a typical ARIMA (short for Autoregressive Integrated Moving Average) model are taken as the comparable models. The results show that, first, the LSTM model has strong generalization ability, with stable applicability in forecasting crude oil prices with different timescales. Second, as compared to other models, the LSTM model generally has higher forecasting accuracy for crude oil prices with different timescales. Third, an LSTM model-derived shorter forecast price timescale corresponds to a lower forecasting accuracy. Therefore, given a longer forecast crude oil price timescale, other factors may need to be included in the model.