Crude oil price volatility and environmental performance.

We study the relationship between crude oil price volatility and corporate environmental performance. Using an extensive dataset from 32 countries consisting of 18,464 firm-year observations, we provide strong evidence that oil price volatility significantly increases firms' environmental performance. Our main inference is robust when using alternative measures of oil price volatility and environmental performance, alternative econometric specifications and samples, and several approaches to control for endogeneity. In a set of additional analyses, we first conduct a difference-in-differences analysis that exploits the Arab Spring as an exogenous oil price volatility increase and document a stronger relationship between oil price volatility and environmental performance in the aftermath of the Arab Spring. We second identify (i) capital expenditures and (ii) alternative energy importation as two mechanisms through which oil price volatility influences environmental performance. We finally show that national culture plays a significant role in moderating the relationship between oil price volatility and environmental performance. Taken together, our empirical findings highlight the role of economic uncertainty in affecting firms' environmental performance and provide significant contributions to management and policymakers.

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.

Forecasting crude oil prices in the COVID-19 era: Can machine learn better?

Since the onset of the COVID-19 pandemic, energy price predictability has worsened. We evaluate the effectiveness of the two machine learning methods of shrinkage and combination on the spot prices of crude oil before and during the COVID-19 epidemic. The results demonstrated that COVID-19 increased economic uncertainty and diminished the predictive capacity of numerous models. Shrinkage methods have always been regarded as having an excellent out-of-sample forecast performance. However, during the COVID period, the combination methods provide more accurate information than the shrinkage methods. The reason is that the outbreak of the epidemic has altered the correlation between specific predictors and crude oil prices, and shrinkage methods are incapable of identifying this change, resulting in the loss of information.

Insights from Adding Transportation Sector Detail into an Economy-Wide Model: The Case of the ADAGE CGE Model.

Computable general equilibrium (CGE) models provide valuable insights into economy-wide impacts of anticipated future structural changes in the transportation sector, yet few CGE models offer detailed transportation representations. We use an enhanced Applied Dynamic Analysis of the Global Economy (ADAGE) CGE model to incorporate disaggregated transportation modes and technologies in on-road passenger and freight transportation. We assess the impacts of these inclusions on U.S. transportation patterns, energy consumption and greenhouse gas emissions. Simulating illustrative global oil price cases with and without transportation detail, we find subsector mode disaggregation and technology additions in a CGE model significantly alter the impacts of oil prices on global trade and freight patterns, energy consumption, and greenhouse gas (GHG) emissions. We find that: (1) alternative technologies are essential for capturing transportation sector impacts, (2) electrification may reduce emissions with electricity decarbonization, and (3) higher oil prices may hasten electrification.

A New Forecasting Approach for Oil Price Using the Recursive Decomposition-Reconstruction-Ensemble Method with Complexity Traits.

The subject of oil price forecasting has obtained an incredible amount of interest from academics and policymakers in recent years due to the widespread impact that it has on various economic fields and markets. Thus, a novel method based on decomposition-reconstruction-ensemble for crude oil price forecasting is proposed. Based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, in this paper we construct a recursive CEEMDAN decomposition-reconstruction-ensemble model considering the complexity traits of crude oil data. In this model, the steps of mode reconstruction, component prediction, and ensemble prediction are driven by complexity traits. For illustration and verification purposes, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data. The empirical result demonstrates that the proposed model has better prediction performance than the benchmark models. Thus, the proposed recursive CEEMDAN decomposition-reconstruction-ensemble model can be an effective tool to forecast oil price in the future.

Revisiting the Dynamic Response of Chinese Price Level to Crude Oil Price Shocks Based on a Network Analysis Method.

Crude oil price shocks have led to a fluctuation in commodity prices through the industrial chain and supply-demand relationships, which can substantially influence a country's economy. In this paper, we propose a transmission model of oil price shocks to Chinese price levels and explore the direct and indirect impacts of crude oil price shocks on various Chinese price indices, combining the Granger causality test, impulse response function, and network analysis method. The empirical data are the Brent, WTI, Dubai, and Daqing spot crude oil prices and eight categories of Chinese price indices from January 2011 to March 2020. We found the following results: (1) Consumer price index (CPI) and the price index for means of agricultural production (MAPPI) cannot be directly impacted by crude oil price fluctuations, while they could be indirectly affected. (2) The duration and degree of the impacts of oil prices on each price index vary, and the export price index (EPI) is the most significantly affected. (3) The proportion of the indirect impact in the total impact of crude oil price shocks ranges from 0.03% to 100.00%. Thus, indirect influence cannot be ignored when analyzing the influence of crude oil price fluctuation on Chinese price level.

The effects of the oil price and temperature on food inflation in Latin America.

The impacts on food prices of temperature, the oil price, the exchange rate and wages in the agricultural industry were examined via a structural vector autoregression model and panel Granger causality test, using monthly data between January 2003 and December 2020 for Latin American countries. The paper concerns how much the determinants affect food prices. Empirical findings show that the oil price and temperature can be significant factors for reducing food inflation. According to the result of variance decomposition, in general, a considerable part of food inflation was explained by the exchange rate, but its effect did not show any significant change in the long term. The impacts of the oil price and temperature were limited in the early months, but they created larger changes over time. Impulse response function and the Granger causality test also indicated that exchange rate was a crucial dynamic in explaining food inflation in all countries except Ecuador. This country successfully mitigated the negative effect of the exchange rate, but the oil price and temperature had an impact on food inflation. All results indicate that both monetary and fiscal policies are essential to control food prices. These countries can accomplish this by conventional policies or by radical institutional changes. Nevertheless, the oil price and temperature are external dynamics, and crucial in creating alternative policies to control food inflation.

Does a sanitary crisis drive oil prices and carbon emissions in the USA? Evidence from VECM modeling.

This study explores the interdependence among a sanitary crisis, environmental degradation, oil prices, and economic activity in the USA based on weekly data over the period from January 03, 2020, to October 02, 2020, through VECM and Granger causality methods. The study period is characterized by lockdowns and mobility restrictions due to COVID-19 pandemic that may affect the economic and energy sector in the USA. Thus, a meticulous analysis of the impact of a sanitary crisis on economic and energy sectors seems to be crucial. Findings are very interesting and confirm the existence of a significant impact of a COVID-19 pandemic on WTI oil price. More importantly, bidirectional causal relations between the three couples: COVID-19 infections-carbon emission, COVID-19 infections-economic growth, and COVID-19 infections-oil price are also discovered. Taken together, our empirical findings are effective for the relevant authorities and policymakers in the USA to develop an appropriate financial and fiscal policy such as reducing interest rates, subsidizing, promoting sustainable industrialization, and carbon taxation to boost investment and to recover the economic growth without harming the environment and complicating the sanitary situation.

Impact of global health crisis and oil price shocks on stock markets in the GCC.

This study examines the impact of global COVID-19 cases and oil price shocks on the stock markets in the GCC. Using the Kalman filter to generate the unexpected oil price shocks, we find that, with the exception of Oman, the GCC markets responded to positive and negative oil price shocks before and during the pandemic, with impacts of higher magnitude since March 11, 2020. We also find that the spread of global COVID-19 cases had in itself no meaningful impact on the GCC stock markets.

Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling.

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.

The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China.

In 2020, the world experienced several significant events, including the COVID-19 pandemic and the collapse of international crude oil prices. Both have a great impact on a sustainable economy. Taking China as an example, we use a computable general equilibrium model with multi-sectors and multi-households and consider six different scenarios to simulate and evaluate the aggregate impacts of the pandemic and crude oil prices. We divide the impact of the pandemic into the changes of factor input and the changes of consumer preference and find that the decline of factor input is the leading cause of the economic downturn. The sharp drop in crude oil prices has a significant negative impact on the low-carbon economy. Although the pandemic has led to a decline in global carbon emissions, it is only because of the economic downturn. The epidemic situation and the change of oil price have double impacts on the economy, especially the sustainable economy. Adjusting the price gap between fossil energy and renewable energy (e.g., more stringent carbon pricing) and appropriate tax cuts on residents may be effective ways to alleviate the impact, which should be one of the environmental policies in the post-COVID-19 era.

The Russia-Saudi Arabia oil price war during the COVID-19 pandemic.

The COVID-19 pandemic damaged crude oil markets and amplified the consequences of uncertainty stemming from the Russia-Saudi Arabia oil price war in March-April of 2020. We investigate the impacts of the oil price war on global crude oil markets. By doing so, we use the daily futures and spot prices in three major crude oil markets - West Texas Intermediate, European Brent, and Oman - to perform a systematic analysis of the impacts of the oil price war on them. The event study method, a well-established analytical tool to measure the impacts of a given event on markets, is used in this study. The results indicate that information leakage plays an important role in the impacts of the price war. The outbreak of and truce following the price war have asymmetrical impacts on the markets; negative impacts generated by information leakage during the outbreak are generally more durable than the positive ones it generated during the truce. Furthermore, the magnitude of the impacts on futures markets is negatively correlated with the time-to-maturity of futures. Finally, negative crude oil prices affect West Texas Intermediate crude oil markets the most. Our findings generally show that market participants could perceive and assimilate market changes and adjust their expectations, which restrained the impacts that should have occurred within the oil price war.

The effects of trade openness on decoupling carbon emissions from economic growth - Evidence from 182 countries.

The current rise of protectionism has become the main uncertainty associated with global energy, economy, and the environment. Furthermore, the decoupling carbon emissions from economic growth is crucial for implementing Intended Nationally Determined Contributions (INDCs). These INDCs would be discounted if decreasing carbon emissions would require sacrificing economic growth. This study explored the effect of protectionism (by measuring trade openness based on available data) on the decoupling carbon emissions from economic growth. For this, the heterogenous effects of trade openness on carbon emissions were investigated using in data of 182 countries from 1990 to 2015. The results show that trade openness decreased carbon emissions in high-income and upper-middle-income countries, while having no significant impact on carbon emissions of lower-middle-income countries; even worse, for low-income countries, trade openness increased carbon emissions. The heterogeneous effects of trade openness on carbon emissions indicate that trade openness positively impacts the decoupling economic growth from carbon emission in rich countries, but negatively impacts poor countries. In addition, increasing individual incomes and population distort the decoupling economic growth from carbon emissions. Renewable energy and high oil prices contributed to the decoupling economic growth from carbon emissions. These effects are similar in all countries. Targeted policy implications are presented that enable the decoupling economic growth from carbon emissions for countries with different income levels.

COVID-19 and oil price risk exposure.

This study investigates oil price risk exposure of financial and non-financial industries around the world during the COVID-19 pandemic. The empirical results show that oil supply industries benefit from positive shocks to oil price risk in general, whereas oil user industries and financial industries react negatively to positive oil price shocks. The COVID-19 outbreak appears to moderate the oil price risk exposure of both financial and non-financial industries. This brings important implications in risk management of energy risk during the pandemic.

Sugar Futures as an Investment Alternative During Market Turmoil: Case Study of 2008 and 2020 Market Drop.

In times of turbulent financial markets, investors all around the globe seek for opportunities protecting their portfolios from devastating losses. Historically, commodities were regarded as a safe haven providing sound returns which offset potential losses arising from dropping equity prices in times of market turmoil. While sugar would have provided a proper hedge against crashing equity markets during the initiation of the 2007 bear market and the onset financial crisis, sugar prices dropped likewise equity during the outbreak of COVID-19 and the consequent market shock. The goal of the paper is to elaborate on the differences in sugar price dynamics during the aforementioned economic disruptions by employing a multiple linear regression approach using data from the last quarter 2007 as well as the first quarter of 2019. The findings suggest that the behavioral differences stem from the deep link between oil and sugar prices. While oil did not influence the price of sugar during the outbreak of the financial crisis, it had tremendous influence on sugar prices during the outbreak of the corona crisis. Currently, sugar provides a substantial upside for an investor's portfolio since the demand and supply-side shock on oil prices due to corona crisis as well as the Saudi-Russian oil price war drove oil prices and consequently sugar prices to a historic low. Sugar futures provide the advantage of offering a smaller contract size compared to oil futures, and even though both commodities trade in contango as of March 2020, the sugar future curve is by far not as steep as the oils. Resultingly, investors benefit from lower rollover costs while prospering from a potential surge in oil prices.

Financial implications of fourth industrial revolution: Can bitcoin improve prospects of energy investment?

Bitcoin and the blockchain technology on which it is based are the key drivers behind the accelerated pace of Fourth Industrial Revolution in the domain of Finance. The offshoots of this technology however are not limited and are rapidly spreading in other domains such as oil market. This paper investigates the causality or influences that both markets, Bitcoin price (BP) and oil price (OP) have on each other by applying the bootstrap Granger causal relationship tests considering full as well as sub-samples. Our analysis reveals that shocks originated in OP and transmitted towards BP can be both positive or negative. The positive impact indicates that Bitcoin can be viewed as an asset helpful in avoiding the risks of the high OP, which also indicates that Bitcoin and oil are in the same boat, however, the negative effects cannot support this view. The negative influence of OP on BP can be explained by the burst of the Bitcoin bubble which has weakened its hedging ability. In turn, there is also a negative influence or reverse causality running from BP to OP, highlighting that the demand for oil by investors can be threatened by the increasing BP. Keeping in view the more integrated and complexed financial dynamics which are the results of Fourth Industrial Revolution, investors can benefit from this interrelationship to diversify the risks and optimize their investment by building a more balanced portfolio. Also, governments could promote and protect the healthy development of the Bitcoin and energy market by preventing the Bitcoin bubbles and understanding the reasons of oil price volatility.

Forecasting and unveiling the impeded factors of total export of Bangladesh using nonlinear autoregressive distributed lag and machine learning algorithms.

Rising global oil prices are a major challenge for an emerging oil-importing nation such as Bangladesh. The majority of prior research on the economic effects of an oil price shock has concentrated on developed countries, with emerging economies receiving comparatively less attention. Bangladesh is vulnerable to price shocks due to its rising oil consumption over the past decade. This study aims to investigate how changes in oil prices would affect Bangladesh's total export earnings and to forecast the overall export volume. This study utilized a nonlinear autoregressive distributed lag (NARDL) approach to account for the asymmetric behavior of oil prices from 1991 to 2021. To assess the accuracy of predictions, the study employed the Prophet forecasting model and the Long Short-Term Memory (LSTM) method. Additionally, the symmetry test revealed a nonlinear relationship between export volume and oil price but a linear relationship between inflation and export volume. According to the NARDL assessment, both positive and negative oil shocks increase export earnings over the long run. The short run summary clarifies that both positive and negative changes in oil prices exert a significant negative effect on exports. Also, Inflation influences export earnings negatively in the short run but positively over the long term. Moreover, using machine learning methods, it was found that the LSTM method outperforms the prophet model in prediction performance with a low root mean square error (RMSE) of 1.88. Also, the analysis revealed policymakers that the export sector requires diversification to reduce its exposure to oil price shocks.

Does oil price volatility influences carbon emission trends and financial concerns of oil industry?

This analysis explores the complicated relationship between oil price fluctuations, the oil industry's finances, and the resulting increase or decrease in carbon emissions. Oil price changes have far-reaching effects on the global economy because of its dependence on fossil fuels; therefore, understanding these patterns is essential for effective policymaking and long-term energy planning. The study uses a dataset built from secondary data collected in China over 15 years, starting in 2008 and ending in 2022. This information comes from a wide range of authoritative places, including public records, trade journals, university studies, and the records of international organizations, and provides a solid foundation for study. Oil prices on a global and national scale, oil sector financial performance indicators (such as revenues, earnings, and investment levels), and carbon emission statistics are all significant factors under investigation. As one of the world's largest oil consumers, China has been singled out in this study to allow for a more comprehensive analysis of reactions within this crucial subset of the energy industry. To understand the complex interplay between oil price shocks, the financial dynamics of the oil sector, and carbon emissions, the research utilizes statistical and econometric methods, most notably time-series analysis and regression models. The results are meant to shed light on how oil price shocks consistently affect the monetary aspects of the oil business and, by extension, the patterns in carbon emissions. This study helps us understand these vital interrelationships more completely and nuancedly.

Dynamic spillover effects between EU economic sanctions against Russia, oil prices, and share prices of energy companies in third countries: evidence from China and the USA.

This study establishes a comprehensive suite of sanction indices and employs the time-varying vector autoregressive dynamic spillover index (TVP-VAR-DY) model, to examine the spillover effects of EU economic sanctions against Russia on oil prices and share prices of third-country energy companies, as well as takes China and the USA as examples for analysis. The findings indicate that sanctions targeting the energy sector are the primary drivers of volatility in oil prices and energy company stock prices. The impact on Chinese energy firms' stock prices is more pronounced, while the effects on their American counterparts are more enduring. The indirect impact of EU sanctions on Russia on China is greater than that of the USA. Both direct and indirect sanctions exhibit comparable spillover effects on oil and stock prices. Direct sanctions have better explanatory power for stock price fluctuations, while indirect sanctions have better explanatory power for oil price fluctuations.

Impact of oil price volatility and economic policy uncertainty on business investment - Insights from the energy sector.

This research employs a worldwide sample of 4017 energy sector companies from 1996 to 2022 to examine the effects of economic policy uncertainty (EPU) and oil price uncertainty (OPU) on corporate investment in oil/energy sector and this study analyze how market instability and international economic disasters shape the connection between OPU and business assets. GLM regression with firms-years fixed effects and firm-based clustering indicate that both OPU and EPU had a detrimental influence on corporate investment in energy sector. Generalized linear models provide a universal method for addressing various response modeling issues. It is also revealed that oil-producing nations experience OPU and EPU's negative effects more severely than oil-consuming nations. This paper also demonstrates that the link between corporate investment, OPU and EPU is influenced by nations that produce oil, market volatility, and global financial crises. Strong evidence was found supporting the notion that OPU and EPU had a statistically significant detrimental impact on business assets. The findings of the paper are consistent under a variety of robustness tests and show that the association between OPU and EPU and business assets still holds. The results have significant bearing on the asset strategies that company managers and governments should adopt in light of the volatility of oil prices and EPU and this study provide valuable insights for policymakers who are focused on achieving energy transition, enhancing energy security, and meeting environmental goals such as reducing greenhouse gas emissions.