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As the world is currently in turmoil, geopolitical crises and economic policy uncertainties are increasing significantly. This study aims to provide insight into the dynamics of time–frequency spillovers in the domains of crude oil, geopolitical risk, economic policy uncertainty and stock markets. It represents the first investigation analyzing the time-varying frequency connectedness across the aforementioned domains by adopting the time-varying parameter vector autoregression connectedness combined with the time-varying frequency connectedness measurement [Chatziantoniou et al., 2023]. The study covers the period from January 2004 to February 2023, including the 2008 financial crisis, the COVID-19 pandemic and the turmoil caused by the 2022 Russian–Ukrainian conflict. The analysis finds that short-term frequencies dominate return connectedness, indicating a rapid information processing mechanism responsive to short-run shocks. The stock market indices of oil-exporting countries, the US and the UK act as the primary transmitters of return spillovers. Volatility connectedness is driven by long-term frequencies, with Russia, Canada and the UK serving as the primary volatility spillover transmitters. Economic policy uncertainty is primarily influenced by oil-importing countries. Geopolitical risk mostly serves as the spillover receiver from crude oil, while it primarily transmits spillovers to economic policy uncertainty during major events such as terror attacks, conflicts and wars. The 2022 Russian–Ukrainian conflict amplifies spillovers to economic policy uncertainty. Intriguingly, conflicts deepen economic policy uncertainty, and prior to the conflict, stock market volatility had assimilated the influence of geopolitical risk shocks. The study also employs network topology to visualize spillover transmission mechanisms during the 2022 Russian–Ukrainian conflict.

Crude oil prices wield substantial influence over economic stability and sustainability, exerting a profound impact across various sectors and significantly moulding the economic well-being of nations. Thus, precision of predicting crude oil prices is of utmost importance for a wide array of stakeholders, including policymakers, investors, and participants in the energy market. This study offers an empirical exploration of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMA-X) method, employing RMSE and MAPE values for forecasting crude oil prices during the most volatile periods from 2020 to 2023, including both COVID-19 pandemic and Russia Ukraine war period. The results indicate that the SARIMA-X method is effective for predicting crude oil prices during turbulent market conditions. This model can be a valuable tool for investors, traders, and other market participants, enabling them to make informed decisions when it comes to both intraday trading and long-term forecasting of crude oil prices.

As the world’s second-largest crude oil consumer, China depends on imports for approximately 60% and domestic production for approximately 40%, of its oil demand. Therefore, it is very interesting to assess the pass-through effects of both domestic and international crude oil prices to gasoline and diesel prices. After the short- and long-run investigations using the nonlinear autoregressive distributed lag (ARDL) methodology of Shin et al. [Shin, Y, BC Yu and M Greenwood-Nimmo (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework” Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications, R Sickels and W Horrace (eds.), pp. 281–314. Springer.], we find overwhelming evidence supporting the asymmetric price transmission mechanism between crude oil prices and gasoline prices in both the short- and long-run. In the case of diesel prices, on the other hand, the asymmetry effects seem likely to be a long-run phenomenon.

In this paper, we examine the role of Brazil, Russia, India, China and South Africa’s (BRICS) currency in energy market by using vine copula method. The value-at-risk (VaR) and expected shortfall of two portfolios are calculated. One is a benchmark portfolio which is consisted of only energy prices, the other is a portfolio which adding the BRICS’s exchange rate into the benchmark portfolio. The data period is from 24 August 2010 to 29 November 2019. Our results show the BRICS’s currency can reduce the risk in energy investment.

To determine the optical parameters of crude oil and seawater systems, we carried out spectral investigations using the ellipsometry method, which is a highly sensitive and accurate optical method for studying the surfaces and interfaces of various media. This method is based on studying the change in the polarization state of reflected light after its interaction with the surface of interfaces of these media. Crude oil and seawater from different regions of Caspian Sea were accessed by spectroscopic ellipsometry over the 200–1700 nm spectral range at room-temperature. Optical constants and dielectric function were obtained for massive samples of each substance, as well as for ultrathin layers of the oil spilled over the sea surface. Dielectric function, when completely determined in the frequency regions corresponding to electronic transitions and excitation of atomic or molecular vibrations in the object, is a unique dielectric fingerprint of this object. Oils with even miserable difference in type and concentration of biomarkers and heterocomponents will have different dielectric functions. The possibility to use dielectric function as a unique optical fingerprint for oil identification is figured out.

Development and demonstration of a new method for highly accurate forecasting of the hydrocarbon composition of oil based on determining the spectrum of its universal material constant–dielectric function, as by direct measurement using the method of spectroscopic ellipsometry, are today accepted as the world standard for determining the optical functions of any substance in a liquid or solid state, and by its quantum-mechanical calculation from first principles to complete coincidence with the measurement results. A methodology will be proposed for the complete description of any oil and the identification of it belonging to a particular oilfield. The methodology is not only universal and highly accurate, but also economical. In this work, we obtained several groups of fractions of crude oilsamples from different oil fields in Azerbaijan, which were accessed by spectroscopic ellipsometry over the 1.5–6.5 eV spectral range at room-temperature. Optical constants and dielectric function were obtained for massive samples of each substance and fractions. The proposed method is a complete dielectric fingerprint of oils for widespread use, including for environmental monitoring of oil-contaminated areas of the sea and land.

Understanding the phase transition and deposition behavior of crude oil system with waxy is of great significance to ensure the safe production and transportation of oil. In this paper, molecular dynamics simulation is employed to explore the deposition process of crude oil system with heterogeneous waxy on the solid surface. The results show that in a multiphase system, the morphology of paraffin wax crystals will change correspondingly at different system temperatures. At low-temperature, the paraffin molecules are arranged in an orderly manner, which are easier to form wax crystals, resulting in the density of the system that changes greatly. As the temperature increases, the aggregation of the wax molecules decreases, which makes the fluidity increase, and it is not easy to form wax crystals.

In this paper, we explore the impact of COVID-19 on auto-correlations and cross-correlations among NASDAQ stock index of the USA, China iron ore price index (CIOPI), and West Texas Intermediate Crude Oil price (WTI). To find out the effect of COVID-19 on financial markets, we divide the investigated data series into two sub-periods, i.e., pre-COVID19 period and post-COVID19 period. First, multifractal detrended fluctuation analysis (MF-DFA) of those series shows a general trend of strong multifractality after COVID-19, indicating lower market efficiency after the pandemic shock. Second, multifractal detrended cross-correlation analysis (MF-DCCA) method is employed to examine cross-correlations among NASDAQ, CIOPI, and WTI. The three cross-correlations all increase in post-COVID19. The correlation between NASDAQ and CIOPI increases the most, becoming the strongest correlation among the three cross-correlations in post-COVID19. The surrogate procedure shows that the post-COVID19 cross-correlation multifractalities are mostly due to fat-tail distribution. Third, we use multi-scale multifractal analysis (MMA) to visualize the dynamic behaviors of correlations among the series. The Hurst surfaces of the three cross-correlations have more fluctuation, both at small and large scale in post-COVID19 than that of pre-COVID19. Particularly, the Hurst surface of cross-correlation between NASDAQ and CIOPI exhibits stronger multifractality during the outbreak of COVID-19 than that in both pre-COVID19 and post-COVID19. The above investigations provide helpful insights of relevant market trends.

A novel network with Wavelet denoising-GARCHSK and Mixed CoVaR method is proposed to construct full-sample and dynamic networks for investigating the risk spillover effects across international crude oil and Chinese stock sectors before and after the COVID-19 outbreak. The empirical results denote that the total bidirectional oil-sector risk spillover effects increase rapidly after the COVID-19 outbreak. Interestingly, sectors shift from net risk receivers to net risk contributors in the oil-sector risk transfer effects during the pandemic period. Second, unlike the pre-COVID-19 period, Shanghai crude (SC) replaces Brent as the largest oil risk transmitter to stocks during the COVID-19 period. Third, there are notable sectoral features in the oil-sector risk spillovers, which differ across different periods. After the burst, Energy has an incredibly weak connection with crude oil, while the sectors, which oil products are input for, become close with crude oil. Far more surprising is that the petroleum-independent sectors have increasing closer risk transfer effects with crude, even becoming the largest risk contributors to oil, after that. Finally, the oil-sector relationships during the same period are time-varying but stable. This paper provides policymakers and investors with new method and insight into the oil-sector relationships.

The interaction between new energy vehicle (NEV) stock prices and the crude oil market is crucial for resource allocation and risk management. This study employs Multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the multifractal characteristics of the cross-correlation between Tesla stock price (TSLA) and crude oil price (Brent), as well as between TSLA and other NEV stocks (excluding TSLA). The experimental results reveal long-term persistence and multiple fractal characteristics in the cross-correlations. Additionally, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) demonstrates the asymmetry of the cross-correlation during upward or downward trends between TSLA and Brent, as well as between TSLA and other NEV stocks (excluding TSLA). Furthermore, utilizing the transfer entropy (TE) method, we assess the strength and direction of information flows between TSLA and Brent, and between TSLA and other NEV stocks (excluding TSLA). Interestingly, we observe bidirectional information transmission between TSLA and other NEV stocks, while only unidirectional information transmission from NIO to TSLA is evident. These findings provide valuable insights for resource allocation, supply chain management and sustainable development strategies for decision-makers in the NEV market.

The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry. In this paper, a novel approach of the improved tabu search algorithm has been employed to solve a large scale problem in the crude oil refinery industry. This problem involves 44 variables, 36 constraints, and four decision variables which represent four types of crude oil types. The decision variables have been modeled in the form of fuzzy linear programming problem. The vagueness factor in the decision variables is captured by the nonlinear modified S-curve membership function. A recursive improved tabu search has been used to solve this fuzzy optimization problem. Tremendously improved results are obtained for the optimal profit function and optimal solution for four crude oil. The accuracy of constraints satisfaction and the quality of the solutions are achieved successfully.

Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007–2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.

This study contributes to the COVID-19 related literature in finance by examining asymmetric volatility spillover across stock, Bitcoin, gold and oil markets before and during the COVID-19 pandemic. Based on multivariate VAR asymmetric BEKK GARCH model, findings show that the interdependency across the examined markets intensified during the recent health crisis. Moreover, we find that oil market appears as major receivers of volatility spillovers, particularly from gold and stock market which is mostly the results of dramatic collapse of oil prices during the COVID-19 outbreak. We also document that gold exhibits a strong resilience during COVID-19 crisis, suggesting its potential hedging ability during uncertainty. As for asymmetric volatility spillover, findings show the highest sensitivity of oil and Bitcoin markets to gold and US stock markets. Our findings have important implications for investors, portfolio managers and policymakers.

In recent years, increases in fossil fuel consumption, along with associated resource limitations, high prices, and growing concern about climate change, have led to initiatives in favor of expanding renewable energy use. This study addresses several issues. Firstly, we review drivers and obstacles that the biofuel industry faces. Secondly, the current state of the biofuel industry with emphasis on the EU is investigated. Thirdly, the paper quantifies the factors that foster or harm biofuel use by applying dynamic panel econometric techniques (panel GMM). Economic activity (GDP), high fuel prices, greenhouse gas emissions and to a weaker extent some political characteristics are the main drivers. Our findings suggest that biofuel production will experience substantial growth, especially within developed economies primarily due to their environmental and national energy security concerns.

China has been the world’s second largest oil consumer and importer for several years; though many studies have focused on China’s energy security, few have examined it from the perspective of external suppliers. This study provides a group of indicators to assess the reliability of China’s 37 oil-importing sources over the period 1995–2018 based on the perspective of external suppliers. These indicators are made up of four dimensions: energy availability, political status, economic status, and energy transportation. Then, an index was created based on the reliability of the oil-importing sources and their corresponding shares in China’s total oil imports, to evaluate China’s oil import security. The results show that, for China, the members of Commonwealth of Independent States, the Asia-Pacific region, and American countries are more reliable oil suppliers than Europe, Africa, and the Middle East. Besides, energy availability and energy transportation are the main threats to China’s oil import security. Based on these conclusions, this study suggests that China should increase its internal energy supply and control the growth of energy consumption to reduce its dependence on oil imports, diversify its energy importing sources and transportation routes, and implement energy-focused diplomatic policies and activities to enhance its oil import security.

The COVID-19 pandemic raised global warming issues, and particularly the effect of the carbon dioxide (CO₂) emissions. This is a matter of great concern especially in the aftermath of the forced confinement period, that reorganized the dices. Within this context, this chapter handles the relationship between CO₂ emissions, crude oil, and natural gas prices in the time of the critical epidemic. The results show that any decline in the natural gas and lagged crude oil prices downgrades carbon emissions. Equally, as the number of infected people by the disease increases, these emissions tend to decrease. This chapter provides implications for policymakers and governments.

By using the structural VAR model with time-varying coefficients, this chapter aims to assess the impact of COVID-19 shocks on crude oil and natural gas S&P GSCI during the first wave and to compare it to that during the second wave. The findings confirm that S&P GS indices’ responses to COVID-19 shock are varying over time. This variation in responses can be explained by the unstable behavior of investors in an extremely uncertain environment. We find that the energy market remains vulnerable to the surge in coronavirus deaths, during the second wave, even though the damage on oil prices is less than that of the first wave. Our results show that the negative effect on the natural gas index worsens over time and becomes substantial with the arrival of the second wave.

This paper introduces an information-based model for the pricing of storable commodities such as crude oil and natural gas. The model uses the concept of market information about future supply and demand as a basis for valuation. Physical ownership of a commodity is taken to provide a stream of convenience dividends equivalent to a continuous cash flow. The market filtration is assumed to be generated jointly by the following: (i) current and past levels of the dividend rate, and (ii) partial information concerning the future of the dividend flow. The price of a commodity is the expectation under a suitable pricing measure of the totality of the discounted risk-adjusted future convenience dividend, conditional on the information provided by the market filtration. In the situation where the dividend rate is modelled by an Ornstein—Uhlenbeck process, the prices of options on commodities can be derived in closed form. The approach that we present can be applied to other assets that yield potentially negative effective cash flows, such as real estate, factories, refineries, mines, and power generating plants.

Biodegradation experiment was carried out to evaluate the effects supplementation of microbial consortium and bulking agents in biodegradation of crude oil in soil. The soil with indigenous microbes was spiked with crude oil at 50,000 ppm and a cocktail of microbial consortium at ratio 1:1:1:1 (v/w) which consist of Pseudomonas sp. UKMP 14-T, Acinetobactersp. UKMP 12-T and two fungi isolates Trichodermasp. (TriUKMP-1M and TriUKMP-2M). Bulking agents (sugarcane baggasse (SB) and empty fruit bunch (EFB) from oil palm) at 15% and 20% (w/w), respectively were added and mixed thoroughly. The pH and moisture content of the soil was maintained at 6.5 and 40% VWC, respectively. The degradation of crude oil from the soil was analyzed using gas chromatography-flame ionization detector (GC-FID) and the growth of bacteria was estimated using spread plate method. The result showed that biodegradation of crude oil by microbial consortium with addition of SB produced 100% Total Petroleum Hydrocarbon (TPH) degradation as compared to 91% with EFB after 30 days incubation. The control plot which contains only indigenous microbes showed 62% degradation at the same period of incubation. The results indicate that the types of the bulking agent may influence the intake of the nutrient source by microbial consortia hence influenced the percentage of the TPH degradation.

The wear behavior of rubber stator belongs to the typical non-linear problem, because it is affected by multiple factors. In the background of NBR tribology system, a wearing model has been configured in this article based on BP artificial neural, in which hexane, cyclohexane, toluene, and rubber wearing are considered as the input and output factors, respectively. The wear loss is predicted successfully in different kinds of crude oil, and the error is only 3.98% comparing with the experimental results.