Future Oil Research Articles

ESG, clean energy, and petroleum futures markets: Asymmetric return connectedness and hedging effectiveness

This study investigates the asymmetric return connectedness and portfolio implications among environmental, social and governance (ESG) exchange-traded funds (ETFs), clean energy ETFs, and five petroleum futures markets from December 2016 to December 2022. To this end, it uses an asymmetric time-varying approach of connectedness and constructs portfolios using minimum variance portfolio (MVP), minimum correlation portfolio (MCP), and minimum connectedness portfolio (MCoP). The results show that negative return connectedness is stronger than positive ones across most of the sample period, especially around the peak of the COVID-19 pandemic. The dynamic of net connectedness between markets is altered by the pandemic. ESG ETFs are more connected to petroleum futures markets than clean energy ETFs. The connectedness is strong during the COVID-19 outbreak, which suggests the instability of the system of return connectedness during the global health crisis. The minimum connectedness portfolio approach reflects the asymmetric and non-asymmetric return connectedness more than other portfolio approaches. Notably, clean energy and ESG ETF exhibit a safe-haven property under both symmetry and asymmetry in return connectedness. The findings are insightful from investment and policy perspectives.

Evaluating the contribution of methanotrophy kinetics to uncertainty in the soil methane sink

The oxidation of atmospheric methane by soil microbes is an important natural sink for a potent greenhouse gas. However, estimates of the current and future soil methane sink are highly uncertain. Here we assessed the extent to which methanotrophy enzyme kinetics contribute to uncertainty in projections of the soil methane sink. We generated a comprehensive compilation of methanotrophy kinetic data from modern environments and assessed the patterns in kinetic parameters present in natural samples. Our compiled data enabled us to quantify the global soil methane sink through two idealized calculations comparing first-order and Michaelis–Menten models of kinetics. We show that these two kinetic models diverge only under high atmospheric CH4 scenarios, where first-order rate constants slightly overestimate the soil methane sink size, but produce similar predictions at modern atmospheric concentrations. Our compilation also shows that the kinetics of methanotrophy in natural soil samples is highly variable—both the V max (oxidation rate at saturation) and KM (half-saturation constant) in natural samples span over six orders of magnitude. However, accounting for the correlation we observe between V max and KM reduces the range of calculated uptake rates by as much as 96%. Additionally, our results indicate that variation in enzyme kinetics introduces a similar magnitude of variation in the calculated soil methane sink as temperature sensitivity. Systematic sampling of methanotroph kinetic parameters at multiple spatial scales should therefore be a key objective for closing the budget on the global soil methane sink.

What Insights Do Short-Maturity (7DTE) Return Predictive Regressions Offer about Risk Preferences in the Oil Market?

In this study, we investigate the ability of three higher-order risk-neutral return cumulants to predict short maturity (weekly) returns of oil futures. Our data includes weekly West Texas Crude Oil futures options that expire in 7 days (7DTE). Using a model-free approach, we estimate these risk-neutral return cumulants at the beginning of each options expiration cycle. Our results suggest that the third risk-neutral return cumulant consistently predicts the returns of various oil futures (including WTI, Brent, Dubai, Heating Oil, and RBOB Gasoline). We compare our findings with 14 other predictors and offer a theoretical explanation for the negative coefficient observed for the 7DTE third risk-neutral return cumulant. Our theory connects higher-order risk-neutral return cumulants with the risk premiums of oil futures. Furthermore, our quantitative investment strategy favors the predictability of oil futures returns.

Analysis of Hedging Strategies in the Crude Oil Futures Market

This paper introduces how companies use crude oil futures to hedge their risk and analysis the advantages and drawbacks of using crude oil futures. In the first section, we briefly introduce the crude oil market and crude oil futures. Besides, we explain some basic definitions of hedging like short and long position and basic risk. Then we introduce how companies use crude oil futures to hedge. In the second section, we analyze several cases and summarize the features of crude oil futures. First, we focus on three cases: Jet fuel cross-hedging, Metallgesellschaft AG, and Sinopec and introduce their backgrounds and how these companies operated and the functions of crude oil futures in details. Then we analyze the changes of crude oil futures market at that time and figure out why these companies had have used the crude oil futures that eventually have failed to hedge and then summarize the features of crude oil and crude oil futures. In the third section, we conclude that the crude oil futures provide advantages, such as active market and the potential to protect profit margins. However, they also face some problems like a high leverage ratio, price fluctuation, Etcetera.

The Price Trend of Polypropylene

Since the polypropylene (PP) is one of the most widely used plastics, the futures prices of polypropylene are of great significance for studying the Chinese plastic market and related investors. This paper analyzes the factors that influence the changes in PP futures prices during and before the pandemic period by examining the correlation between upstream raw materials, downstream product markets, and PP futures prices. The results show that the futures prices of the raw materials are positively correlated with PP futures prices. Additionally, during the COVID-19 pandemic, due to the reduction in domestic PP demand in China, blocked crude oil imports, and refinery closures caused by control measures, the impact of crude oil futures prices on PP futures prices is relatively diminished. Furthermore, the increased demand for plastic packaging during the pandemic led to abnormal fluctuations in the packaging stock index, making it less valuable for analyzing PP futures prices. Finally, a regression analysis is conducted to establish a generalized predictive model for PP futures prices during non-pandemic periods, which is 9.91469 * crude oil futures prices + (-1.22343) * packaging stock index + 3.96203 * textile stock index + (-0.26201) * medical devices stock index. Its reliability is tested using data tests, residual vs. fitted analysis, and K-fold cross-validation, among other methods.

Seeing is believing: Forecasting crude oil price trend from the perspective of images

AbstractIn this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image‐based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low‐frequency models for high‐frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends.

A combined model using secondary decomposition for crude oil futures price and volatility forecasting: Analysis based on comparison and ablation experiments

To accurately forecast crude oil futures price and volatility, this article presents a novel deep learning combined model using secondary decomposition with West Texas Intermediate crude oil futures (WTI) and North Sea Brent crude oil futures (Brent) as examples. Firstly, a trend subsequence and several noise subsequences are obtained by decomposing the crude oil futures price or volatility using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the secondary decomposition is performed on the highest frequency noise subsequence using the variational mode decomposition (VMD). Secondly, the remaining subsequences obtained from CEEMDAN and the subsequences obtained from VMD are predicted separately using the BiGRU-Attention-CNN model. Finally, the predicted crude oil futures price or volatility is calculated by linearly integrating the predicted values of each subsequence. The empirical analysis shows that the novel combined model using secondary decomposition proposed in this paper has the best forecasting performance among many models, both in the comparison experiments and in the ablation experiments. The model is also shown to have good robustness by predicting the volatility at different maturities, varying the ratio of the training set and the test set for crude oil futures price prediction, and predicting the price of crude oil futures after extreme events. Overall, the novel combined forecasting model using secondary decomposition proposed in this paper can help countries grasp the direction of the crude oil market and improve national economic and political security.

Forecasting oil futures returns with news

This paper aims to explore the extent to which text data contains valuable information for predicting oil futures returns. A novel mixed-frequency data sampling random forest regression (MIDAS-RF) approach is proposed to construct a textual indicator. This approach can extract nonlinearity and interaction information from news and allows us to better handle the mixed-frequency and high-dimensional data. Comparing it with traditional sentiment variables and financial factors, our indicator demonstrates better forecasting performance both statistically and economically, with a monthly out-of-sample R2 of 5.26% and an annualized certainty equivalent return gain of 3.08%, respectively. Further evidence suggests that the predictability of the textual indicator is primarily driven by words related to capital markets and macroeconomic topics.

An updated checklist of serpentinophytes for research and conservation in ultramafic ecosystems on the southern Iberian Peninsula (Spain)

AbstractAlthough checklists are essential tools for managing and conserving ultramafic ecosystems, no updated checklist currently exists for ultramafic ecosystems on the southern Iberian Peninsula. Thus, the objectives of our study were (1) to create an updated checklist of serpentinophytes on southern Iberian Peninsula, (2) to determine whether the distribution of serpentinophytes is associated with certain specific types of ultramafic rocks, and (3) to calculate the abundance and richness of serpentinophytes per outcrop to guide conservation efforts. Following a methodology involving field work and searches of bibliographies and herbaria we produced an updated checklist containing 28 serpentinophytes (i.e., 23 obligates, one preferential taxon, one sub‐serpentinophyte, and three regional serpentinophytes). The serpentinophytes showed different petrological affinity, where harzburgite–lherzolite and harzburgite–pyroxenite–dunite exhibited higher occupancy, possibly due to their mineralogical and chemical composition (e.g., containing heavy metals) and/or the larger surface area of those outcrops. We also observed that the occupancy of 21 species was higher in different petrographic entities, the reasons for which could be elucidated by future soil analyses. The highest richness of serpentinophytes was found in the main outcrop of Bermeja, followed by smaller outcrops of Alpujata, Aguas, and Guadalhorce Valley. Although the richness of Aguas resembled that of Alpujata, a notable difference emerged in some of its areas owing to bioclimatic and biogeographic isolation. Given the exclusive presence of serpentinophyte flora on the southern Iberian Peninsula, all southern Iberian outcrops should receive some form of conservation as protected natural spaces.

Technical support as a catalyst for innovation and special project success in oil and gas

In the dynamic landscape of the oil and gas industry, technical support emerges as a pivotal force driving innovation and ensuring the success of special projects. This paper explores the multifaceted role of technical support in this sector, shedding light on its significance in maintaining operational efficiency, safety, and fostering innovation. Amidst the ever-evolving challenges faced by the industry, from fluctuating market demands to stringent regulatory requirements, technical support stands as a cornerstone in navigating complexities and driving progress. Drawing from real-world examples and industry insights, the paper elucidates the challenges and opportunities encountered in fostering innovation within the oil and gas sector. It highlights the critical role of technical support in identifying technological gaps, spearheading research and development initiatives, and facilitating the implementation of innovative solutions. Moreover, the paper delves into the realm of special projects, exploring their importance in driving strategic growth and competitive advantage. It examines the challenges inherent in executing special projects and underscores the indispensable role of technical support in mitigating risks and ensuring successful outcomes. Through a comprehensive analysis of best practices and case studies, the paper elucidates strategies for leveraging technical support as a catalyst for innovation and special project success in oil and gas. It emphasizes the importance of integrating technical support into innovation initiatives, fostering collaboration across multidisciplinary teams, and investing in advanced technologies and expertise. Ultimately, the paper underscores the transformative potential of technical support in shaping the future of oil and gas operations, driving sustainable growth, and fostering resilience in an ever-changing landscape. Keywords: Technical Support, Catalyst for Innovation, Special Project Success, Oil and Gas.

Volatility spillover between China's crude oil futures and sectoral stock markets from a frequency dynamics perspective

Volatility spillover between China's crude oil futures and sectoral stock markets from a frequency dynamics perspective

Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes.

Understanding Intraday Oil Price Dynamics during the COVID-19 Pandemic: New Evidence from Oil and Stock Investor Sentiments

This study employed intraday stock market and oil investor sentiment data related to news and social media (i.e., the Thomson Reuters MarketPsych Indices [TRMI] sentiment index) to gauge investors’ interest in the West Texas Intermediate (WTI) crude oil futures market during the recent health crisis. We proposed an original nonlinear empirical framework by considering oil price dynamics’ complexity and its potential interaction with investor sentiment. The analysis revealed three noteworthy findings. First, we observed evidence of nonlinearity in the relationship between excess returns on WTI crude oil futures and investor sentiment data. Second, the causality direction moved only from oil and stock market investor sentiment to oil returns. Third, the impacts of oil and stock market sentiment data on crude oil returns (i.e., volatility) were always negative. Furthermore, sentiment data related to social media showed a more pronounced cross-correlation than that of news.

Drive Control Technology of Dual Three Phase Permanent Magnet Synchronous Motor for Electric Propulsion Aircraft

Green aviation is one of the effective ways to cope with future oil energy shortages and improve the atmospheric environment. As the executing mechanism of electric propulsion aircraft, the fault tolerance and safety of the electric drive system are the primary considerations. This article uses dual three-phase permanent magnet synchronous motors (PMSM) to drive propellers to provide power for electric propulsion aircraft. Firstly, the working principle of the propellers is analyzed, and a simulation model of the propellers is established using Simulink; Secondly, the space vector control method of dual three-phase PMSM is elaborated, and two different forms of space vector control are compared. At the same time, a vector control model of dual three-phase PMSM is established, and the third harmonic content of dual three-phase PMSM under two different space vectors is compared through simulation. Finally, establish a simulation model of dual three-phase PMSM driving propeller, compare the motor phase current and driving performance of different vector control methods, and analyze the advantages and disadvantages of different vector control methods.

Soil classification based on unsupervised learning using cone penetration test data

Borehole data obtained during geological surveys are the most essential source for understanding soil stratification. It is a prerequisite to know the soil classes up to some depths prior to any construction. However, the direct method to identify the soil classes by drilling boreholes and testing soil samples is costly. A cost-effective alternative is the Cone Penetration Testing (CPT), which is one of the most popular soil investigation methods. This paper explores the intelligent classification of soil layers based on CPT data using three unsupervised machine learning methods: K-means, Gaussian Mixture Process, and BIRCH. The research investigates the classification performance of different models in scenarios with 2 combinations, 3 combinations, 4 combinations, and 5 combinations. The results indicate that the Gaussian Mixture Process method exhibits the best classification performance, followed by the BIRCH method, while K-means performs relatively poorly. Using unsupervised learning for intelligent soil layer classification offers a fast and clear process, but the accuracy still requires further improvement. This study provides a valuable reference for future soil classification studies.

Connectedness among Chinese climate policy uncertainty, exchange rate, Chinese and international crude oil markets: Insights from time and frequency domain analyses of high order moments

This paper investigates the connectedness among the Chinese climate policy uncertainty, CNYUSD exchange rate, the Chinese crude oil and the international crude oil futures markets in terms of average return, volatility and high order moments. Meanwhile, different timescales and frequencies are also studied using Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) approaches. The empirical results show that the connectedness among climate policy uncertainty, exchange rate and crude oil futures markets are time-varying and can be intensified by extreme events. The strength of connectedness depends on the moment order of spillovers. Covid-19 intensified the return, volatility and kurtosis connectedness. However, the impact of Covid-19 on higher order connectedness is relatively weak. In addition, the return spillovers are mainly caused by the short-term effect while higher order moment spillovers are driven by the long-term effect. Brent oil is the largest net shock transmitter and the Chinese crude oil futures mostly is the largest net shock receiver. Exchange rate is a net receiver except for kurtosis spillovers. The role of Chinese climate policy uncertainty is uncertain. In terms of the net pairwise connectedness, Chinese crude oil futures receives most shocks from Brent oil. In oil-exchange rate nexus, Chinese crude oil futures transmits shocks to the exchange rate in high order moment spillovers. The correlation between climate policy uncertainty and Chinese crude oil futures is time-varying. These findings offer useful implications for investors, policymakers and market supervisors.

Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model

This study examines the predictive ability of nine high-frequency jumps on the Chinese crude oil futures volatility using a series of the Heterogeneous Autoregressive (HAR) models. Out-of-sample empirical results indicate that among the nine high-frequency jump tests, the JO jump component is powerful because the prediction model including this component demonstrates superior predictive performance. Compared to other competing models, the model incorporating JO jump component, jump intensity, and Markov-regime achieves higher predictive accuracy. During the outbreak of the COVID-19 pandemic and periods of high volatility, this new model continues to exhibit strong predictive capability for volatility in the Chinese oil futures market. This study provides novel insights into forecasting volatility in the Chinese oil market under the presence of extreme shocks.

Dynamic spillover between crude oil, gold, and Chinese stock market sectors –analysis of spillovers during financial crisis data during the last two decades

The present study investigates the presence of asymmetric return spillovers among crude oil futures, gold futures, and ten Chinese stock sector markets. Time-varying asymmetric spillovers between commodities and the 10 sectors are shown by utilizing the spillover index developed by Diebold and Yilmaz (2012, 2014). Our findings indicate that the industrial and discretionary consumer sectors generate and benefit the most from spillovers. Furthermore, it has been established that the basic materials sector exhibits a net positive impact on spillovers. In contrast, oil futures, gold futures, and other sectors demonstrate a net negative impact as recipients of spillovers. Moreover, the negative return spillovers outweigh the positive return spillovers. Our analysis spans from 2000 to 2023 to include various financial crises. The spillover effects of asymmetry are impacted by various factors, including the global financial crisis (GFC), the European sovereign debt crisis (ESDC), the decline in oil prices, and the outbreak of the COVID-19 pandemic. Including gold and oil in individual equity markets can benefit equity investors. Furthermore, implementing hedging strategies is susceptible to the global financial crisis, economic slowdown, oil price decline, and the recent COVID-19 pandemic. The oil futures exhibit the greatest hedging effectiveness during the COVID-19 spread. The findings indicate that gold exhibits comparable outcomes solely in the presence of positive spillover effects. At the same time, its performance reaches its peak during the recovery phase in the context of negative spillover effects.

Exploring the relationship between Chinese crude oil futures market efficiency and market micro characteristics

This paper uses daily frequency data spanning from March 26, 2018 to February 3, 2023, and combines the exponential generalized autoregressive conditional heteroskedasticity in mean (EGARCH-M) model with the time-varying parameters state-space (TVPSS) model to dynamically test the efficiency of the Chinese crude oil futures market (CCOFM). Additionally, the paper examines the relationship between market efficiency and market micro characteristics such as market depth, market liquidity, noise trading, speculation rate, and information asymmetry. The results show that the CCOFM exhibited weak-form efficiency in June 2020, which lasted until the end of February 2022, and after that, the efficiency showed a poor development trend. Analyzed from the macro level, the Market Maker System and Trader Suitability System have a positive effect on market efficiency, while the Russian-Ukrainian conflict has a negative effect on market efficiency. Analyzed from the micro level, market depth and information asymmetry have a positive effect on market efficiency, while the speculation rate has a negative effect on market efficiency, and noise trading and market liquidity have no significant effect on market efficiency. Market efficiency has a positive effect on market depth and a negative effect on speculation rate. Market liquidity and noise trading have a positive effect on speculation rate, while both information asymmetry and market liquidity have a negative effect on noise trading. In addition, speculation rate has a weak negative effect on information asymmetry, and market depth and speculation rate have a weak positive effect on market liquidity. This study provides theoretical support and decision-making foundation for the development of CCOFM by dynamically exploring the relationship between CCOFM efficiency and micro characteristics.

Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables

AbstractThis paper aims to study the volatility forecasting of Chinese crude oil futures from the large‐scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO‐PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large‐scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO‐PCA method. The empirical results show that both the OLS and SVR combined with LASSO‐PCA can improve the forecasting accuracy, especially SVR‐LASSO‐PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out‐of‐sample forecasting.