Future Oil Research Articles

Nonlinear Risk Spillover Path Between China’s Carbon Market, China’s New Energy Market, and the International Crude Oil Futures Market

Nonlinear Risk Spillover Path Between China’s Carbon Market, China’s New Energy Market, and the International Crude Oil Futures Market

Dynamic linkages and contagion effects: Analyzing the linkages between crude oil prices, US market sector indices and energy markets

This paper aims to assess the dynamic linkages between crude oil futures contracts, renewable energy indices, carbon credit futures indices and several US sector market indices by applying the DECO-GARCH model and the connectedness index of Diebold and Yilmaz (2012). The analysis is conducted on a daily data sample from August 2014 to February 2024 and performed at both the aggregated and disaggregated levels. The disaggregated analysis revealed that the correlation between the variables was lower before periods of stress, such as the COVID-19 crisis and the Russian–Ukrainian conflict as well as a complex correlation structure with a diverse mix of positive and negative values between different pairs of variables. Moreover, the static connectedness results in terms of returns underscore a significant degree of interconnection and transmission of shocks between oil prices and sector markets, renewable energy, and carbon credit futures indices. In addition, the results highlight the responsiveness of the clean energy and carbon credit sectors to global circumstances and economic conditions. The study concludes with a dynamic connectedness analysis, highlighting once again the intricate connections and interactions between all the variables, which are exacerbated during periods of market instability and key events. The net interconnectedness analysis demonstrated that changes in crude oil prices have a significant impact on most of the variables analyzed. The findings of this study have clear implications for a wide range of market participants, policy-makers, and individuals managing portfolios, particularly in terms of diversification opportunities.

Eco-evolutionary strategies for relieving carbon limitation under salt stress differ across microbial clades

With the continuous expansion of saline soils under climate change, understanding the eco-evolutionary tradeoff between the microbial mitigation of carbon limitation and the maintenance of functional traits in saline soils represents a significant knowledge gap in predicting future soil health and ecological function. Through shotgun metagenomic sequencing of coastal soils along a salinity gradient, we show contrasting eco-evolutionary directions of soil bacteria and archaea that manifest in changes to genome size and the functional potential of the soil microbiome. In salt environments with high carbon requirements, bacteria exhibit reduced genome sizes associated with a depletion of metabolic genes, while archaea display larger genomes and enrichment of salt-resistance, metabolic, and carbon-acquisition genes. This suggests that bacteria conserve energy through genome streamlining when facing salt stress, while archaea invest in carbon-acquisition pathways to broaden their resource usage. These findings suggest divergent directions in eco-evolutionary adaptations to soil saline stress amongst microbial clades and serve as a foundation for understanding the response of soil microbiomes to escalating climate change.

The development and utilization of shale oil and gas resources in China and economic analysis of energy security under the background of global energy crisis

The key scientific problem to be solved in this paper is the optimal development and utilization model and the economic evaluation model of China's land-phase shale oil and gas resources, and the purpose of the research is to promote the large-scale commercial development and utilization of China's shale oil and gas resources, and to safeguard China's oil and gas energy security and the sustainable development of the economy. The article proposes to adopt the small surface element volume method (oil content rate method) to evaluate the pure shale oil resources, adopt the Cobb–Douglas production function model as the optimization model to measure the boundary production capacity of shale oil and gas, construct the optimal development and utilization model for shale oil and gas resources considering the five first-level safeguard indexes, namely, science and technology (A), capital (K), talents (L), reserves (S) and ecological environment (E), and establish the basic constraint model for the optimal development and utilization of shale oil and gas resources. The basic constraint model, as well as the evaluation model of economic coefficients for the development and utilization of shale oil and gas resources were established. The pure shale oil resources are mainly calculated based on the movable oil content of shale. In the paper, the S1 of normal pyrolysis (300 °C) is regarded as movable oil, and the sum of S1 and evaporated hydrocarbon (light hydrocarbon) loss is the movable oil content of shale. The integrated geological-physical exploration-engineering comprehensive evaluation of China’s land-phase shale oil-rich and high-yielding “sweet spot” is an important prerequisite for the realization of shale oil and gas resources to build production scale and effective development, and the least-squares method is used to estimate the average production function, the distance to the maximum value of the residuals, and the boundary capacity production function. The average production function and residual maximum distance are estimated by the least squares method, and the production function of the boundary capacity is derived, and the quotient of the boundary capacity and the actual capacity is calculated to get the capacity utilization rate, which can be used to analyze the potential of future shale oil and gas growth. The development of shale oil and gas resources in a target block requires comprehensive consideration of the first-level guarantee indicators such as science and technology (A), capital (K), talents (L), reserves (S) and ecological environment (E), as well as more than 10 s-level indicators and a number of third-level indicators, in order to ensure that the oil companies maximize their profits by organizing the development and production. The economic coefficient can be expressed as the ratio of economically recoverable resources to geological reserves. The larger the economic coefficient for the development and utilization of shale oil and gas resources is, the better the economy of the area is, and the larger the proportion of shale oil and gas resources that can be exploited. There is little special literature on the optimal development and utilization model of shale oil and gas resources and energy security among many research results at home and abroad. The evaluation of pure shale oil using the small surface element volume method (oil content rate method) and the construction of the boundary capacity calculation model, the optimal development of the basic constraints model and the economic evaluation model that we have determined, although they can not yet fully cover all the links and factors related to the development and utilization of shale oil and gas resources, are not yet fully covered by our research work. However, our research work has given the model more geological and economic theoretical connotations, and provided an economic basis and technical reference for the large-scale and commercial development and utilization of shale oil and gas resources as an effective alternative to oil imports.

Functional Volatility Relationship Analysis and Prediction in International Crude Oil Futures Markets

ABSTRACTTo measure intraday volatility in international crude oil futures markets, we use the functional conditional variance to measure volatility and focus on volatility relationship analysis and prediction. This paper analyzes the simultaneous and predictive volatility relationships in crude oil futures markets. For covariate markets with significantly positive predictive volatility relationships, this paper empirically extends the fGARCH‐X model so that it can introduce the volatility characteristics of covariate markets. The empirical application shows that using the fGARCH‐X model can generally improve the predictive effects of functional volatility in crude oil futures markets. The robustness results indicate that the improvement in volatility prediction is significant. This study is beneficial for the stable development of international crude oil futures markets and is valuable for investors' investment decision‐making.

Heavy Metal Pollution in Gas Flare-Impacted Soils: An Assessment Using Pollution Indices in Obunagha, Bayelsa State, Nigeria

Gas flaring is the controlled burning of natural gas which occurs as a result of the activities of oil exploration and exploitation using flare stacks. In Nigeria, gas flaring activities is high and it mainly takes place in the Niger Delta region. The aim of this study was to investigate the bioavailability and concentration of heavy metals pollution of gas flare-impacted soils using pollution indices such as contamination factor, geo-accumulation index and pollution load index. Soil samples were collected with soil auger at depths of 0-15 cm from distances of 200 m, 500 m, 1000 m, 2000 m, and 3000 m away from the flaring point. Flame atomic absorption spectrometry was used to assess the presence of the following heavy metals in the soils: cadmium, chromium, nickel, zinc, copper, and manganese. The findings revealed that Fe (90% bioavailability) had the highest bioavailability among the metals tested in the soils, while Cr (45%) had the lowest. The metals bioavailability decreased in the order of Fe>Mn>Cu>Cd>Ni>Zn>Cr. The geo-accumulation index revealed that the soils were practically uncontaminated by Zn and Cu at all distances, moderately polluted by Cd and Ni at 1000 m and 2000 m and strongly polluted with Cd at a distance of 200 m. The Contamination factor results were consistent with the geo-accumulation index results. The pollutant load index (PLI) was found to be high but decreasing (1.37-0.49) as the distance from the flare point increased, indicating that the research region was polluted. In conclusion, the study's overall findings suggested that the soils were contaminated with heavy metals (particularly Cd) as a result of nearby gas flaring. As a result, the area must be appropriately monitored and managed to prevent future soil contamination by heavy metals to a level that is hazardous to human health.

Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This article proposes a solution to improve irrigation efficiency, which is called DRLIC (deep reinforcement learning for irrigation control). DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances in which the output of our DRL control agent is unsafe, such as irrigating too much or too little. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively conservative action instead. To demonstrate the real-world application of our approach, we develop an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compare the water consumption of DRLIC with a widely used irrigation scheme. Our results indicate that DRLIC outperforms the traditional irrigation method by achieving water savings of up to 9.52%.

Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets

ABSTRACT This research aims to investigate the intricate dynamics between energy futures prices and both tropical and traditional commodities futures prices, with a particular focus on the critical role of oil futures prices in predicting the trajectory of agricultural futures prices. The study employs the Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC-MGARCH) model to analyse the interplay among oil and agricultural futures price series over 2008:01–2021:06. Additionally, it incorporates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to highlight the differences in how energy futures prices predict tropical and traditional agricultural futures prices. The findings reveal numerous connections between oil prices and both tropical and traditional agricultural futures prices, underscoring the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights from this study provide valuable guidance for futures market participants, encouraging them to use these findings to refine and optimize their market strategies, thus enhancing their ability to navigate and capitalize on the complexities of these interconnected markets.

On being an “oil and gas worker”: dominant discourse, self-representation, and Canada’s energy future

ABSTRACT Political wrangling over the future of oil and gas in the context of climate change dominates national debates about Canada’s energy future. These debates frequently rhetorically center around the needs and desires of oil and gas workers. Who, though, do politicians and pundits imagine these workers to be, and how do these imaginations measure up against how these workers imagine themselves and their futures? I answer these questions using the sociology of knowledge approach to discourse analysis to examine media and political debates as well as interviews with oil and gas workers. I find that oil and gas workers’ self-representation is considerably more versatile and adaptable to different energy pathways than media and politicians’ representations of them. I use this finding to argue for disaggregating workers’ agency from the dominant stories told about them. Doing so has the potential to open new pathways for energy transition.

Future Studies of the Oil and Gas Industry: Understanding the Dilemmas of Innovation Management, Governance, and Sustainability

Purpose: The study was to systematically analyze the scientific production over the last decade (2010-2021) in terms of future studies about the oil and gas (O&G) industry. Theoretical Framework: The future of the O&G industry has become the ground for research with various methods to study the finite life of this resource, global climate change, the prospect of low-carbon economies, and the transition of energy to renewable sources. Method: This systematic literature review analyzed scientific production in the last decade about the future of the O&G industry, indicating new perspectives for the field, identifying all the primary research relevant to an issue, and allowing for critical assessment and synthesis of results. Findings: There is an increase in the number of publications on the subject, emphasizing topics such as innovation management and energy consumption. There was a predominance of empirical studies that employed forecasting methods; however, foresight was used for topics such as governance and strategy. Research Implications: Contributing to a synthesis of scientific publications, although the list is not exhaustive, nevertheless provides a starting point for advancing research into the future of the industry. Originality/value: Various contributions and innovation proposals for organizations were identified. This study provides a structured scientific basis for future oil and gas industry studies.

Asymmetric TVP-VAR connectedness between highly traded commodities and hedging strategies: Evidence from major contagions

The objective of this study is to examine the return interconnectedness and asymmetric spillover effects in global commodity futures markets, with a focus on the impact of contagion. A competent asymmetric time-varying parameter vector autoregressive (TVP-VAR) model was employed for highly traded commodity futures (cocoa, coffee, corn, cotton, soy, sugar, wheat, and oil) between January 1, 2000, and March 31, 2024. This study investigates the connectedness of commodities in three dimensions: asymmetric spillovers, the influence of oil and oil substitutes on the network, and the impact of major contagions. The average total connectedness index (TCI) indicates that the connectedness is significant throughout the period and increases during the invasion. The findings imply that contagion effects trigger a potential alteration in the structure of the network integration level of the commodities, amplifying system-wide dynamic connectivity due to disurptions caused by oil and oil substitutes. The net plot depicts corn and soy as the net transmitters, with their magnitude increasing during the contagions. The pairwise connectedness index (PCI) revealed that corn-soy, corn‒wheat, and soy-wheat were the primary interactors, while oil became a significant interactor, particularly during the oil crash and the COVID-19 outbreak. Additionally, compared with other contagions, GFC had a potential asymmetric effect on the network. Positive returns dominate the interaction between the primary transmitter and receivers, whereas negative returns do not significantly dominate the total network. These investigations contribute to the literature on the food-fuel nexus in terms of asymmetries and the impact of contagions on the futures market. It also identified the optimal portfolio allocation based on the hedging effectiveness of three portfolio construction strategies.

Fossil materials demand within deep decarbonization scenarios: A feasibility assessment

Integrated Assessment Models (IAMs) are cornerstone tools in guiding decarbonization policies. However, the petrochemical sector, which is crucial to the future of oil and gas due to its non-energy fossil feedstock demands, has been overlooked in IAMs. This study aims to evaluate the implications of this under-representation on decarbonization strategies. We assessed the Illustrative Mitigation Pathways from the Intergovernmental Panel on Climate Change in its Sixth Assessment Report. Our analysis indicates that, without stringent material policies, the feedstock demand can reach unattainable levels from 2050 onwards. This is evidenced by the required disruptive yields of primary oil in feedstocks, which exceed 80 % in some cases. The findings highlight the necessity for improved modelling of the petrochemical sector within IAMs.

Do OPEC+ policies help predict the oil price: A novel news-based predictor

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.

Hydrocarbon-generating potential of the Middle Permian Lucaogou source rock in the eastern Junggar Basin, China

The Middle Permian Lucaogou Formation is the most significant source rock in the eastern Junggar Basin. Previous studies have confirmed its excellent hydrocarbon-generating potential in the Jimsar Sag. However, its potential in other areas of the eastern Junggar Basin remains uncertain. Based on total organic carbon and pyrolysis, organic petrology, hydrocarbon simulation experiments, basin simulation, and combined well-seismic coupling interpretation, this study systematically compares the hydrocarbon-generating potential of the Lucaogou source rock in the Jimsar Sag with other areas of the eastern Junggar Basin. It discusses the sedimentary environment of high-quality source rocks and depicts the distribution of practical source kitchens. The Lucaogou source rocks in the eastern Junggar Basin are oil-prone, dominated by type I–II kerogen, and generally classified as good to excellent source rocks. Nowadays, the area of the Lucaogou source rocks that have entered the main oil-generating window is approximately 11 × 103 km2. Except for the bulge area, the Lucaogou source rocks in the eastern Junggar Basin successively entered the hydrocarbon-generating threshold during the Jurassic and the main oil-generating window in the Cretaceous. The Lucaogou source rocks in the Jimsar Sag and other parts of the eastern Junggar Basin share similar biomarker fingerprints, characterized by relatively low ratios of Pr/Ph, Pr/n-C17, Tm/C30 hopane, C19/C20 tricyclic terpene, and C24 tetracyclic terpene/C26 tricyclic terpene, and high β-carotene content, gammacerane index, and Ts/Tm ratios. These characteristics reflect deposition in a strongly reducing brackish lacustrine environment with parental sources dominated by lower organisms such as algae and bacteria. Generally, the Lucaogou source rocks in the eastern Junggar Basin have an oil-generating intensity of more than 3 × 106 t/km2. Several oil-generating centers with an intensity of more than 5 × 106 t/km2 have developed in the front of the Bogda Mountain, Jimsar Sag, Dongdaohaizi Sag, Wucaiwan Sag, and Shazhang Fault Zone, covering a total area of approximately 12,500 km2. These characteristics of the Lucaogou source rocks promise favorable potential for forming large and medium oil fields. The results further consolidated the oil and gas resources in the eastern Junggar Basin and provided valuable references for exploring future oil and gas fields.

Целесообразность создания пилотной площадки по отработке безопасной эксплуатации технико- технологических средств для освоения длительнозамерзающих акваторий арктических морей

The Russian sector of the Arctic shelf to the east of the Kara Sea has been very poorly studied by seismic exploration and drilling. It is conditioned by several causes, one of which is the short ice-free period in combination with the remoteness from the shore bases, which significantly hampers operations making them extremely expensive and resource-consuming. The development of natural resources in conditions specific to the Kara Sea and to the east of it is restricted by the depths of the use of ice-resistant platforms and remoteness from the shore bases for subsea production systems. Various experts consider the perspective solutions of the implementation of subsea manned and unmanned drilling and production systems as concepts. At the same time, the existing technologies and the deep drilling technique are creatively reconsidered; the conclusion regarding the inapplicability of the currently used solutions for subsea wellhead drilling has been made. A specific focus has been made on the safety of the future oil and gas production facilities in the Arctic, considering both the operation of highly hazardous and complex manned underwater engineering facilities and the current strained geopolitical situation and the threats of terrorism. It is supposed that testing of perspective engineering solutions and technologies will be more efficient not only in conditions of plant testing laboratories and centers but also at the test site with maximally realistic conditions of the future operation. The analysis of potential test sites in accordance with selected criteria has chosen the Murmansk gas field in the southeast part of the Barents Sea as a prospective facility. Due to the large scale and complexity of the long-term challenge (20–30 years), a conclusion is made about the role of the state as an initiator and coordinator of the project involving the oil and gas companies concerned (Rosneft, Gazprom), scientific centers, and equipment manufacturers.

Response of N, P, and metal ions in deep soil layers to long-term cultivation of rubber and rubber-based agroforestry systems

The growing demand for natural rubber products has driven the expansion of rubber plantations in recent decades. While much attention has been given to studying the long-term effects of rubber and rubber-based agroforestry systems on surface soil properties, there has been a tendency to overlook changes in soil properties in deeper layers. Our study addresses this gap by examining alterations in nitrogen (N), phosphorus (P), and metal ion levels in deep soil layers resulting from the prolonged cultivation of rubber and rubber-based agroforestry systems. We found notable shifts in soil NH4+ and NO3− concentrations within the 0–30 cm soil layer across different-aged rubber and rubber-based agroforestry systems. Particularly in mature systems, NO3− and available P levels were close to zero below 30 cm soil depth. Introducing Flemingia macrophylla into young rubber plantations increased soil NH4+ and NO3− in the 0–90 cm soil layer and available P in the 0–10 cm soil layer. Over the long term, cultivation of rubber plantations increased the depletion of total P in the 0–50 cm soil layer, available iron (Fe) and manganese (Mn) in the 30–90 cm soil layer, available copper (Cu) and zinc (Zn) in the 0–90 cm soil layer, accompanied by a decrease in soil pH and increase in exchangeable aluminum (Al) in the 0–90 cm soil layer. Notably, soil exchangeable Al levels exceeding 2.0 cmol kg−1 appeared to induce aluminum toxicity. Furthermore, soil pH below 5.2 triggered a sharp release of exchangeable Al within the 0–90 cm soil layer of rubber plantations, with soil available P nearing zero when exchangeable Al levels assed 7.3 cmol kg−1. Our findings underscore the profound impact of long-term rubber plantation cultivation on surface and deep soil properties. Addressing soil degradation in these deep soil layers poses significant challenges for future soil restoration efforts.

Exploring the impact of cluster bagging-based weather attention on oil market volatility under the GARCH-MIDAS framework

ABSTRACT Many studies have confirmed the impact of weather and Google Trends attention on oil markets. However, research examining the predictive influence of weather attention on oil price volatility is lacking. To fill this research gap, we have developed an extended GARCH-MIDAS forecasting model based on TBATS and clustering bagging. We utilize weather-related keywords from Google Trends to obtain weather attention data and assess its predictive capability. By decomposing the data using TBATS and combining the decomposed trend and seasonal components with the remaining portion through clustering bagging and arithmetic mean aggregation, we construct a series of GARCH-MIDAS models, and specifically focus on the clustering bagging-based TBATS decomposition GARCH-MIDAS model. Empirical analysis demonstrates a significant impact of weather attention magnitude on in-sample testing, with the clustering bagging-based TBATS model outperforming other models in out-of-sample forecasting. Our findings provide new evidence that incorporating weather attention information into a GARCH-MIDAS model can improve prediction results and highlight the influence of weather attention on future oil price fluctuations. Finally, robustness analysis confirms the robustness of the proposed model. The proposed extended GARCH-MIDAS model based on TBATS and clustering bagging offers new insights into oil market volatility prediction.

Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.

Forecasting Crude Oil Volatility Using the Deep Learning‐Based Hybrid Models With Common Factors

ABSTRACTBased on empirical evidence of the Chinese commodity futures volatility dynamics, we propose a novel and flexible hybrid model, denoted as SAE‐HAR‐DL, which combines a supervised autoencoder (AE) with the deep learning‐based HAR model framework to capture essential common factor information and uses the reconstruction error of the AE component as a regularizer to enhance the generalization ability of the testing subsample. The empirical findings strongly support the effectiveness of this model in accurately forecasting crude oil futures volatility in the post‐COVID‐19 era, compared to the HAR, HAR‐PCA, and HAR‐DL models. Moreover, a robustness check also demonstrates the positive contribution of common factors to the volatility prediction of other commodity futures. Notably, we establish that these common factors act as effective regularizers, mitigating prediction losses within the HAR model in extreme risk events such as the COVID‐19 pandemic and the Russia–Ukraine conflict.

Dynamic Correlations Between Carbon Futures and Energy Futures Markets

An essential instrument for reducing carbon emissions worldwide is the carbon market. Comprehending the dynamic relationships between carbon and energy futures prices is crucial as carbon trading picks up promote globally. This research examines how two energy futurescrude oil and natural gasinteract with carbon futures in the American and European markets between November 21, 2013, and March 28, 2024. We examine correlations between futures returns across different regions using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. Our findings reveal significant, albeit fluctuating, correlations influenced by external market events. Interestingly, compared to crude oil futures, there is a stronger dynamic link between carbon futures and natural gas futures, especially in the European market, demonstrating strong long-term persistence.