Future Oil Research Articles

Modeling extreme risk spillovers between crude oil and Chinese energy futures markets

This paper aims to model the extreme risk spillovers between crude oil and Chinese energy futures markets to assess the effect of excessive oil price volatility on Chinese energy sectors. To this end, we set up a Generalized Autoregressive Conditional Heteroskedasticity - Extreme Value Theory Value-at-Risk specification (or GARCH-EVT-VaR hereafter) to flexibly model extreme risks. Moreover, we focus on two international crude oil futures markets and ten Chinese energy futures markets to measure the extreme risk spillovers. Our findings point to two main results. First, we find significant evidence of extreme risk spillovers from the two international crude oil markets to Chinese energy futures markets, which are asymmetric. More specifically, the spillover effects across extreme risks are more significant than those measured with the return series. Second, some Chinese energy future markets also exhibit internal extreme risk spillovers from the petrochemical sector to the coal sector. These findings reveal the potential vulnerability of Chinese energy sectors and call for active risk management policies to better hedge Chinese energy futures markets against extreme events.

Quantitative effect of kerogen type on the hydrocarbon generation potential of Paleogene lacustrine source rocks, Liaohe Western Depression, China

Kerogen types exert a decisive effect on the onset and capacity of hydrocarbon generation of source rocks. Lacustrine source rocks in the Liaohe Western Depression are characterized by thick deposition, high total organic carbon (TOC) content, various kerogen types, and a wide range of thermal maturity. Consequently, their hydrocarbon generation potential and resource estimation can be misinterpreted. In this study, geochemical tests, numerical analysis, hydrocarbon generation kinetics, and basin modeling were integrated to investigate the differential effects of kerogen types on the hydrocarbon generation potential of lacustrine source rocks. Optimized hydrocarbon generation and expulsion (HGE) models of different kerogen types were established quantitatively upon abundant Rock-Eval/TOC/vitrinite reflectance (Ro) datasets. Three sets of good–excellent source rocks deposited in the fourth (Es4), third (Es3), and first (Es1) members of Paleogene Shahejie Formation, are predominantly types I–II1, II1–II2, and II–III, respectively. The activation energy of types I–II2 kerogen is concentrated (180–230 kcal/mol), whereas that of type III kerogen is widely distributed (150–280 kcal/mol). The original hydrocarbon generation potentials of types I, II1, II2, and III kerogens are 790, 510, 270, and 85 mg/g TOC, respectively. The Ro values of the hydrocarbon generation threshold for type I–III source rocks gradually increase from 0.42% to 0.74%, and Ro values of the hydrocarbon expulsion threshold increase from 0.49% to 0.87%. Types I and II1 source rocks are characterized by earlier hydrocarbon generation, more rapid hydrocarbon expulsion, and narrower hydrocarbon generation windows than types II2 and III source rocks. The kerogen types also affect the HGE history and resource potential. Three types (conventional, tight, and shale oil/gas) and three levels (realistic, expected, and prospective) of hydrocarbon resources of different members in the Liaohe Western Depression are evaluated. Findings suggest that the Es3 member has considerable conventional and unconventional hydrocarbon resources. This study can quantitatively characterize the hydrocarbon generation potential of source rocks with different kerogen types, and facilitate a quick and accurate assessment of hydrocarbon resources, providing strategies for future oil and gas exploration.

Crude oil security in a turbulent world: China's geopolitical dilemmas and opportunities

China's economic and social development relies heavily on crude oil, which it imports and consumes more than any other country. Thus, the country's oil security is crucial. Geopolitical risk (GPR) is used to describe the instability and uncertainty resulting from political conflicts, terrorism, wars, sanctions, and other events that affect the production, transportation, and consumption of crude oil. Therefore, we use MF-VAR to examine how geopolitical risk GPR causes changes in Chinese crude oil security (COS). Unlike previous studies, we treat GPR as an outcome variable and use oil demand and cost channels to analyse its effects on COS. Quarterly government hazards endanger Chinese oil safety, and combined frequency data fixation reveals a dynamic link in various quarters of the year. Hence, to ensure a consistent supply of crude oil, the Chinese government's oil corporations must implement several initiatives, including purchasing shares and developing oil fields. To protect oil production and transportation, China should also forge strong links with nations that export oil and participate in UN initiatives such as the Somali escort and peacekeeping in Africa. To better manage oil price volatility brought on by geopolitical dilemmas, RMB-based crude oil futures are a smart approach for increasing affordability.

Changes in volatility leverage and spillover effects of crude oil futures markets affected by the 2022 Russia-Ukraine conflict

We investigate the changes in the volatility regular/inverse leverage and spillover effects of three crude oil futures markets—WTI, Brent, and Oman—before and after the outbreak of the 2022 Russia-Ukraine conflict. We find that this geopolitical conflict has significantly changed the leverage effect in the three markets, and increased slightly and stabilized the dynamic conditional correlation and weakened the volatility spillover effect between every pair of them. In contrast to the inverse, the regular leverage effect remains the dominant effect driving oil prices during both the sub- and whole-sample periods. In the face of this conflict, Oman can play a good role in portfolios, not necessarily in hedging, of crude oil futures; and it highlights the importance in risk-taking of Asian crude oil-exporting countries. These findings have implications for crude oil-exporting and importing countries, and futures investors.

Mapping of current and future soil erosion risk in a semi-arid context (haouz plain - Marrakech) based on CMIP6 climate models, the analytical hierarchy process (AHP) and RUSLE

Mapping of current and future soil erosion risk in a semi-arid context (haouz plain - Marrakech) based on CMIP6 climate models, the analytical hierarchy process (AHP) and RUSLE

A new long short-term memory based approach for soil moisture prediction

Water scarcity is becoming more severe around the world as a result of suboptimal irrigation practices. Effective irrigation scheduling necessitates an estimation of future soil moisture content. This study presents deep learning models such as CNN-LSTM, a hybrid Deep Learning model that predicts future soil moisture using climate and soil information, including past soil moisture content. The study also investigates the appropriate number of observations and data sampling rate required to predict the next day’s soil moisture value. In terms of MSE, MAE, RMSE, and R 2 , the hybrid CNN-LSTM model is compared to standalone LSTM and Bi-LSTM models. The LSTM model achieved an MSE of 0.2471, MAE of 0.1978, RMSE of 0.4971, and R 2 of 0.9714. The LSTM model outperformed the Bi-LSTM model, which had an MSE of 0.3036, MAE of 0.3248, RMSE of 0.5510, and R 2 of 0.9614. With an MSE of 0.1348, MAE of 0.1868, RMSE of 0.3672, and R 2 of 0.9838, the hybrid CNN-LSTM model outperformed the LSTM. Our findings suggest that deep learning models, particularly the Convolutional LSTM, hold great potential for predicting soil moisture accurately. The Convolutional LSTM model’s superior performance can be attributed to its ability to capture spatial dependencies in soil moisture data. Furthermore, the results show that for better prediction, sub-hourly data samples from the previous three days should be considered.

Hedging Strategies in Carbon Emission Price Dynamics: Implications for Shipping Markets

The European Union (EU) has agreed to gradually include shipping in the EU emissions trading scheme (EU ETS), which makes shipping companies vulnerable to carbon price fluctuations. The aim of this paper is to investigate the effectiveness of carbon and petroleum futures contracts in managing carbon and bunker risks. We examine the effectiveness of alternative hedging methods, including both static and dynamic approaches, to estimate optimal hedge ratios under single and composite cross-hedge settings. Our results show that carbon future contracts are important for hedging the carbon emission allowances price risk, and Brent oil futures are the most effective instrument for out-of-sample hedging of bunker prices. In addition, the hedging effectiveness indicates that conventional methods outperform the sophisticated models in terms of variance reduction. Our study offers new insights into how the carbon and bunker markets relate to a combination hedging in reducing the joint price risk, which can be used to promote risk management in the market.

Visibility graph-based segmentation of multivariate time series data and its application.

In this paper, we propose an efficient segmentation approach in order to divide a multivariate time series through integrating principal component analysis (PCA), visibility graph theory, and community detection algorithm. Based on structural characteristics, we can automatically divide the high-dimensional time series into several stages. First, we adopt the PCA to reduce the dimensions; thus, a low dimensional time series can be obtained. Hence, we can overcome the curse of dimensionality conduct, which is incurred by multidimensional time sequences. Later, the visibility graph theory is applied to handle these multivariate time series, and corresponding networks can be derived accordingly. Then, we propose a community detection algorithm (the obtained communities correspond to the desired segmentation), while modularity Q is adopted as an objective function to find the optimal. As indicated, the segmentation determined by our method is of high accuracy. Compared with the state-of-art models, we find that our proposed model is of a lower time complexity (O(n3)), while the performance of segmentation is much better. At last, we not only applied this model to generated data with known multiple phases but also applied it to a real dataset of oil futures. In both cases, we obtained excellent segmentation results.

Recurrent arrival of oil spill remnants along the Brazilian coast: In situ long-term weathering of the largest spill in Brazil’s history

Three years after the most severe oil spill affected Brazil’s coastline, tar balls still recurrently reach the Brazilian northeastern beaches. New accidents involving oil spills have not been reported since 2019. Information on the origin of these new oil deposits remains scarce. Samples of the tar balls found along Brazilian beaches in May 2022 were analyzed for hydrocarbon signatures, revealing a clear link with the oil that washed ashore in 2019. The new tar balls were more affected by biodegradation than photooxidation, suggesting the oil was trapped in the subtidal sediments and was later transported to the coast by marine currents. This work provides the first evidence of the persistence of oil in the marine environment and raises concerns regarding future oil influxes on the Brazilian coast.

Utilizing Reservoir Model to Optimize Future Oil Production for Hydraulic Fracture Wells in Tight Reservoir

The tight oil reservoir has low porosity and permeability generally suffers from rapid declines in production rates for oil wells, especially in southeast Iraq in the Halfaya oil field of the Sadi Formation, which is considered as tight oil reservoir with a reserve of about 25%; their OOIP accounts for more than a quarter of the total in the H oilfield. Implementing a pilot hydraulic fracture technique was a focus of attention in Iraq to increase the production rates, but the main issue faced in hydraulic fracturing wells was producing with a high oil rate for a short period of time then starting to decline rapidly, so a reservoir dynamic model was utilized to achieve the purpose of this study. The purpose of this study is to predict the production rate to prolong the production stabilization in horizontal and vertical hydraulic fracturing wells in order to avoid highly depleted fracture storage capacity and production below the bubble point. Recognize a practical procedure with horizontal hydraulic fracturing wells to reach stabilization. The reservoir simulator results show that a good history matches till 2021, predicting the rates that stabilize the production with flowing pressure above the bubble point pressure till 2025, producing rates for an eight-stage well with 700 BOPD and 900 BOPD for an eleven-stage well, whereas the rate for a vertical hydraulic fracture well is 225 BOPD. A practical procedure in horizontal hydraulic fracturing wells is achieved by keeping the wellhead pressure constant during the production period to stabilize transient behavior.

SPILLOVER EFFECTS OF THE FUTURES CONTRACT PRICES OF THE NYMEX CRUDE OIL: A MULTIVARIATE GARCH APPROACH

The main aim of this paper is to investigate spillover effects of the futures contracts prices of the Nymex Crude Oil. Tse and Tsui (2002) multivariate generalized autoregressive conditional heteroscedasticity model is used and a two-step MGARCH estimation of the two crude oil futures contracts prices of the New York Mercantile Exchange (NYMEX) is carried out. The results revealed of conditional volatility correlations between crude oil futures contract-3 and futures contract-4 of the NYMEX. The economic implication of this results is that a volatility of futures contract-4 could affect the volatility of futures contract-3 and vice-versa, which suggests that investment uncertainties associated with one of the contracts could foster investment uncertainties in the other contract which might raise the level of risks in the market and affect portfolio allocations of investors as well as firms' investment plans on the market. Therefore, the high level of volatility co-movements between the two series suggests that during the sample period there was high degree of volatility correlations between the two futures prices, which indicates that the forces driving the two futures prices of the NYMEX might be similar. Based on the finding of the study, the paper recommends that, authority concern should come up with appropriate policy that will reduce volatility correlations between crude oil future contracts.

A Tale of Two Tails: A New Unique Information Share Measure Based on Copulas

Abstract I propose a novel measure of information share, termed tail information share (TIS), which focuses on modeling the tail dependence of price innovations using copulas. I discuss its detailed technical properties, including unique identifiability, estimation procedures, and statistical properties. The proposed TIS improves over two commonly used measures by providing meaningful economic rationale and unique identifiability. My simulation studies indicate that TIS can yield more accurate estimates of market-specific contributions to price discovery when tail dependence is present. Additionally, I demonstrate the asymptotic consistency and efficiency of TIS estimators. An empirical illustration is provided using a new dataset of high-frequency crude oil futures.

The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models

This paper expands the emerging literature on volatility forecasting for China's oil market by exploring the predictive ability of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the heterogeneous autoregressive (HAR) framework, but considering the possible multicollinearity and nonlinearity, it is extended to various machine learning (ML) models and combination forecasting models. The results reveal that higher-order moments, including the two highest moments, always significantly improve predictive performance for the COVID-19 crisis. We further examine the interpretability of ML models and each factor's contribution to the prediction, finding that odd and even moments contain short- and long-term prediction information, respectively. This paper also highlights the effectiveness of ML models for capturing trends in oil futures volatility with higher-order moments and the satisfactory performance of combination forecasting models. Finally, we investigate the predictability of asymmetric risk patterns and obtain identical results. Our study has important implications for financial risk management, asset pricing, and portfolio allocation.

Jumps in the Chinese crude oil futures volatility forecasting: New evidence

This study examines whether incorporating jumps and jump size can enhance the predictive ability of Chinese crude oil futures market realized volatility within the MIDAS model framework, incorporating Markov-switching. Our out-of-sample analysis indicates that the Markov-switching mixed data sampling model incorporating large and small jumps (MS-MIDAS-LMLS) performs better than other models in forecasting the volatility of Chinese crude oil futures. To ensure the robustness of our findings, we conducted various sensitivity test. The results consistently exhibit strong predictive performance of the MS-MIDAS-LMLS model. Additionally, we explored the impact of jump size on forecasting performance using an alternative jump test method (the ABD test) and different volatility regimes, with similar results indicating the positive impact of incorporating jump size on forecasting accuracy. Overall, our results underscore the potential benefit of integrating jumps and jump size within the MIDAS model framework to enhance the accuracy of volatility forecasting of Chinese crude oil futures market volatility.

Extreme quantile spillovers and connectedness between oil and Chinese sector markets: A portfolio hedging analysis

Oil price instabilities have direct and heterogeneous implications for stock sector markets as a result of portfolio risk management and fund allocations. Previous studies have shown that the oil-stock market nexus is asymmetric and strongly vulnerable to international events. Using daily data of ten Chinese stock sector indices and the West Texas Intermediate crude oil futures over the period from July 2, 2007, to September 3, 2021, we examine the quantile return spillovers and interconnectedness between these markets using the approach of Ando et al. (2022), showing that return spillovers between the markets under investigation are more pronounced under bearish market conditions than during bullish ones. Major financial, political, energy, and COVID-19 pandemic events have magnified spillovers. Irrespective of the state of the market, oil is always a net receiver of return spillovers. Moreover, for all sectors other than materials, the sector that acts as a net receiver during bearish market conditions becomes a net contributor during bullish market conditions, and vice versa. During the COVID-19 period, the hedging technique was the most cost-effective. In the event of a global pandemic, the IT, financial, telecommunication, and energy sectors can benefit from oil's higher hedging effectiveness. Oil was a cheaper hedging asset during the pandemic, and it offered the highest hedging effectiveness to utilities before the outbreak and to the financial sector during the COVID-19 pandemic.

Fertilization-induced reactive nitrogen gases and carbon dioxide emissions: insight to the carbon-nitrogen cycles

Different agricultural practices can pose significant threats to environmental quality and human health. This study aimed to assess the emissions of reactive nitrogen (NH3, NOx, and N2O) and carbon dioxide (CO2) induced by fertilization in spinach and cabbage farmlands. Field and pot experiments were conducted to analyze the emission fluxes and intensities of reactive nitrogen gases and CO2. The findings revealed that the total emissions of reactive nitrogen for cabbage and spinach ranged from 21 to 798 kg-N ha−1 and 1.1 to 489 kg-N ha−1, respectively. Generally, organic fertilizers exhibited higher emission intensities of NH3 compared to N2O. While slow-release fertilizers effectively reduced NH3 emissions, they resulted in increased soil N2O emissions. Furthermore, the total emissions of reactive nitrogen from the soil showed a positive correlation with soil CO2 emissions. Particularly, organic farming practices, especially in the case of cabbage, led to increased CO2 emissions from farmlands. Based on the experimental findings, three priority directions were suggested to achieve sustainable soil carbon and nitrogen management in order to minimize emissions from farmlands. This study provides valuable insights for future soil carbon and nitrogen management in subtropical regions.

Conditional macroeconomic survey forecasts: Revisions and errors

Using data from the European Central Bank's Survey of Professional Forecasters and ECB/Eurosystem staff projections, we analyze the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the updating and ex-post performance of predictions for inflation, real GDP growth and unemployment are related to beliefs about future oil prices, exchange rates, interest rates and wage growth. While oil price and exchange rate predictions are updated more frequently than macroeconomic forecasts, the opposite is true for interest rate and wage growth expectations. Beliefs about future inflation are closely associated with oil price expectations, whereas expected interest rates are related to predictions of output growth and unemployment. Exchange rate predictions also matter for macroeconomic forecasts, albeit less so than the other variables. With regard to forecast errors, wage growth and GDP growth closely comove, but only during the period when interest rates are at the effective zero lower bound.

Linking niche size and phylogenetic signals to predict future soil microbial relative abundances.

Bacteria provide ecosystem services (e.g., biogeochemical cycling) that regulate climate, purify water, and produce food and other commodities, yet their distribution and likely responses to change or intervention are difficult to predict. Using bacterial 16S rRNA gene surveys of 1,381 soil samples from the Biomes of Australian Soil Environment (BASE) dataset, we were able to model relative abundances of soil bacterial taxonomic groups and describe bacterial niche space and optima. Hold out sample validated hypothetical causal networks (structural equation models; SEM) were able to predict the relative abundances of bacterial taxa from environmental data and elucidate soil bacterial niche space. By using explanatory SEM properties as indicators of microbial traits, we successfully predicted soil bacterial response, and in turn potential ecosystem service response, to near-term expected changes in the Australian climate. The methods developed enable prediction of continental-scale changes in bacterial relative abundances, and demonstrate their utility in predicting changes in bacterial function and thereby ecosystem services. These capabilities will be strengthened in the future with growing genome-level data.

Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market

This paper suggests a Markov-switching model to evaluate commodity futures and spot dynamics, such that the diffusion coefficients and jump size parameter are associated with a hidden Markov chain. We improve the current models in the literature of the commodity markets by modeling the sudden jumps in the commodity prices through the hidden Markov chain. From the crude oil spot price in West Texas Intermediate, we estimate the parameters of proposed Markov-switching model based on expectation–maximization algorithm. To perform this task, we apply this estimation algorithm to the model discretized by Euler scheme and provide some convergence analysis for this discretization method. There are options, such as European options, which are written on the commodity futures. In this study, we evaluate them under the regime-switching model with various economic states. In the following, we calibrate the option prices resulting from the proposed commodity model to a set of observed European call options written on crude oil futures. For this purpose, we first apply an inverse Fourier transform and obtain a semi-analytical option pricing formula. Then, we use the fast Fourier transform method to compute option prices. Since the investors need to calculate Greeks in order to understand the risk involved in option investments, the Greek formulas of Delta, Rho, Theta, and Gamma are derived.

INE oil futures volatility prediction: Exchange rates or international oil futures volatility?

This study examines the predictive performances of volatility information from international oil futures volatility and exchange rates for Shanghai International Energy Exchange (INE) oil futures volatility. Various empirical findings show that the predictive performance of international oil futures volatility is superior to that of the exchange rate. In addition, we examine whether regime switching is efficient, and the results show that time-varying regime switching plays a satisfactory role. Moreover, these results are robust even during some special periods, including the COVID-19 pandemic, the Russia–Ukraine conflict, and different business cycles. This study provides new insights into the volatility prediction of the INE oil futures market.