Crude Oil Futures Research Articles

Analysis of the Interaction Between Bitcoin Price and Crude Oil Futures Price

The aim of this thesis is to analyze the interaction between Bitcoin price and crude oil futures price over the past seven years using the Ordinary Least Squares (OLS) method. Bitcoin, as a virtual currency, is characterized by high price volatility, while crude oil futures, as an important commodity, are also affected by a variety of factors. By collecting and analyzing data on the price of Bitcoin and crude oil futures in the past seven years, this research aims to investigate the interaction between Bitcoin price and crude oil futures price and explore their correlation and causation. The results of the study will provide investors and policymakers with important information about the bitcoin and crude oil futures markets in order to better understand market dynamics and formulate relevant strategies. Meanwhile, the time span of the study includes both the New Crown Epidemic period and the Post-Epidemic period, which is generalizable and more conducive to research and analysis under different era conditions.

Volatility forecasting of crude oil futures based on Bi-LSTM-Attention model: The dynamic role of the COVID-19 pandemic and the Russian-Ukrainian conflict

The COVID-19 epidemic and the Russian-Ukrainian conflict have created significant uncertainty in the crude oil market, greatly increasing the difficulty of crude oil futures price volatility forecasting. The objective of this study is to investigate the time-varying nature of the impact of major events on the crude oil market and to examine how to accurately predict crude oil futures price volatility during major shocks. To this end, we propose a novel crude oil futures price volatility forecasting framework based on the Bidirectional Long and Short-Term Memory Neural Network-Attention Mechanism Model (Bi-LSTM-Attention), which contains period division, variable screening, model prediction, and assessment indicators, to further analyze in-depth the impacts of COVID-19 and the Russia-Ukraine conflict on the volatility of crude oil futures price. In addition, we also investigate the impact of external factors on the crude oil market, including economic status, geopolitical events, and the COVID-19 pandemic. Empirical evidence demonstrates that the impact of major events on crude oil futures price volatility is significantly time-varying and differentiated. Particularly during high shock periods, the Fake News Index plays an extremely influential role in driving crude oil futures price volatility. Moreover, our proposed model reliably captures the trend of crude oil futures price volatility during these violent shocks. The contributions of this study are to provide valuable insights into the impact of significant events on crude oil price volatility and contribute to a deeper understanding of crude oil market dynamics.

A Study on Co-movements and Information Spillover Effects Between the International Commodity Futures Markets and the South Korean Stock Markets: Comparison of the COVID-19 and 2008 Financial Crises

Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises.
 Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat.
 Findings - The results of the co-movement analysis were as follows: First, it was shown that the co- movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed.
 Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.

A matrixed nonlinear exponential grey Bernoulli model for interval number prediction of crude oil futures prices

PurposeThe effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.Design/methodology/approachFirst, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.FindingsThe predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.Originality/valueCrude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

A novel crude oil futures trading strategy based on volume-price time-frequency decomposition with ensemble deep reinforcement learning

With the deepening financialization of crude oil, an increasing number of investors are becoming involved in trading crude oil futures. However, various factors influence crude oil futures price fluctuations, presenting complex characteristics. For this reason, a trading strategy plays a crucial role in investment. In this paper, we construct an ensemble trading strategy by combining time-frequency feature extraction of crude oil volume-price series with deep reinforcement learning. We decompose the original oil volume-price series by using the optimized variational mode decomposition. Additionally, taking into account the long memory characteristics of the crude oil time series, we employ the Sharpe ratio to filter the appropriate agents corresponding to market volatility. Moreover, the best features of the three agents are effectively combined and used in the next phase of trading, thus robustly adapting to different market volatility situations. Finally, we tested our strategy using Brent crude oil futures. Compared with other benchmark models, the proposed OVMD(V–P)-DRL-Ensemble strategy is suitable for the highly volatile crude oil market and performs well in terms of the corresponding performance evaluation metrics, showing excellent return performance and stability.

How Does the Time-Varying Network Structure Evolve between the EU Carbon Futures Prices and Industrial and Energy-Related Indices? A Study Based on a Time-Varying T-Copula

Industrial and energy-related industries are major sources of carbon dioxide emissions, and their interdependence, as reflected in the financial field, has attracted the attention of scholars. For the purpose of exploring the evolutionary characteristics of the short-term dynamic correlation coefficient between the EU carbon futures price and the industrial and energy-related indices, this paper selected the settlement price of EU carbon emission quota futures, the MSCI energy I index on three dimensions, and the Dow Jones industrial index and West Texas crude oil futures price, as sample data. Using the time-varying t-copula model to measure the dynamic correlation coefficient between variables, the time-sliding window idea and coarse-grained method were combined to establish the correlation fluctuation mode, and a complex network theory and analysis methods were used to study the evolutionary traits of the time-varying network structure between the EU carbon price and the industrial and energy-related index. The results show that the transmission objects of the key correlation fluctuation modes in the network are stable and maintain their own state with a high probability. Second, the clustering effect exists in the transmission process. Some nodes with high mediating abilities are also the key correlation wave modes in the dynamic correlation evolution network. This study provides ideas for the study of the correlations between multiple variables and is also a useful reference for international investors.

Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices

The Shanghai crude oil futures market exudes distinct speculative attributes, underscoring the pivotal significance of precise price forecasts. Accurate forecasting of Shanghai crude oil futures prices assumes vital importance for investors to optimize their portfolios profitably, for producers to mitigate production risks in the crude oil spot market, and for providing cogent decision-making support to government entities. This study implements a groundbreaking unbiased structural forecasting for Shanghai crude oil futures' open-high-low-close (OHLC) prices leveraging the Transformer framework coupled with the model-driven and penalty term-based loss function designs. Based on OHLC forecasts, this study devises three intraday trading strategies. Notably, our results evince that the forecasting accuracy of the Transformer outperforms the Naïve method, vector autoregression (VAR) and vector error correction model (VECM), multiple linear regression (MLR), support vector regression (SVR), and long short-term memory (LSTM) neural network when applied to different temporal granularities of Shanghai crude oil futures OHLC data. Furthermore, the three proposed intraday trading strategies exhibit higher annualized return rates and Sharpe ratios, alongside lower maximum drawdowns and standard deviations of returns in comparison to the conventional close-to-close strategy relying solely on the close price. Remarkably, the forecasting process and intraday trading strategies explicated in this study can equally apply to other futures products in the energy sector, including electricity, coal, and natural gas.

Shanghai crude oil futures: Returns Independence, volatility asymmetry, and hedging potential

Amidst the rise of Shanghai crude oil futures (SCOF) as a preeminent contender in the global oil arena, this study analyzes its returns and volatility structures in compelling contrast to West Texas Intermediate (WTI) and Brent oil futures (BRENT). A comprehensive examination is conducted using various GARCH models and News impact curves, with the analysis based on daily data spanning from April 2021 to March 2023. The results reveal distinct responses of SCOF when contrasted with WTI and Brent. Firstly, the study finds that SCOF returns exhibit a level of independence from global market movements. Secondly, the assessment of potential asymmetries in the volatility structures displays notable differences among the three markets. Specifically, WTI demonstrates the highest asymmetry, while SCOF exhibits lower asymmetry. These findings imply that SCOF returns exhibit stability and resilience and hold the potential to serve as a formidable hedge against adverse shocks. As investors and policymakers navigate the complex terrain of the global oil market, these insights underscore the strategic advantages and opportunities that SCOF may offer, both in individual investment decisions and broader risk management strategies.

The Impact of the Fed's Interest Rate Adjustment Cycle on the Price of WTI Crude Oil Futures

This article is based on monthly data from January 2016 to January 2022, including the US Federal Funds Rate and WTI crude oil futures price index. A TVP-VAR model is constructed to study the impact of the Federal Reserve's interest rate cycles on WTI crude oil futures prices. The research found a positive correlation between crude oil futures prices and the Federal Reserve's interest rate cycles. That means that when the Federal Reserve raises or lowers the US Federal Funds Rate, it leads to an increase or decrease in crude oil futures prices. Therefore, it is believed that in the current context of global quantitative easing and the sustained rise in international crude oil prices, China should formulate a new energy strategy that matches the high-quality development of the social economy and accelerate the transformation and upgrading of the crude oil industry.

Risk Management Case Analysis of ICBC's "Crude Oil Treasure" Incident

Driven by the marketization of interest rates and technological innovation, commercial banks in China continue to innovate various financial services businesses and devote more attention to attracting customers to purchase various investment and wealth management products in order to expand sources of bank performance and gain more benefits. In 2018, paper crude oil-linked wealth management products emerged. Paper crude oil products are linked to the prices of international crude oil futures contracts. The products feature controllable risks and relatively high returns, which are very popular among large numbers of customers. However, despite the continuous expansion of China's financial derivatives trading volume, the categories of derivative products and regulation still have many limitations. The development of innovative paper crude oil-linked products is not perfect and has many potential risks, which are highly susceptible to market fluctuations. Taking China Bank's "Crude Oil Treasure" product as the research focus, this paper analyzes the massive loss incident that occurred in late April 2020 through case study analysis and qualitative analysis methods. It is divided into four parts. The introduction part explains the research background, significance, content, methods and innovations and inadequacies of this paper. The first part sorts out the background and process of the "Crude Oil Treasure" case at China Bank and elaborates on the operating principles of this product. The second part analyzes the "Crude Oil Treasure" case in detail. It first focuses on the problems in the product's trading liquidity, domestic and international time differences and product promotion. Then it focuses on the internal and external causes of the case, discussing external factors such as international crude oil market turmoil and futures trading characteristics, and internal factors such as product design, risk control and lack of experience. It also evaluates the hedging strategy, contract design and compliance of the "Crude Oil Treasure" product. The third part mainly analyzes the impacts of the "Crude Oil Treasure" rollover incident and China Bank's subsequent response measures. The fourth part puts forward certain countermeasures and suggestions from the perspectives of bank management, investors and regulatory agencies to prevent such incidents from recurring based on the case analysis.

Functional classification and dynamic prediction of cumulative intraday returns in crude oil futures

Predicting the movement of crude oil futures is crucial for investors to identify opportunities and for the efficient functioning of financial markets. This paper applies functional classification and dynamic prediction (FCDP) to forecast the cumulative intraday returns of INE and Brent crude oil futures for every 5 min, to address the characteristics of high-frequency data and complex change patterns of crude oil futures market. The primary goal of FCDP is to classify trading days with the same volatility pattern employing probabilistic functional classification based on the artificial distinction between the daytime and nighttime trading hours, and then to generate forecasts using the classification results. The robustness test compares the forecasting results of FCDP with ARIMA, OLS and RW methods. According to the findings, FCDP outperforms other methods when it comes to making predictions concerning crude oil futures. Due to the FCDP's incorporation of the concept of dynamic updating, which is not reflected in conventional prediction methods, the prediction error gradually lowers as the amount of seen data increases. INE and Brent crude oil futures market trading patterns at night are more intricate than they are during the day. This study supports flexible trading strategy adjustments for both investors and policymakers.

Volatility Forecasting of Crude Oil, Gold, and Silver Futures: A Case of Pakistan Mercantile Exchange

The volatility of commodity prices has been a topic of interest for researchers and investors for decades. In recent years, the prices of key commodities have shown significant fluctuations, causing challenges for market participants to make informed investment decisions. Therefore, this paper provides an understanding of forecasting and modeling the volatility of commodity futures in the Pakistan Mercantile Exchange (PMEX) using GARCH and ARIMA models. The study aims to analyze and predict the volatility of three key commodities, namely Gold, Silver, and Crude Oil, and to compare the performance of the two models in forecasting their future prices. The study uses daily time-series data from 2010 to 2021 and finds that the prices of Gold and Crude Oil futures exhibit asymmetrical effects on their volatilities, while silver futures show stability over time. The results are useful for potential investors, economic agents, managers, financial researchers, and policymakers to analyze the volatility of commodity futures in the market. This will also help the investors to diversify their investments by analyzing the variation in such commodities in the international markets.

Forecasting crude oil futures price using machine learning methods: Evidence from China

Crude oil is an indispensable energy resource. With the establishment of the local crude oil futures market in China, providing accurate forecasts for crude oil futures price is urgent. To cope with this challenge, this paper introduces artificial intelligence methods to evaluate the optimal forecasting strategy for China's crude oil futures price. We use machine learning to process historical information, volatility and non-linear features. Using daily data from March 26, 2018 to February 28, 2023, we estimate the forecasting effects of RNN, LSTM, GRU, SVR, MLP, CNN and BP models on China crude oil futures, respectively. With a series of evaluation tests, we demonstrate that the GRU model outperforms other models in terms of forecast accuracy and performance for China's crude oil futures price. Taking multiple influencing factors into account, the forecasting accuracy of proposed models is improved by including influential factors. Therefore, these findings effectively explore the forecasting of China's crude oil futures prices, contributing to the improvement of the emerging crude oil futures market and the management of energy price risks.

Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China

In the structure of energy consumption of China, coal and oil rank the top and the second place. It is obviously important to better understand the dynamics of the price volatility between crude oil and coal in China, and then to build a reasonable energy portfolio. As the listing of China's crude oil futures offers a new sample to study the volatility contagion of oil and coal prices, by using daily data from 2nd May 2018 to January 23, 2020, four different multivariate GARCH models are compared and contrasted in this paper. Then, based on the results of the four MGARCHs, we investigate the oil-coal portfolios' optimal weights and hedge ratios. The results indicate that, first, a long-run co-integration relationship exists in China's crude oil and coal. Second, in short run, futures price of crude oil leads that of coal, the volatility spillover effect of crude oil on coal is stronger than that of coal on crude oil. Third, the DCC-GARCH model fits best for data. Last, weights of optimal portfolio and ratios of hedge show that the crude oil is a tool to make the risk of portfolio minimal in energy markets. These results are expected to have implications for energy producers, users and trading enterprises to inform their decision-making.

Evidence of the internationalization of China's crude oil futures: Asymmetric linkages to global financial risks

Since its launch, China's crude oil futures (COIL) have gradually integrated with the international financial market but undoubtedly face more risks. For the purpose of exploring the influence mechanism between COIL and global risk, this paper uses a series of Granger causality tests to provide more evidence of COIL internationalization. In addition, we focus on exploring whether this influence mechanism has asymmetric characteristics. We choose a comprehensive new indicator, the Global Financial Risk Index (COVOL), to represent global financial risk, and also pay attention to three kinds of global risk refer to international energy, inflation, and financial system uncertainty. First, we find a marked asymmetry in the impact of global risks on COIL, especially energy-related risks, followed by financial system uncertainty. Second, COIL also has a significant asymmetric impact on global financial risks, including COVOL, GOIL, FSI, and EPU. Next, we further explore long-term and short-term changes in these asymmetric relationships. Empirical results have found that most asymmetric patterns exhibit long-term characteristics. Finally, given the potential impact of global extreme events, we provide an in-depth analysis of whether this asymmetric link still holds after the COVID-19 outbreak and the Russia-Ukraine conflict. These findings may provide important implications for improving coil market policy and related investment decisions.

The forecast ability of a belief-based momentum indicator in full-day, daytime, and nighttime volatilities of Chinese oil futures

China launched its domestic crude oil futures market in 2018 and this nascent market is steadily developing. Studying volatility predictability for Chinese crude oil futures has important academic and practical significance. In this study, we examine the predictive impacts of a novel predictor, the belief-based momentum indicator of conditional past return (CPR), a cluster of predictors from the domestic (Chinese) stock and oil markets and the US stock and options markets, and global finance-related indicators. In addition to the full-day oil realized volatility (ORV), we also forecast the daytime and nighttime ORVs. First, we find that the novel CPR indicator has significant incremental predictive information for the full-day, daytime, and nighttime ORVs, whether in the short, middle, or long term. Furthermore, the CPR indicator is the most reliable among the four endogenous predictors. Second, predictors of the Chinese stock market have no effective predictive information; instead, predictors from the US stock and options market have great impacts on future Chinese ORVs. Third, our evidence shows that the predictability of the night ORV is half that of the daytime ORV, and the VIX is relevant for the nighttime ORV. Our research helps Chinese policymakers improve the crude oil market mechanism and prevent market risks.

Volatility predictability in crude oil futures: Evidence based on OVX, GARCH and stochastic volatility models

The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks — Brent in Europe and WTI in America are compared. OVX index is able to provide the optimal forecast for the volatility of Brent’s future. The GJR-GARCH model outperforms its competition volatility models in both oil markets. It also reveals that WTI exhibits a faster and stronger response to market shocks compared to Brent oil. Most importantly, the predictability exhibit consistent and convincing results during remarkable events: the 2014 oil price decline, the 2020 coronavirus pandemic, and the 2022 Russian–Ukraine war. In practice, the paper contributes to portfolio strategy construction and investment risk management.

Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint

AbstractParameter instability and model uncertainty are two key problems affecting forecasting outcomes. In this paper, we propose a time‐dependent weighted least squares with ridge constraint (TWLS‐Ridge) to solve the above two problems in the forecasting procedure. The new TWLS‐Ridge approach is applied to the heterogenous autoregressive realized volatility model and its various extensions. The empirical results suggest that TWLS‐Ridge produces more accurate volatility forecasts than several alternative models dealing with parameter instability and model uncertainty. The superior performance of TWLS‐Ridge is robust under different forecast horizons, evaluation periods, and loss functions. An investor with mean–variance preference can improve utility using TWLS‐Ridge forecasts of oil volatility instead of ordinary least squares model forecasts.

Spillover relationship between different oil shocks and high- and low-carbon assets: An analysis based on time-frequency spillover effects

In this paper, the spillover relationship between different oil shocks and high- and low-carbon assets is investigated through DY and BK spillover methods. It is found that the spillover relationships between them are heterogeneous, with WTI crude oil futures and heating oil futures being highly affected by oil demand and supply shocks, and clean energy sector stocks being highly affected by oil risk shocks. The spillover relationship between them changes at different time frequencies. For high- and low-carbon assets, more attention should be paid to protecting against risks from the same assets. Natural gas futures are little affected by spillovers.

The Effect of the Chinese Industry Sector in Predicting Oil Price: Evidence from Information Geometric Causal Inference and GWO-ELM

The COVID-19 outbreak and the implementation of peak and carbon neutral policies have severely impacted oil price volatility and the industrial sector. Exploring the impact mechanisms between oil prices and industries is particularly important for accurate forecasting of crude oil prices. As one of the world’s largest commodity consumers, China’s crude oil market is more representative and susceptible to external factors than that of developed countries. In this paper, we propose an analytical forecasting framework based on the causal effects between Shanghai crude oil prices and various industries in China to improve the forecasting accuracy of crude oil prices. Information geometric causal inference (IGCI) is applied to detect causal relationships between 31 different industries in China and Shanghai crude oil prices in the three time periods before, during and after COVID-19, and industries with strong causal information effects on crude oil prices in the long run are screened out as additional features. An oil price forecasting model based on Gray Wolf Optimization and Extreme Learning Machine (GWO-ELM) is proposed. Considering the small amount of data for Shanghai crude oil, this paper proposes a cross-learning data approach to solve the problem. Experimental results show that the GWO-ELM model outperforms RF, LSTM, GRU, and migration learning-based Tr-LSTM and Tr-Adaboost models in the task of Shanghai crude oil futures price prediction, and find that industry characteristics with long-term causal effects on oil prices can improve the model prediction accuracy. Our proposed analytical prediction can capture the oil price trend more accurately through the information of the industry and solve the problem of insufficient training data for the model. The application of this framework is expected to provide new methods and ideas for data mining of crude oil and other futures prices.