Crude Oil Futures Research Articles

Asymmetric and time-frequency volatility connectedness between China and international crude oil markets with portfolio implications

This paper tries to examine asymmetric and time-frequency volatility connectedness between the Chinese crude oil futures market and international oil benchmarks. To this end, we separate the realized volatility as bad or good volatility and decompose the aggregate volatility connectedness among these oil markets into the short-, medium-, and long-term components. Our results first show that these crude oil markets are highly connected, whereas the Chinese crude oil futures market is a net receiver in the volatility system. Second, the spillover effect caused by bad volatility is significantly different from that of good volatility, revealing significant asymmetry in the volatility spillovers. Third, the volatility spillovers in the short-term frequency band account for the most of total volatility spillovers. Finally, our results prove that the volatility connectedness information among different oil markets helps design trading strategies and shed light on the arbitrage opportunities in the Chinese new crude oil futures market.

Research on the Impact of Crude Oil Price Fluctuation Based on EGARCH Model

At present, the largest commodity futures variety in the global futures market is crude oil futures, which plays a role in price discovery, risk hedging, and stabilizing international crude oil prices. The fluctuation of its price is a key issue that governments, investors, and scholars around the world pay attention to. This paper establishes GARCH model and EGARCH model under three distribution states of t distribution, Normal distribution and GED distribution. Then, through model comparison, it was found that the EGARCH model with t-distribution had the best fitting effect. Finally, the EGARCH model under t distribution is predicted within and outside the sample. It is found that under the impact of the financial crisis in 2008 and the COVID-19 epidemic, the crude oil futures prices fluctuated rapidly and violently, indicating that global International incident have a great impact on crude oil futures prices. The innovation of the research lies in comparing crude oil futures under different distributions based on GARCH and EGARCH models, which depict the characteristics of asymmetry.

Time-varying tail risk connectedness among sustainability-related products and fossil energy investments

In this paper, we analyse the dynamic transmission of tail risk across a set of well-established sustainability-related financial indices & equities and energy assets using a novel CAViaR-TVP-VAR connectedness measure on daily data from 14 October 2014 to 31 August 2022. Findings suggest that the total risk connectedness is at medium level and the short-run effect of COVID-19 on the risk transmission was mild. Furthermore, ESG and green equities are persistent net risk transmitters, while green bond, carbon asset, and energy commodities are tail risk takers. The role of renewable energy stocks is inconclusive due to distinct time-varying characteristics. With reference to pairwise relationship, we show that sustainability equities strongly interact with crude oil futures and fossil energy equities. Furthermore, green bond, carbon, natural gas and coal futures weakly associate with the remaining assets in the system. Finally, we find that EPU, OVX, VIX, GPR, and the spread of US Treasury have asymmetric impact on the spillovers. Altogether, our results offer insightful implications for policymakers and especially for “green” and “brown” products investors in risk diversification from the VaR perspective.

A study of crude oil futures price volatility based on multi-dimensional data from event-driven and deep learning perspectives

There are two unique challenges in current research of price volatility studies in the crude oil futures market, which is how to capture the impact of interactivity between data as much as possible when multi-dimensional data are fused, and how to solve the problem of temporal heterogeneity of research samples existing in multi-dimensional data. We proposed a tensor-based event-driven gated recurrent unit (Event-GRU) deep learning model that helps to solve the above dilemmas. Our model can effectively prevent the loss of interaction relationship of multi-dimensional data by taking the advantage of spatial data representation of tensor, as well as using news occurrence as a trigger to construct the event-driven mechanism based on GRU model, in order to overcome the feature space distortion between discrete news data and continuous trading fundamentals data due to the uneven sample time matching. Regarding the current insufficient research of crude oil prices in China, we addressed the crude oil futures trading data from the Shanghai Futures Exchange and 31,422 news data of the crude oil futures market from April 2018 to August 2020. We used the XGBoost algorithm (eXtreme Gradient Boosting) to evaluate and filter. Besides, we built a tensor-based event-GRU deep learning model to finally analyze the closing price on the day after. This research contributes to improving the accuracy of crude oil futures price analysis compared with using benchmark models such as DT, SVM, BP, LSTM, and GRU, and laying the foundation for the subsequent opening of corresponding quantitative investment strategies.

Derivatives Accident: The Crude Oil Treasure Incident Research

The price of the WTI2005 crude oil futures contract was concluded at -37.63 US dollars per barrel on April 20 at US Eastern Time. This is the first negative price of WTI crude oil since it was listed on the Chicago Stock Exchange, causing vast losses to the Bank of China and crude oil treasure investors. In order to avoid similar accidents in the future, both internal and external risks need to be evaluated. The Bank of China is primarily accountable for the accident as the seller of the crude oil treasure due to its blatant incompetence in regard to the company's products, sales, risks, disclosure, supervision, and internal controls. However, external factors including COVID-19, the Organization of the Petroleum Exporting Countries, the CME Clearing, as well as regulators and consumers of crude oil treasure actually have some obligations thereby providing suggestions to banks, regulators, and investors. This study is vital for the use and regulation of derivatives in light of the rapidly expanding and developing financial sector in China.

Exploring the dynamic behaviour of commodity market tail risk connectedness during the negative WTI pricing event

Using a TVP-VAR analytical framework, this study explores the change and persistence of the dynamic connectedness of international energy and carbon credit markets. The overall destabilising effects generated by recent political and epidemiological events, and the subsequent consequences of shocks such as the negative WTI pricing event, have the potential to be disruptive to the continued growth and development of several regional oil markets. Results are presented via a comprehensive analysis of the dynamics of extreme risk spillovers for particular commodity pairs. In particular, WTI and Brent crude oil are found to have transmitted significant tail uncertainty shocks to other energy markets. However, Shanghai crude oil and carbon credit markets typically function as shock absorbers. The remaining energy-related commodities primarily function as tail uncertainty receivers. Further, by incorporating EGARCH-based robustness procedures, tests for significant market connectedness within international energy markets adds further validity to the results. Specifically, results relating to the substantial rebalancing of information to Shanghai crude oil futures and EUA carbon futures merit special consideration, as dynamic interactions strengthen evidence supporting their continued maturation into significant international markets. These findings are particularly interesting to policymakers and market participants who use such products to hedge against and diversify regional oil market fluctuations.

Network connectedness between China's crude oil futures and sector stock indices

The financialization and international influence of China's crude oil market are increasing with the listing of China's crude oil futures (INE). In this context, we use the time-frequency connectedness network model to analyse the dynamic spillover effect between Chinese crude oil futures and sector stock indices, and compare it with American crude oil futures (WTI), where the study period includes the COVID-19, the European energy crisis, the Russia-Ukraine conflict and its like. The empirical results demonstrate that the total spillover effect of the crude oil and stock markets is mainly generated by high frequency (1–5 days). Extreme events in crude oil markets such as the Saudi Arabia oil field attack and the negative WTI price led to significant changes in the connectedness network of the system, increasing spillover risk and reducing system stability, but it recovers quickly. Moreover, the heterogeneity of spillover effect from crude oil futures to sector stock indices exists with energy industry and crude oil market the strongest, and meanwhile the spillover effects between crude oil and sector stock indices are smaller than those among sector stock indices in general. Further, China's sector stocks are more closely related to INE (crude oil futures listed on Shanghai International Energy Exchange) than WTI. Specifically, the spillover effect between WTI and INE is asymmetric, that is, the spillover effect from WTI to INE is significantly stronger than from INE to WTI. Finally, Dynamic portfolio weights indicate that assets with low connectedness are beneficial for balancing risk and return. These new findings suggest some useful implications: Regulators should pay close attention to cross-market risk and abnormal vibration of INE, and stock investors in China had better not regard crude oil futures as a hedging market.

Oil price uncertainty and audit fees: Evidence from the energy industry

This study examines how oil price uncertainty, measured by implied volatility, affects audit fees. Using samples from the energy industry, we find that oil price uncertainty increases audit fees. Furthermore, the mechanism analyses demonstrate that the increase in audit fees is driven by the increased audit effort rather than a higher audit risk premium. Additionally, the positive impact of oil price uncertainty on audit fees is more pronounced for non-state-owned firms, firms with weaker corporate governance, and firms with greater earnings manipulation risks. Finally, our main finding remains robust for oil price uncertainty measured by Shanghai Crude Oil Futures and West Texas Intermediate.

The effectiveness of crude oil futures hedging during infectious disease outbreaks in the 21st century

AbstractThis study analyzes the hedging effectiveness of crude oil futures during infectious disease outbreaks in the 21st century. The conditional volatility of crude oil markets experienced a greater shock during the coronavirus disease 2019 (COVID‐19) pandemic than during the severe acute respiratory syndrome and swine‐origin influenza A outbreaks. The sharp decline in the conditional correlation of crude oil markets following the onset of COVID‐19 increases the persistence of shocks to correlations, thereby diminishing the effectiveness of futures contracts as a hedging instrument.

Causal Relationship Between Spot Price & Future Derivative Prices – Case of Crude Oil

This research aims to examine the theoretical and empirical connections between current and future crude oil prices using cutting-edge econometric methods. It surveys existing literature and theoretical frameworks, exploring theories like arbitrage, market efficiency, and risk management techniques. The study then conducts a comprehensive empirical investigation using a large dataset, including data from extreme market volatility and other significant events in the financial world. Time series econometric methods like Granger causality tests, Vector Autoregression (VAR), and impulse response functions are used to capture the causal connections between spot and future prices. The analysis uncovers insights on lead-lag dynamics, volatility spillovers, and both short- and long-term causal impacts between current and future crude oil prices. Market fundamentals, geopolitical developments, and speculative behavior are also highlighted as influencing the causal relationships studied. The findings shed light on the link between future and current crude oil prices and provide valuable insights into the factors influencing these prices. This paper examines the relationship between future and spot crude oil prices, offering valuable insights for energy policy, risk management, hedging techniques, investment allocation, and governmental interventions.

How does bubble risk propagate among financial assets? A perspective from the BSADF-vine copula model

In this paper, the BSADF method is used to construct the bubble net value sequence of 11 mainstream financial assets, and the vine copula model is used to analyze the characteristics of the bubble-related structure. First, we use the BSADF method to measure the bubbles of various financial assets. The results show that since the outbreak of COVID-19, most types of assets have experienced a sharp bubble rise and their longest bubble period as central banks have implemented quantitative easing policies. Furthermore, this article reveals the path characteristics of bubble risk transmission in different assets using the vine copula model. The R vine copula is found to be best for analyzing the tail dependence structure. The S&P 500 and the international gold price occupy the hub position in the bubble dependence structure of various financial assets. The Shanghai Composite Index and the US Dollar Index can act as relays and accelerators in the transmission of bubble risk across the financial system. Finally, we explore the tail dependence structure between financial assets. The results indicate that gold can be a good hedge against the bubble risk of the Hang Seng Index and WTI crude oil futures. The COVID-19 pandemic and accommodative monetary policies by governments can create conditions for bubble risk to spread between assets such as Bitcoin and China’s 10-year Treasury bonds and between the US Dollar Index and the S&P 500.

How does China's crude oil futures affect the crude oil prices at home and abroad? Evidence from the cross-market exchange rate spillovers

This study explores the cross-market relations among the prices of China's crude oil futures launched by the Shanghai International Energy Exchange (INE), China's domestic and foreign crude oil spots, and the USD against the RMB (USD/CNY) exchange rate. We emphasize the research of whether and how the RMB-denominated INE crude oil futures affect the exchange rate spillovers to China's domestic and foreign crude oil markets. With the construction of a theoretical framework and a Time-varying Parameter Structural Vector Autoregression Model with Stochastic Volatility (SV-TVP-SVAR) model, we find that: (1) The INE crude oil futures prices have close linkages with China's domestic crude oil spot prices but have insignificant and unstable spillovers on international crude oil prices, indicating that INE crude oil futures still have limited outward influence. (2) More importantly, the oil price-exchange rate nexus is diversified under different crude oil pricing currencies. The USD/CNY exchange rate shocks have negative spillovers on international crude oil denominated by USD while having positive spillover effects on INE crude oil futures denominated by RMB. (3) The USD/CNY exchange rate also positively influences China's domestic spot market via the spillovers of INE crude oil futures.

Exploring the relationship between global economic policy and volatility of crude futures: A two-factor GARCH-MIDAS analysis

This study investigates the impact of global economic policy on the volatility of crude oil futures using a two-factor GARCH-MIDAS (Mixed Data Sampling) analysis. The core of the problem statement is the need to understand how global economic policy affects the volatility of crude oil futures, which has significant implications for investors, decision-makers, and market participants. The study aims to determine the correlation between critical global economic policy variables and crude oil futures volatility. This study combines the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework and the Mixed Data Sampling (MIDAS) method to create a Two-Factor GARCH-MIDAS model. This model thoroughly analyzes the long- and short-term dynamics between economic policy and crude oil futures volatility by incorporating macroeconomic and financial market data. The analysis covers a specific period from 2010 to 2021 and considers both prosperous and challenging economic times. The study's narrow geographic focus on a region with significant global economies and oil markets ensures a thorough representation of monetary and fiscal policies and changes in crude oil futures prices. The analysis provided insight into the complex link between volatility in crude oil futures and global economic policy. The results provide insightful information about how changes in interest rates, fiscal policies, and trade regulations affect the volatility of crude oil futures. The study advances knowledge of the connections between economic policy and energy markets, enabling investors and decision-makers in the international oil market to make more informed choices.

A Hybrid Forecast Model of EEMD-CNN-ILSTM for Crude Oil Futures Price

Crude oil has dual attributes of finance and energy. Its price fluctuation significantly impacts global economic development and financial market stability. Therefore, it is necessary to predict crude oil futures prices. In this paper, a hybrid forecast model of EEMD-CNN-ILSTM for crude oil futures price is proposed, which is based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Long Short-Term Memory (ILSTM). ILSTM improves the output gate of Long Short-Term Memory (LSTM) and adds important hidden state information based on the original output. In addition, ILSTM adds the learning of cell state at the previous time in the forget gate and input gate, which makes the model learn more fully from historical data. EEMD decomposes time series data into a residual sequence and multiple Intrinsic Mode Functions (IMF). Then, the IMF components are reconstructed into three sub-sequences of high-frequency, middle-frequency, and low-frequency, which are convenient for CNN to extract the input data’s features effectively. The forecast accuracy of ILSTM is improved efficiently by learning historical data. This paper uses the daily crude oil futures data of the Shanghai Energy Exchange in China as the experimental data set. The EEMD-CNN-ILSTM is compared with seven prediction models: Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), LSTM, ILSTM, CNN-LSTM, CNN-ILSTM, and EEMD-CNN-LSTM. The results of the experiment show the model is more effective and accurate.

Connectedness and hedging effects among China's nonferrous metal, crude oil and green bond markets: An extreme perspective

The interactions between nonferrous metal and crude oil markets have been wildly discussed in recent years. However, these interaction effects under extreme bearish and bullish market conditions are rarely investigated. This paper aims to quantify not only the extreme connectedness effects between China's nonferrous metal and crude oil futures markets, but also the hedging benefits of green bond on the nonferrous metal and crude oil futures portfolios. The empirical results show that China's nonferrous metal, crude oil and green bond markets are very closely connected under extreme bearish and bullish market conditions with no clear asymmetry. Moreover, the China's green bond can provide moderate hedging benefits to the nonferrous metal and crude oil futures portfolios even in extreme market environments, and these benefits are stable across different allocation methods.

Quantile Dependence between Crude Oil and China’s Biofuel Feedstock Commodity Market

This paper investigates the heterogeneous dependence between global crude oil futures and China’s biofuel feedstock commodities under different market conditions. Quantile-on-quantile regression and the causality-in-quantiles test are employed to capture comprehensive and informative relationships. The empirical results are as follows: First, there is a positive relationship between the returns on China’s biofuel feedstock commodities and crude oil. The effects are heterogeneous, conditional on the market regimes, where the impacts of the bearish/bullish crude oil market on biofuel feedstock commodity returns are significant when the commodity market in China is in a bearish/bullish state. Second, crude oil returns have reliable predictive power for the returns on China’s biofuel feedstock commodities under the average market condition and move in connection with the volatility of China’s biofuel-related commodity market in normal and bullish market conditions. Third, the risk reduction effectiveness of soybean and corn is significant, while for wheat, this reduction in portfolio risk is less apparent and enhanced, and the risk reduction effectiveness increases significantly during financial and oil crises. Overall, our findings will be helpful in understanding the heterogeneous interplay between global oil and China’s biofuel-related commodities and in evaluating portfolio diversification opportunities under different market conditions.

DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

Crude oil futures prediction plays an important role in ensuring sustainable energy development. However, the performance of existing models is not satisfactory, which limits its further application. The poor performance mainly results from the lack of data mining of economic models and the poor stability of most data analysis models. To solve the above problems, this paper proposes a new dynamic model ensemble transformer (DMEformer). The model uses three different Transformer variants as base models. It not only ensures the difference of base models but also makes the prediction results of base models not to appear disparity. In addition, NSGA-II is adopted to ensemble the results of base models, which considers both the modeling stability and accuracy in the optimization. Finally, the proposed model adopts a dynamic ensemble scheme, which could timely adjust the weight vector according to the fluctuation of energy futures. It further improves the reliability of the model. Comparative experiments from the perspective of single models and ensemble models are also designed. The following conclusions can be drawn from the experimental results: (1) The proposed dynamic ensemble method can improve the performance of the base model and traditional static ensemble method by 16% and 5% respectively. (2) DMEformer can achieve better performance than 20 other models, and its accuracy and MAPE values were 72.5% and 2.8043%, respectively. (3) The proposed model can accurately predict crude oil futures, which provides effective support for energy regulation and sustainable development.

Examining Rail Transportation Route of Crude Oil in the United States Using Crowdsourced Social Media Data

Safety issues associated with transporting crude oil by rail have been a concern since the boom of the U.S. domestic shale oil production in 2012. During the last decade, over 300 crude-oil-by-rail incidents have occurred in the United States. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on crude-on-rail routes and their associated risks is available to the public. To this end, this study proposed an unconventional way to reconstruct crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method linked the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments that those crude oil trains were traveling on. A shortest path-based method was applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their directional information. Validation of the inferred routes was performed using a public map and official crude oil incident data. The results suggested that the inferred routes based on geotagged photos had high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through several metropolitan areas of high population density, who were exposed to potential risk. These findings could improve situational awareness for policy makers and transportation planners. In addition, with the inferred routes, this study has established a good foundation for future crude oil train risk-analyses along the rail route.

Selective hedging strategies for crude oil futures based on market state expectations

This paper studies the ex-ante selective hedging strategies of crude oil futures contracts based on market state expectations and compares the hedging performances to the traditional minimum variance routine hedging strategies. The main advantage of the proposed method is that it achieves a trade-off between return and risk, rather than hedges risk at all costs. Specifically, we first use a multi-input Hidden Markov Model(HMM) to identify the market state, assess the market’s herding impact, and then integrate the findings of identification and measurement to forecast the price trend. We offer an adjustment criterion for the hedge ratios driven by GARCH22GARCH is the abbreviation of Generalized AutoRegressive Conditional Heteroskedasticity.-type models based on the anticipated market state. We conducted an empirical analysis to examine the hedging effect of WTI and Brent crude oil futures, the results indicate that the proposed state-dependent hedging strategies are superior to the traditional model-driven hedging strategies concerning the hedged portfolio based on four criteria. The robustness check reveals that the proposed hedging strategies still outperform in different market situation. The findings can help traders in the crude oil markets, and the methodology can be applied to other energy markets.

Sample frequency robustness and accuracy in forecasting Value-at-Risk for Brent Crude Oil futures

In this paper we examine how sensitive Value-at-Risk (VaR) forecasts based on simple linear quantile regressions are to the sampling frequency used to calculate realized volatility. We use sampling frequencies from one to 108 min for ICE Brent Crude Oil futures and test the out-of-sample performance of a set of quantile regression models using formal coverage tests. The results show that a one-factor model performs exceptionally well for most sampling frequencies used to calculate realized volatility. In comparison with the well-known Heterogeneous Auto-regressive Model of Realized Volatility (HAR-RV) and a quantile regression version of the HAR model (HAR-QREG), we also find that the one-factor model is much less sensitive to the sampling frequency used to calculate realized volatility.