Oil Futures Market Research Articles

Investor sentiment and machine learning: Predicting the price of China's crude oil futures market

Sentiment analysis technology has made it possible to precisely calculate the daily reactions and opinions of investors, which has been found to have a significant influence on financial asset pricing. Thus, in this study, we examine the impacts that predictive power investor sentiment has over the price of China's crude oil. We first constructed investor sentiment indexes of China's crude oil futures based on specific economic variables and comments found on one of the most active online financial forums. Then, five popular machine learning tools were utilized to generate predictions. According to our findings, the long short-term memory model combined with the composite sentiment index performed the best due to a lower rate of prediction errors and greater directional accuracy for time-series forecasting of one-day-ahead prices. In this way, this study could aid researchers to more effectively investigate the energy sector which is rapidly changing and highly speculative in nature

Time-varying spillover effects and investment strategies between WTI crude oil, natural gas and Chinese stock markets related to belt and road initiative

This paper investigates the return volatility spillover effects and the dynamic relationships among WTI crude oil futures, Natural Gas futures, and the Chinese stock markets related to Belt and Road initiative employing the method of Diebold and Yilmaz (2012, 2014) based on TVP-VAR model and generalized forecast error variance decomposition. Our empirical results show that there exists a high interdependence among all analyzed assets, and the total volatility spillover has a sharp increase under the major crisis events. On average, WTI, GAS and the stock markets of FM, AGR and COM are the net receivers of systematic shocks, while the stock markets of EU, HSR and INF are the net transmitters of systematic shocks. Besides, we also calculate the hedge ratios, the optimal portfolio weights and the corresponding hedging effectiveness based on DCC-GARCH t-copula model. We find that WTI crude oil and Natural Gas futures are cheap hedging tools. When investing a small part in WTI crude oil future market and a large part in the analyzed stock markets, or investing a small part in the analyzed stock markets and a large part in Natural Gas future market, high hedging effectiveness could be achieved.

Newspaper-based equity uncertainty or implied volatility index: new evidence from oil market volatility predictability

ABSTRACT This article investigates two equity-oriented market volatility measures, newspaper-based equity market volatility (EMV) index and COBE-implied volatility index (VIX), which contain more predictive contents for the oil futures market based on a Markov regime-switching method. We find that both EMV and VIX contain valuable content for the oil market. Interestingly, the predictive performance of the model containing both EMV and regime switching is better than that of the model containing VIX and regime switching, further showing that EMV can be more powerful than VIX for oil futures volatility when considering regime switching. Our study tries to provide new insight into the oil futures market.

The influence of the Shanghai crude oil futures on the global and domestic oil markets

The newly established Shanghai crude oil futures market provides a natural experimental setting for studying the price cointegration and causal relationships between the Chinese domestic benchmark (Daqing) and international benchmarks (West Texas Intermediate and Brent). We find a significant change in the relationships between domestic and global oil prices before and after the launch of the Shanghai market. Moreover, using a Johansen cointegration test and a vector error correction model, we find long-term equilibrium and short-term relationships between the Shanghai market and global benchmarks. Shanghai crude oil futures prices also play a leading role in the price discovery of domestic crude oil spot prices, reflecting their dominant influence on the domestic market. The prices of the domestic crude oil market are thus becoming less influenced by global benchmarks but more influenced by the Shanghai crude oil futures market. The examined results of dynamic ARDL simulations model support the long run and short run relations between the Shanghai market and global benchmarks. The results also shows that the Shanghai crude oil futures market has response lags relative to those benchmarks.

Extreme Return Spillovers and Connectedness between Crude Oil and Precious Metals Futures Markets: Implications for Portfolio Management

Extreme Return Spillovers and Connectedness between Crude Oil and Precious Metals Futures Markets: Implications for Portfolio Management

Hedging behaviour in China's crude oil futures market

Hedging behaviour in China's crude oil futures market

China's Crude oil futures forecasting with search engine data

In this paper, we used the Baidu feed index to quantify the valuable information from the search engine, and proposed a China's crude oil futures price forecasting model with feed index. The empirical analysis confirms that the feed index, which characterizes the investor attention of participants in China's crude oil futures market, provides valuable information for internet concerns and investor sentiment, and has significant impact on China's crude oil futures price forecasting. We found that the time lag between the search query results and the crude oil futures price changes, which mostly lies within the shorter time range. The forecasting model with the search engine data would produce forecasts with superior performance.

Forecasting oil futures realized range‐based volatility with jumps, leverage effect, and regime switching: New evidence from MIDAS models

AbstractThis study adds ample evidences on forecasting oil futures realized range‐based volatility (RRV) in the mixed data sampling (MIDAS) framework. Considering the features and frequent extreme risk in oil futures trading in practice, we investigate the effect of jumps, leverage effect, and regime switching. The results highlight the great importance of combing jumps, leverage effect, and regime switching simultaneously in oil futures RRV forecasting, which still holds the best predictability during a highly volatile state. Moreover, considering regime switching can also improve forecast accuracy for longer forecasting horizons such as weekly, biweekly, and monthly. The conclusions are robust to various settings and criteria. Our findings are essential for oil enterprises and quantitative investors to have a good command of crude oil futures market characteristics and achieve risk aversion.

Research on Time-Varying Two-Way Spillover Effects Between Carbon and Energy Markets: Empirical Evidence From China

With the improvement of China’s carbon emission trading system, the spillover effect between carbon and energy markets is becoming more and more prominent. This paper selects four representative pilot carbon markets, including Beijing (BEA), Guangdong (GDEA), Hubei (HBEA) and Shanghai (SHEA). And three representative energy markets, including Crude Oil Futures (SC), power index (L11655) and China Securities new energy index (NEI). Combining the rolling window technology with DY spillover index, set a 50-weeks rolling window to measure the spillover index, and deeply analyze the time-varying two-way spillover effect between China’s carbon and energy markets. The results show that the spillover effect between China’s carbon and energy markets has significant time variability and two-way asymmetry. The time-varying spillover effect of different carbon pilot markets on the energy market has regional heterogeneity. The volatility spillover effect of Beijing and Shanghai carbon markets mainly comes from the crude oil futures market, Guangdong carbon market mainly comes from the new energy market, and Hubei carbon market mainly comes from crude oil and electricity market. The above research results contribute to the prevention of potential risk spillover between carbon and energy markets, which can promote the establishment of China’s unified carbon market and the prevention of systemic financial risks in energy market.

The impacts of COVID-19 crisis on spillovers between the oil and stock markets: Evidence from the largest oil importers and exporters

This study examines the multiscale spillovers and nonlinear causalities between the crude oil futures market and the stock markets of the United States (US), Canada, China, Russia, and Venezuela before and during the COVID-19 pandemic. Using the wavelet coherency method, we find strong co-movement between the oil futures market and these five stock markets, particularly from March 2020 to May 2020 (initial period of the COVID-19 outbreak) at high frequency. Furthermore, we find positive co-movements at low frequency during the overall COVID-19 period. This finding suggests that the bearish trend of stock markets is associated with a downward movement in oil prices. Using the wavelet-based Granger causality approach, we find that the oil and stock indices have less co-movement on a smaller scale but greater movement on a larger scale across all periods. As an exception, the Russian market is significantly influenced by oil prices, even on a small scale, before the COVID-19 period, but not after the beginning of the pandemic. We also find effects in the opposite direction—the Canadian and U.S. markets influence oil prices on a small scale during the COVID-19 period, an effect that is not visible for the U.S. market in the pre-COVID-19 sample. The results also show a significant bidirectional causality from oil to stock markets and vice versa during Russian-Saudi oil price war at high scale. Furthermore, we find that investors should hold more oil futures than stock shares in their portfolios for all periods. This evidence confirms that oil instruments are important for hedging during normal periods and act as safe-haven assets during crisis periods. We observe that the U.S. and Canadian stock markets were more affected by oil price shocks than were other countries.

Study on the correlation between Crude oil price and new energy stock price in China and America

In today's new world situation, the consumption structure of energy is constantly changing. All countries attach importance to the use of new energy, vigorously promoting the development of new energy-related industries.Traditional energy and new energy are interchangeable, so there is a complex relationship between crude oil futures market and new energy stock market. China, as an economy with strong energy demand and high dependence on oil, will be affected by changes in oil futures prices. America's new energy policy has two striking sides. On the one hand, due to the lack of consensus, the US has so far failed to come up with new energy development plans and targets at the national level. On the other hand, a series of supportive policies launched by the federal and local governments have enabled the U.S. wind and solar industries to maintain a high growth rate in recent years. In view of this, the research takes WTI crude oil price, Zhongzheng New energy Index and China crude oil price as the research object, analyzes the interaction among them by using VAR model and GARCH model, and predicts the volatility of crude oil price and new energy stock price.

Does the Launch of Shanghai Crude Oil Futures Stabilize the Spot Market? A Financial Cycle Perspective

Based on the examination of price discovery between Shanghai crude oil futures and the spot market, this paper explores whether the introduction of Shanghai crude oil futures can play a stabilizing role in the spot market, alleviating the impact of the financial cycle risk on the crude oil market from March 2018 to December 2019. The results show that there is only a uni-directional relationship of the spot price to futures price, and spot plays a leading role in price discovery. The risk of the financial cycle will increase the volatility of spot price, and the introduction of crude oil futures market can increase the impact of the financial cycle on the spot market. The additional research on the microcosmic mechanism of Shanghai crude oil futures indicates that crude oil futures market mainly influences the spot market fluctuation through the behaviour of traders: speculation increases price volatility in the spot market, which is more pronounced in the high volatility of the financial cycle as oppose to hedging transaction.

Oil futures volatility predictability: Evidence based on Twitter-based uncertainty

This paper explores the influence of the Twitter-based uncertainty index on oil futures market volatility. The Twitter-based Market Uncertainty (TMU) index, based on the novel Markov-regime GARCH-MIDAS model, may significantly improve prediction accuracy for oil futures volatility. Moreover, the TMU was still useful in predicting oil volatility during the COVID-19 pandemic. Furthermore, when the alternative Twitter-based uncertainty index, Twitter-based Economic Uncertainty (TEU), is adopted, these results are also robust. This paper highlights the importance of the Twitter-based uncertainty index for oil futures market.

The state-dependent trading behavior of banks in the oil futures market

We study the state-dependent trading behavior of financial institutions in the oil futures market, using structural vector autoregressions with Markov switching in heteroskedasticity. We consider two states of the world: tranquil and turbulent. We decompose the observable time-varying price volatility during the period 2006M6–2016M5 into changes in the slopes of traders’ demand curves and into changes in the variability of their demand shocks. We find that the downward-sloping demand curve of intermediaries steepens significantly during crises times and that the variance of their demand shocks doubles. These findings suggest that the futures pricing of financial institutions is highly nonlinear and raises the hedging costs of producers and consumers of oil when volatility is high.

Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting

The launch of the China's Shanghai International Energy Exchange (INE) oil futures market in 2018 has shed new light on the role of China in international crude oil market. Understanding the dynamics of the newly arrived RMB denominated crude oil futures market not only facilitates the international market participants in risk management and hedging, but also provides helpful information for policy-makers, especially for those from emerging countries, to financialize its energy market, along with the currency internationalization and financial market liberalization. However, the literature on the China crude oil futures is quite scant compared with abundant literature on international benchmarks. In this regard, we make attempts to capture the dynamics of the China crude oil futures by: (1) adopting Markov switching analysis to uncover the regime switching of the INE crude oil futures market; (2) investigating the dynamic connectedness of INE, WTI, and Brent crude oil futures; (3) forecasting the realized volatility of INE crude oil futures. The results have shown that: (1) the outbreak of global pandemic at the beginning of 2020 has switched the crude oil futures market from a stable regime to a volatile regime; (2) the increasing financial uncertainty originated from the world, U.S., other advanced countries and emerging countries could significantly negatively affect the movement of crude oil futures. However, China suffers the least; (3) the dynamic conditional correlations between INE vs WTI, and INE vs Brent are high but lower and more volatile than that of WTI vs Brent; (4) Brent crude oil futures contribute to improving the accuracy of volatility forecasting of INE crude oil futures; and more importantly (5) we highly recommend carrying out volatility forecasting by incorporating intraday realized measures into mixed data sampling approach, in particular, when intraday transactions present extremely different behavior across the time.

The Relationship between Crude Oil Futures Market and Chinese/US Stock Index Futures Market Based on Breakpoint Test.

Combined with the B-P (breakpoint) test and VAR–DCC–GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relationship in the mean between WTI crude oil futures market and Chinese stock index futures market or US stock index futures market. The relationship in mean between WTI crude oil futures prices and S&P 500 stock index futures, or CSI 300 stock index futures is weakening. Meanwhile, there is a decreasing dynamic conditional correlation between the WTI crude oil futures market and Chinese stock index futures market or US stock index futures market after the breakpoint in the price series. The Chinese stock index futures are less affected by short-term fluctuations in crude oil futures returns than US stock index futures.

Multi-WRNN model for pricing the crude oil futures market

In this paper, we introduce a multi-factor wavelet-based deep recurrent neural network (Multi-WRNN) model for more accurate pricing of the crude oil future market. This model is capable of including several key factors (e.g. stock-change and refinery capacity utilization rate) flexibly. The Multi-WRNN model enables us to classify the time series of the key factors into stationary and non-stationary. Also, the model provides a dynamical system for predicting the variation mean, volatility, and value of non-stationary key factors which have high influences on market in crisis time, and consequently, can be applied in pricing of the crude oil market. The model uses a decomposition method to efficiently predict the volatilities time series. We compare our results with empirical mode decomposition (EMD) and the discrete wavelet transforms (DWT). Then, according to locality of support property of DWTs, including B-spline reverse subdivision, we use them to decompose the original high resolution volatility time series into a lowresolution time series and several details in various resolution levels. Then all decomposed time series from the DWT multiresolution process are fed to a deep recursive neural network (DRNN) model. Moreover, we analyze and compare different DWTs, including B-splines with various degrees, and two of Daubechies wavelets. Since the locality of the operations is essential, we consider only compact supported wavelets. Also, we analyze the effect of all given key factors in the performance of our pricing model. Finally, the results of Multi-WRNN model is compared with the conventional models, such as stochastic two-factor, ARIMA, FFNN-GARCH, and GARCH which show that the Multi-WRNN model with all seven key factors as inputs of the network outperforms the other models.

The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market

This paper is the first to comprehensively investigate the causality between the crude oil futures market and investor sentiment under extreme shocks. By dividing the data into various pairs of extreme positive, extreme negative and normal shocks, we introduce an extended Granger causality approach in the time and frequency domains. Although weak evidence of Granger causality is discovered, we find evidence of strong causality running from extreme sentiment to crude oil futures, which has not been documented in previous literatures. Moreover, the crude oil futures market and investor sentiment show dynamic causality at different frequencies, while the extreme shock-related causal linkages from the frequency perspective illustrate that short-term causality is more significant than medium- and long-term causality. Finally, we find evidence that crude oil futures are more strongly affected by negative extreme shocks than by positive extreme shocks. Our findings call for investors to make better risk management decisions and for policymakers to design better macroeconomic and energy policies.

An Analysis of the Causes of Fluctuations in the Futures Price of Fuel Oil in China Using the VECM Model

Purpose - This paper analyze the causes of fluctuations in the futures price of fuel oil in China by dividing them into domestic and foreign factors. In particular, this study aims to compare and analyze the impact level of the world’s three major crude oil futures markets on China’s fuel oil futures market. Design/Methodology/Approach - This paper first identifies the long-term equilibrium relationship between Chinese fuel oil futures price and each influencing factor through Johansen’s cointegration test. It then analyzes the short-term relationship through the impulse response function and variance decomposition of prediction errors by setting up a VECM (Vector Error Correction Model) model. The variables used were China’s fuel oil futures price, crude oil imports, crude oil production, light oil production, WTI (West Texas Intermediate) crude oil futures price, Brent crude oil futures price and Dubai crude oil futures price. Data consisted of monthly statistical data from January 2006 to December 2019. Findings - Based on cointegration test resuts, it was found that there is a long-term equilibrium relationship among the variables. As a result of variance decomposition analysis of the impact response function and prediction error, it was found that the light oil production in domestic factor has the greatest effect on China’s fuel oil futures price. In terms of foreign factors, Dubai crude oil futures price has the greatest effect on China’s fuel oil futures price. Research Implications - China is seeking a stable import of energy resources through the systematization of the oil futures market, and furthermore, there seems to be an intention to enhance China’s influence in the international oil market. Like China, Korea mainly imports and consumes Middle Eastern crude oil, so these results will have certain implications for Korea.

Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method

This paper analyzes dynamic volatility spillovers between four major energy commodities (i.e., crude oil, gasoline, heating oil and natural gas) in the oil-natural gas future markets. We construct a time-varying spillover method by combining the TVP-VAR-SV model and the spillover method of Diebold and Yilmaz (2009, 2012, 2014). We use the spillover method to obtain time-varying total, directional and pairwise volatility spillover indices. Our results summarize as follows: (1) The volatility spillover indices present peaks and troughs during some periods, such as shale gas revolution, financial crisis, and oil price crash; (2) After the U.S. shale gas revolution, the size of volatility spillover from natural gas future market has reduced sharply, but volatility doesn't decouple from the other three oil future markets; (3) The directional spillover is asymmetric. The crude oil and heating oil futures market are main net transmitter of volatility risk information, while the gasoline and natural gas futures markets are the net receiver; (4) For natural gas future market, the pairwise volatility spillover from crude oil future market has the most significant influence.