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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. [ABSTRACT FROM AUTHOR]

This paper constructs a China petroleum market volatility (CPMV) tracker based on Chinese newspapers from March 2018 to July 2021 for the first time. Then, we use the Generalized AutoRegressive Conditional Heteroskedasticity mixed‐data sampling (GARCH‐MIDAS) model to explore the explanatory ability of the CPMV tracker, and the results show that compared with other volatility trackers, the CPMV tracker has a superior ability to explain the volatility of Shanghai crude oil futures (SC). Further, we conduct a more detailed analysis of the CPMV tracker, and find that the CPMV tracker can reflect the information and market sentiment of SC, and needs to be taken into consideration when explaining SC volatility. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 shocked financial markets globally, including China's crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China's first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring. [ABSTRACT FROM AUTHOR]

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China's future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations. [ABSTRACT FROM AUTHOR]

The volatility of Shanghai crude oil futures prices is researched in this paper. The cusp catastrophe analysis of Shanghai crude oil futures price is based on the perspective of volatility influencing factors. Some important factors are selected based on commodity attributes and financial attributes, including certain China's macrofactors, such as producer price index and macroeconomic prosperity index. The principal component analysis is used to process the factors. The control variables of the cusp catastrophe model are extracted. The discriminant of the cusp catastrophe model and the principal component analysis are combined to determine the month of mutation. The three-dimensional renderings and plane projections of Shanghai crude oil futures price mutations in 2018 and 2019 are presented. The model is tested by comparing the time points of price sudden change nodes and emergencies. The results show that only nine factors can be used to roughly determine whether the Shanghai crude oil futures price has a sudden change. During the analysis period, the mutation months are May 2018, December 2018, February 2019, June 2019, and September 2019. It can be proved that the established model has certain feasibility and accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Sina Weibo and Twitter are the top microblogging platforms with billions of users. Accordingly, these two platforms could be used to understand the public mood. In this paper, the authors want to discuss how to generate and compare the public mood on Sina Weibo and Twitter. The predictive power of the public mood toward commodity markets is discussed, and the authors want to solve the problem that how to choose between Sina Weibo and Twitter when predicting crude oil prices. Design/methodology/approach: An enhanced latent Dirichlet allocation model considering term weights is implemented to generate topics from Sina Weibo and Twitter. Granger causality test and a long short-term memory neural network model are used to demonstrate that the public mood on Sina Weibo and Twitter is correlated with commodity contracts. Findings: By comparing the topics and the public mood on Sina Weibo and Twitter, the authors find significant differences in user behavior on these two websites. Besides, the authors demonstrate that public mood on Sina Weibo and Twitter is correlated with crude oil contract prices in Shanghai International Energy Exchange and New York Mercantile Exchange, respectively. Originality/value: Two sentiment analysis methods for Chinese (Sina Weibo) and English (Twitter) posts are introduced, which can be reused for other semantic analysis tasks. Besides, the authors present a prediction model for the practical participants in the commodity markets and introduce a method to choose between Sina Weibo and Twitter for certain prediction tasks. [ABSTRACT FROM AUTHOR]

This paper investigates co-movements among the Chinese stock market, Shanghai International Energy Exchange (INE) crude oil futures and West Texas Intermediate (WTI) crude oil futures. We use Copula models to capture tail dependencies and employ the VAR-BEKK-GARCH model to examine the direction of volatility spillovers. We find that there are positively time-varying dependency relationships among the three markets. Compared with the corresponding upper-tail dependencies, the lower-tail dependencies were larger before the COVID-19 pandemic while relatively weaker after the breakout of the pandemic. Before the COVID-19 pandemic, there was only a statistically significant volatility spillover from WTI crude oil future market to the INE crude oil future market. After the breakout of the COVID-19 pandemic, there were statistically significant volatility spillovers in the two pairs of markets, namely, the WTI–INE and Chinese stock–WTI. However, we only find statistically significant evidence of unidirectional volatility spillover from the Chinese crude oil future market to the Chinese stock market during the pandemic. [ABSTRACT FROM AUTHOR]

Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its co-movements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases. [ABSTRACT FROM AUTHOR]

Applying recently developed Diebold and Yilmaz (2012) spillover index, this paper investigates the oil–stocks returns and volatility connectedness with weekly data between January 14, 2000 and March 31, 2017. For the purpose of robustness, we have both used WTI and Brent oil prices. Sensitivity of overall spilover index is also examined using different lag‐structures and different forecast horizons. The empirical results are qualitatively similar either when WTI or Brent oil prices are used to examine the spillover amongst series under consideration. Specifically, the spillover index value for WTI and Brent, respectively, is 71.60 and 72.32%. We show that total spillover connectedness, as measured by a rolling‐window approach, has dynamic and volatile characteristics in returns and volatility series across major markets. Last but not least, we find from the net spillover analysis that NYK, SPTSX, IBOV, MICEX, SENSEX, Shanghai, TOP40 and WTI stock markets are net receiver of return spillover, whereas SPX, DAX, UKX, FTSEMIB and CAC40 are the net contributors. [ABSTRACT FROM AUTHOR]

The relationship between markets has always been a topic of heated debate among scholars from various countries. One of the most important concerns is the need to model the relationships between the crude oil market and other markets. Based on daily return observations from 2018 to 2019, we apply a GARCH-vine-copula approach to probe the linkage between Shanghai crude oil futures, stock, foreign exchange, and gold markets. We find that obvious tail dependencies do exist between these markets. And the crude oil futures market occupies a dominant position. Moreover, when the Shanghai crude oil futures market is taken as the known condition, the links between different markets reduce to some extent. Finally, value at risk results denote that the risk of the Shanghai crude oil futures market is relatively high, but portfolio investment can effectively reduce the risk. Moreover, the model fitting results at different confidence levels have passed the Kupiec backtest, indicating that the model in this paper fits the relationship between these markets well. [ABSTRACT FROM AUTHOR]

In the current paper, we investigate the problem of how do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak. Specifically, given that noise, conditional higher moments and asymmetric tail dependence may exist in crude oil markets, a Wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method is proposed to construct hedging portfolios in crude oil markets during the epidemic period. Based on the in-sample and out-of-sample results, the hedging roles of Brent futures and Shanghai crude oil (SC) futures for light and medium crude spots after the COVID-19 outbreak are further researched. The empirical results demonstrate that noise, conditional higher moments and asymmetric tail dependence do exist in crude futures and spots, which have impact on the precision of modeling results. Secondly, the Wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method outperforms all control groups, obtaining the best in-sample and out-of-sample hedging effectiveness. Finally, it is reported in the in-sample and out-of-sample hedging results that Brent is the optimal futures to hedge light oil, while SC is the optimal futures to hedge medium oil. The paper provides substantial recommendations for policymakers and investors. • A Wavelet denoising-GARCHSK-SJC Copula method is proposed to estimate hedge ratio. • The hedging roles of Brent and SC futures after the COVID-19 outbreak are researched. • Noise, conditional higher moments and asymmetric tail dependence are discovered. • The Wavelet denoising-GARCHSK-SJC Copula method outperforms all control groups. • Brent and SC are the optimal futures to hedge light and medium crude spots, respectively. [ABSTRACT FROM AUTHOR]

With the continuous increase of China's foreign-trade dependence on crude oil and the accelerating integration of the international crude oil market and the Chinese finance market, the spillover effect of international oil price fluctuation on China's stock markets increasingly attracts the attention of the public. In order to explore the impact of international oil price fluctuation on China's stock markets and the time-varying spillover differences of industry sectors, this study proposes three research hypotheses and constructs a multi-time scale analysis framework based on wavelet analysis and a time-varying t-Copula model. In this paper, we use the Shanghai Composite Index as the representative of a general trend of the stock market, and we use the stock index of the China Securities Industry as the counterpart of industrial sectors. Based on the data from 5 January 2005 to 31 May 2020, this paper measures and analyzes the spillover effect of international oil price fluctuation on China's stock markets, under different volatility periods. The results show that, firstly, the spillover effect of international oil price fluctuation on the Chinese stock markets is different. In the short and medium volatility period, the changes in international oil price are ahead of the changes in the Chinese stock markets, while the latter is ahead of the former under long-term fluctuations. Secondly, the spillover effect of international oil price fluctuation on China's industry stock indexes is persistent. As the time scale increases, the tail dependency will increase. Finally, the impact of risk events aggravates the volatility of the stock markets in the short-term, while the mid- to long-term impact mainly affects the volatility trend. Investment risk control can make overall arrangement on the basis of the characteristics of oil price impact under different fluctuation stages. [ABSTRACT FROM AUTHOR]

The time-varying spillover effect of China's crude oil futures market and new energy market has an important impact on promoting the green development of China's economy. This study uses the dynamic connectedness method based on DCC-GARCH model to analyze the time-varying spillover effects between Shanghai crude oil futures and various industries in new energy markets. The results show that there was a stable volatility correlation and high degree of connectedness between Shanghai crude oil futures and the new energy stock market. The new energy vehicle and energy storage industries were driving the market, while Shanghai crude oil futures and both wind power and photovoltaic industries were driven by the market.With the analysis results, the study provides scientific policy recommendations for the development of China's crude oil futures market and new energy market, which are expected to contribute to the sustainable development of the energy market. [ABSTRACT FROM AUTHOR]

We examine the price discovery performance of China's crude oil futures traded on the Shanghai International Energy Exchange (INE) for the spot prices of 19 types of deliverable and nondeliverable Asian crude oil. We find evidence for the INE crude oil futures price discovery function even at the early stage for almost all the deliverable crudes and some nondeliverable crudes. Both the INE crude oil futures price and the spot price significantly contribute to the price discovery process, with substantially time‐varying informational roles. While the price discovery performance was severely damaged around the period of COVID‐19 pandemic shock intensification in China with the temporary cancellation of nighttime trading, it improved to some extent after China started the recovery from the shock. But such improvement deteriorated drastically and disappeared since early 2021. Further analysis reveals that both economic fundamentals (e.g., the warehouse inventory) and trading‐related characteristics of the futures market are significant determinants of the price discovery performance. The overall findings imply that the INE crude oil futures market has evolved into a useful and important information source in pricing Asian crudes, and is on the path to emerge as an Asian benchmark. [ABSTRACT FROM AUTHOR]

The dominant position in crude oil pricing affects a country's energy and economic security. We construct different time-series forecasting models to analyze the long-term trends of Shanghai crude oil futures and guide current price trading. First, we built a classic ARIMA model as the benchmark. It is a good choice for short-term investors but does not apply to long-term trend prediction. We propose an improved HW-Prophet model that can maximize its advantages in selecting and reconstructing mutation points and reduce the forecast error by approximately 22%. The Prophet model is then introduced into the prediction of futures price series, and the Haar wavelet and KS (HWKS) algorithm and sliding window are used to optimize the change-point selection of the Prophet model. Finally, we created different combinations of long short-term memory (LSTM) and HW-Prophet models based on residual correction and optimal weighting. The results showed that the combined residual correction method was unsuitable for the two single models in this study. Because the residual sequence predicted by the HW-Prophet model is similar to white noise, it is difficult for LSTM to learn the effective dynamic laws from it. By minimizing the error sum of squares to solve the optimal weight coefficients and combining the two models linearly, the prediction advantages of a single model in different intervals can be effectively transmitted, and the prediction accuracy is greatly improved. The HW-Prophet-LSTM combination model constructed in this study based on the optimal weight is currently one of the best models for predicting the long-term trends of Shanghai crude oil futures. [ABSTRACT FROM AUTHOR]

The novel coronavirus COVID-19 has severe consequences for countries around the globe. The purpose of this study to find out the impact of COVID-19 on China's sectoral indices in the short and medium run. This research investigates the impact of COVID-19 on stock exchange index, exchange rates, money lending, and oil prices in the Chinese economy in the short and medium run by using secondary data of different sectoral sectors. The outcomes show that COVID-19 has strongly influenced these sectors. The Shanghai stock exchange is showing a decline in transactions due to this pandemic in the short run. But in the medium run, the outcome shown that Shanghai stock exchange has upward trend. The pandemic has reduced exchange rate of Chinese RMB to US dollar in short-run, but in medium run, Chinese RMB has gained appreciation against US dollar. [ABSTRACT FROM AUTHOR]

This study examines how returns and volatility of future contracts for Brent crude oil (Brent), West Texas Intermediate crude oil (WTI), Henry Hub natural gas, and Newcastle thermal coal impacts industries in China. Using the firm‐level data of 3,750 stock listings across both Shanghai and Shenzhen stock exchanges, segregated into 138 subindustries under the Global Industry Classification Standard, this study finds evidence that crude oil futures have the most significant influence. Further analysis suggests that stock returns of oil‐related companies are more closely align to Brent and WTI's futures returns following China's key oil pricing reform on March 27, 2013. Overall, Chinese industries are also more exposed to global crude oil futures volatility after this event. [ABSTRACT FROM AUTHOR]