Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices

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

Financing SMEs and business development as new post Covid-19 economic recovery determinants

Identifying the comovement of price between China's and international crude oil futures: A time-frequency perspective

Global financial uncertainties and China’s crude oil futures market: Evidence from interday and intraday price dynamics

China crude oil futures were officially listed in the Shanghai International Energy Exchange Center (INE) on March 26, 2018. The cross-correlation function of the return series of crude oil futures prices between INE and mature markets (WTI and Brent) was calculated. The result shows that the return series of the INE crude oil futures price had the strongest correlation with that of WTI and Brent when the interval was one day. Then, the R/S analysis, multifractal detrended fluctuation analysis (MF-DFA), and multifractal spectrum were used to analyze the fractal characteristics of the INE crude oil market. The results show that the returns of the INE crude oil price have significant multifractal characteristics. Compared with the mature crude oil futures markets (WTI and Brent), the multifractal characteristics of the INE crude oil futures market are weaker than the Brent market but stronger than the WTI market Lastly according to the multifractal spectrum, the risk of the INE crude oil futures market is less than WTI and Brent market

Multifractal characteristics analysis of crude oil futures prices fluctuation in China

This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results.

Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data

These days, the visual analysis of the literature (using the Web of Science as the data source) in the field of international swimming has been used to clarify the current status and patterns of research in this field. The data source of web of science provides a reference for the dynamic update of international swimming research. In this paper, we have used CiteSpace software and applied it to analyze the core literature related to international swimming from 1998 to 2020 in terms of overall literature measurement, core authors, source institutions, country distribution, discipline distribution, research hotspots, frontiers, and so on. We performed numerous experimental works for dynamic analysis of international swimming research using this tool. The findings indicate that international swimming research is in the second (growth) stage of the “Lepus curve,” and that there is a “head effect” in the distribution of journals (primarily in sports medicine). We obtained these large core cooperative networks, forming some high-production institutions as the core of large-scale cooperative networks and comprehensive university high-production institutions. In the future, we plan to focus on sports injury and rehabilitation, physiological characteristics, biomechanical analysis, and reaction in the future.

Dynamic Analysis of International Swimming Research Using CITESPACE

Allocation of pollution and carbon emission reduction targets for city clusters: Empirical evidence from China's Yangtze River Delta city cluster based on cost optimization

In this study, the multifractal detrended moving-average cross-correlation analysis (MF-X-DMA) is combined with the Mean-Semi-Variance model to establish a Mean-MF-X-DMA model. This model is used to construct portfolio strategies by the stocks with the larger market value in each sector of the Standard and Poor's 500 Index (SP secondly, the portfolio strategy based on Mean-MF-X-DMA model performs better than the traditional portfolio model, such as returns, Sharpe ratio, β coefficient and utility for different risk preference investors; finally, the risk will be lower when the time scale is smaller under the same expected returns.

A portfolio strategy of stock market based on mean-MF-X-DMA model

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