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The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.

This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.

This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.

This paper has research limitations in variable measurement and data selection.

This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.

The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

Growing attention of policymakers, governments and regulation authorities towards climate change and global warming has spurred the extensive need to carefully examine the current practices of green and sustainable finance. This study aims to provide a comprehensive analysis on the current state and future directions of green and sustainable finance through bibliometric analysis.

For extensive bibliometric analysis, the study comprises 1,413 documents published in peer-reviewed journals indexed in the SCOPUS database for the period ranging from 1990 to 2021.

The authors find that there are mainly three key areas of green and sustainable finance, which are largely addressed by the scholars following the given time. The key areas include socially responsible investments, green finance and climate finance that are in line with the previous studies and existing trends and practices prevailing in the business and corporate world.

The findings are important for policymakers, regulatory bodies, upcoming scholars, environmentalists and investors as findings of the study provide an effective framework for adopting sustainable strategies, to trade-off between profits and environmental hazards and to generate value from the green avenues of research and practice.

The study offers novel contributions to the existing literature in terms of comprehensively providing evidence of the current practices of green and sustainable finance. Meanwhile, significant implications for the prospective audience further refine the contribution of research.

Milk is one of the most produced, consumed and protected agricultural commodities worldwide. The purpose of this paper is to assess how trade-opening policies can foster food security in the Chinese milk sector.

The empirical evidence proposed in our paper is based on time series data from the National Bureau of Statistics of China (2019) and FAOSTAT (2020). Differences in income elasticity between urban and rural areas are estimated by OLS regressions. The data also provide empirical evidence to assess to what extent and to which countries China is resorting to meet its growing demand.

Per-capita milk consumption of Chinese is rising. The authors’ estimates show that milk income elasticity is higher in rural areas. China is also progressively increasing its dependence on imports. Producers who benefit the most are those from countries implementing trade-opening policies.

Other methods could be applied, by way of example, the gravitational model.

Trade agreements and the removal of barriers could be effective responses to protectionist pressures and to food security concerns.

The case examined is of particular interest as it intervenes on food security and safety.

The paper adds value and evidence to the effects of trade on food security in a country with limited and exploited natural resources addressing a health emergency and environmental concerns.