Highlights Volatility spillover effects are examined in "The B&R" currency market. "The B&R" system spillover index reflects some sudden regional crises. COVID-19 has affected systemic stability and the influence of RMB.

The stock market is a complex system with unpredictable stock price fluctuations. When the positive feedback in the market amplifies, the systemic risk will increase rapidly. During the last 30 years of development, the mechanism and governance system of China’s stock market have been constantly improving, but irrational shocks have still appeared suddenly in the last decade, making investment decisions risky. Therefore, based on the daily return of all a-shares in China, this paper constructs a dynamic complex network of individual stocks, and represents the systemic risk of the market using the average weighting degree, as well as the adjusted structural entropy, of the network. In order to eliminate the influence of disturbance factors, empirical mode decomposition (EMD) and grey relational analysis (GRA) are used to decompose and reconstruct the sequences to obtain the evolution trend and periodic fluctuation of systemic risk. The results show that the systemic risk of China’s stock market as a whole shows a downward trend, and the periodic fluctuation of systemic risk has a long-term equilibrium relationship with the abnormal fluctuation of the stock market. Further, each rise of systemic risk corresponds to external factor shocks and internal structural problems.

No abstract

Total electricity consumption is a barometer of a country’s economy. Long-term forecasting of total electricity consumption in the whole society can effectively track a country’s economic development and monitor the implementation of energy conservation and emission reduction policies. How to effectively forecast the long-term total electricity consumption is an important topic in the academic and industrial fields. The combined model of kernel principal component analysis (KPCA) and linear regression (LR) proposed in this paper can accurately predict the changes in total electricity consumption over time, even if the sample size is small. Meanwhile, the model results have strong interpretability and practical value. Further, through the correlation analysis of principal components obtained from KPCA dimensionality reduction, this paper finds that the most important features affecting the total electricity consumption are the economy feature and production efficiency feature. Finally, this paper predicts that China’s total social electricity consumption will reach 1.83 trillion KWH in 2035, which is more optimistic than the prediction of Oxford experts, which is consistent with the reality that China has achieved an overall victory in the fight against COVID-19.

No abstract