Based on the synergetics perspective, this paper constructs a composite system of non-ferrous metal futures and stock prices, using MATLAB to analyze the data of 3405 trading days from 2004 to 2018 in China. The empirical results show that non-ferrous metal stock prices are generally more orderly than futures prices in the selected period; the price discovery function of aluminum futures is worse than that of copper and zinc; and the 2008 financial crisis has an indelible negative impact on the coordination of China's non-ferrous metals futures market. Finally, this paper discusses whether there are representative metals in the non-ferrous metal market, and makes a brief summary.

In recent years, the Chinese capital market has suffered several violent shocks, and the characteristics of systemic risk contagion across industries and markets have become increasingly important. It brings great potential danger to the stability of financial markets. Therefore, exploring the risk spillover among the real sectors has gradually attracted the attention of scholars. This paper examines the cross-industrial tail risk spillover network in the Chinese financial market. The characteristics and the dynamic contribution of each industry in the tail risk transmission chains are explored. We use the ∆CoES-ENGDFM-LVDN model based on monthly data from 2006 to 2020 to measure the tail risk of 28 industries in China and form a cross-industrial tail risk spillover network. The results show that different industries have different levels of spillover and importance in the network. Tail risk mainly spills over from the nonfinancial sector to the financial sector. The nonbank financial industry is the main recipient of tail risk spillover and is becoming progressively more important in the risk network. In addition, with the promotion of industrial structure, emerging industries such as communications, computers, and health care have begun to play more important roles in the tail risk spillover network in China. This paper not only enriches the research in the areas of tail risk spillover and systemic risk, but also has implications for regulators to maintain financial stability and prevent financial risks.

Draining liquidity is one of the main reasons for the financial crisis. Therefore, exploring the link between various indicators of banks and bank liquidity has great significance for studying bank liquidity risk. The article applies principal component to analysis the main components are extracted from the perspectives of assets, loans, deposits, borrowings and reserves. Then the Bayesian network is used to construct a network of links between liquidity and indicators based on this.