Analysis of the Volatility of CSI 300 Stock Market Based on Complex Networks
Minna Sun
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Haisong Cao *
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Hengyan Li
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
*Author to whom correspondence should be addressed.
Abstract
Aims/Objectives: To analyze the volatility and structural characteristics of the CSI 300 stock market using complex network theory, and to reveal the evolution of market structure under different macroeconomic environments.
Study Des: An empirical study employing complex network analysis and an innovative double-layer network modeling approach to capture dynamic, phase-dependent structural evolution.
Place and Duration of Study: Constituent stocks of the CSI 300 index, covering the period from October 1, 2022, to October 1, 2024.
Methodology: We constructed a double-layer network model to analyze the interconnection relationships and structural characteristics of constituent stocks across different time periods. The methodology included the threshold filtering method, weight matrix construction, and centrality analysis to examine market volatility and risk propagation paths.
Results: The results show that during the G1 stage (Russia-Ukraine conflict and Fed interest rate hike), the market relied on the stability of the financial sector, exhibiting high network concentration. During the G2 stage (post-pandemic consumption recovery), the consumption and manufacturing sectors gained prominence, and the market structure gradually shifted towards an internally-driven model with improved risk resistance. The modularity increased from 0.2667 in G1 to 0.3878 in G2, indicating a more dispersed risk distribution.
Conclusion: The market network structure evolves dynamically with the macroeconomic environment. The double-layer network analysis reveals enhanced market resilience in the G2 stage, with modularity, density, and clustering coefficient changing by 17.05%, 14.55%, and 11.42%, respectively, reflecting the market’s transition from risk concentration to a diversified equilibrium. These findings provide practical insights for risk management and policy formulation, offering regulators and investors valuable tools for monitoring market stability.
Keywords: Double-layer network, CSI 300 index, threshold filtering method, weight matrix, Volatilitya