Abstract
When the correlation in stock markets is higher than the development of market fundamentals, the network correlation will gradually evolve into systemic risk. Multivariate time series contains rich information of corresponding complex systems. We use complex network to analyze the market development trend and potential systemic risk. Specifically, we use the method of converting multivariate time series into complex networks to analyze financial time series and the underlying dynamic topological index to analyze the network transmission characteristics to explore the roles of different stock indices. Based on the network dynamics, we construct a systemic risk measurement model from the perspective of regional, financial and global stock indices respectively for verification. We also introduce the topological parameter model to measure the systemic risk and confirm that it is practically operational. Moreover, our model can provide more detailed and accurate information regarding systemic risk of stock markets. Locating different market function modules, we further analyze the correlations and systemic risk of different stock indices. We believe that our model can give more reasonable suggestions for effective early warning of market risk and support regulators and investors with rational decisions.