An analysis of the clustering effect of a jump risk complex network in the Chinese stock market

Abstract With the development of Chinese financial market, the correlation between stock price jump risks cannot be ignored. This study uses the complex network method to analyze the clustering effect of stock price jumps. Taking a sample of stocks from the CSI 300 Index, the realized jumps are extracted from the 5-minute high frequency data using the MinRV method. The authors use the Minimum Spanning Tree algorithm to construct a complex network of stock price jumps. It is found that there is a clear correlation among stocks in the entire jump network. The jump in manufacturing industry stocks plays the most important role in the network. The Modular Q function and the Fast Unfolding algorithm are used to divide the entire complex network and study the differences in jump correlation between different communities. The result shows that the correlation among the financial industry stocks is stronger, and a large fluctuation in the price of one financial stock can cause the price of another financial stock to fluctuate significantly.