Analysis of topological properties of complex network of Chinese stock based on Copula tail correlation

Due to the fact that Pearson correlation coefficient could not reflect the nonlinear correlation of stock return, this paper uses the tail correlation coefficient to construct the complex network and analyzes the network structure of Chinese stock market. The results show that the network of Chinese stock market shares the small-world and scale-free characteristics, and Hub nodes mainly focus on finance, manufacturing and architecture industry, reflecting the importance of these industries in Chinese economy. Because of the close relation among Chinese stocks, information can be transmitted efficiently. Communities show such characteristics: the second industry cluster and the tertiary industry scattered. The networks of Chinese stocks have a strong stability against random attacks, while the network has a weak robustness against intentional attacks. This paper studies the network structure of Chinese stock market from the macroscopic perspective, which will help investors to grasp the change of the whole market and generate an effective portfolio.