Abstract: Systemic risks are received much attention in financial studies these years. Most existing risk measures could be not directly appliable to measure the systemic risk contribution of financial institutes in China for the complex financial network in big data era. To describe the nonlinear correlation of financial returns, a new multi-layer correlation network, named by Local Gaussian Correlation Network (LGCNET), is constructed by combining quantile regression and local Gaussian correlation coefficient.The new method is used to measure the systemic risk contributions of 50 A-shares listed companies in China from 2018 to 2021. The empirical results show that: 1) The finance and technology industries are often the center of network nodes. They often have high correlations with other industry companies, which demonstrates that such industries are usually the center of risk transmission. 2) Due to their high market value, infrastructure and banking companies are generally more important in the financial system. Meanwhile, more attention should be paid to companies whose importance exceeds their market value, because they often have greater influence in the market. 3) At the systemic level, the whole system has a relatively high level of risk during 2018, especially at the beginning of 2018, which may be mainly affected by the increase of credit risk and the trade war, whereas the domestic systemic risks have been well controlled during the new crown epidemic in 2020. Finally, some suggestions are provided for improving China’s financial risk prevention system.

Key words: LGCNET; systemic risk; local Gaussian correlation; multilayer network