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With the launch of the Shanghai-Hong Kong and the Shenzhen-Hong Kong Stock Connect programs, the correlations among the three stock markets have been more complex. Therefore, this paper applies the Minimum Spanning Tree and Sliding Window methods to analyze the complex correlations of the 38 financial institutions in the three markets. Based on closing prices ranging from January 1, 2011 to December 31, 2021, three conclusions are obtained. The top 20 institutions in the network were mainly banking institutions from 2011 to 2013, while the node strength of non-banking financial institutions tended to increase from 2014 to 2021. The network shows different structures over time, even the changes in the three correlation coefficients generally show the same trend. The degree correlation between any two layers is positive, but the similarity shows different characteristics. These results would help to enrich multi-layer network theory and reveal the evolutionary characteristics of the correlation between financial institutions. [ABSTRACT FROM AUTHOR]

Stock networks, which are constructed from stock price time series, are useful tools for analyzing complex behaviors in stock markets. Following former researches, the epidemic model has been usually used to detect dynamic characteristics in a stock price complex systems. Recently, multilayer networks have been demonstrated well when working on heterogeneous nodes rather than integrated networks. In this paper, we proposed a two-layer SIR propagation model with an infective medium to analyze the spread of financial shocks. In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng(medium), we established a two-layer Granger networks. Betweenness showed that the Hong Kong stock market had a promoting transition function of financial shocks between the US stock markets and the mainland China stock markets. In addition, with a big basic reproduction number, stock markets system appeared to be vulnerable during extreme financial shock such as the outbreak of COVID-19 epidemic and the meltdown of stock markets. Furthermore, sensitivity analysis and the spreading simulation indicated that the US stock markets were much more robust to financial shocks than the mainland China stock markets. • New two-layer SIR propagation model with an infected medium for financial shocks. • Two-layer Granger networks based on the US stock markets, the mainland China stock markets, and the Hong Kong stock market. • Focusing on economic influence caused by COVID-19 epidemic. • Monte Carlo algorithm simulates the model in different conditions. [ABSTRACT FROM AUTHOR]

With the integration of international financial markets, the links between stock markets have become closer. Firstly, based on one-minute high-frequency returns, this paper applies the thermal optimal path (TOP) method to examine the lead–lag dependence between CSI 300 index and HSI index from 2016 to 2020. Secondly, regression analysis and correlation test are applied to cross-verify the results of TOP method. Finally, the robustness is tested through analysis with different T values and various market conditions as well as the replacing of proxy variable. The empirical results show that Hong Kong stock leads Chinese mainland stock for about one minute, and this leading effect is magnified when the stock markets fall. The experimental result is robust and has passed consistency test. This research is of great significance that not only guides investors, but also provides empirical evidence and effective information for policy makers. • One-minute high-frequency market data of Chinese mainland and Hong Kong are used. • Dynamic evolution of lead-lag relationship reveals Non-parametric TOP method. • Regression analysis and correlation test show the same results as TOP method. • Hong Kong leads about 1min, and this effect magnifies when the markets fall. [ABSTRACT FROM AUTHOR]