Self-dynamics and inter-dynamics network reconstruction for characterizing the systemic risk in stock market

Reconstructing a dynamic network evolving over time is a fundamental scientific problem for understanding the propagation and diffusion of risk in complex systems. In this paper, we reconstruct the dynamic network of the global stock market at 4927 time points using self-dynamics and inter-dynamics model while considering time factors. Through orthogonal basis decomposition, the dynamic interactions within the stock market are transformed into a linear inverse problem that can be solved using parallel computing. We analyse the evolution of the dynamic interaction among the stock indices and characterize the systemic risk of stock market from a network topology perspective. The results suggest that the occurrence of unexpected events enhances the dynamic interactions among stock indices. The self-dynamics and inter-dynamics model is capable of identifying stock indices that are more significantly affected by such events. Furthermore, these stock indices tend to be concentrated in countries or regions that are highly correlated with unexpected events. Additionally, the average path weight and clustering coefficient are effective indicators of systemic risk in the stock market. This paper also compares the self-dynamics and inter-dynamics model with Granger test, GARCH-BEKK and DY framework methods and finds that the results are still robust. This method offers distinct advantages in characterizing systemic risk.