Analysis of global stock markets’ connections with emphasis on the impact of COVID-19()

We explore global stock markets’ connections during the financial crises or risks since 1995 with emphasis on the situation under COVID-19. We choose 40 countries/regions and take one index from each of them, and then compute the correlation coefficients and distances between each pair of the indices with a sliding window. We construct the complexes and carry out topological data analysis mainly through persistence landscapes and their [Formula: see text]-norms, which exhibit the complexes’ daily changes. We establish a critical dates’ detection system based on the persistence landscapes. Topological features of the complex networks are shown on the critical dates and dates before them. All the results show clearly that the connections became even closer among the markets when COVID-19 spread worldwide than those of any other risk. The robustness and effectiveness of these methods provide guidance for the analysis of financial crises in the future.