Analyzing volatility patterns in the Chinese stock market using partial mutual information-based distances

Arash Sioofy Khoojine (), Ziyun Feng (), Mahboubeh Shadabfar () and Negar Sioofy Khoojine ()
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Arash Sioofy Khoojine: Yibin University
Ziyun Feng: Yibin University
Mahboubeh Shadabfar: Nanjing University of Science and Technology
Negar Sioofy Khoojine: Middle East Technical University

The European Physical Journal B: Condensed Matter and Complex Systems, 2023, vol. 96, issue 12, 1-21

Abstract: Abstract This study examines the dynamic range of financial networks in the Chinese stock market between 2019 and 2021. It provides an objective assessment of the network’s characteristics and scalability. The research time-frame is divided into three segments, reflecting the fluctuations of the financial market, including stable, volatile, and follow-up periods. To establish correlations among companies, the study employs the partial mutual information distance (PMID) method, followed by the construction of three minimum spanning tree (MST) networks for each period. Given the non-linear nature of financial phenomena, PMID is found to be more appropriate than linear methods in the study of financial markets. Additionally, the power law is observed in all three networks. This study is organized hierarchically into levels of nodes, clusters, and global indicators, providing a comprehensive perspective on network behavior and adaptation. Three-level indicators are calculated for each of the three networks, and the findings display a noteworthy variation between the volatile network and the other two networks. During stable and follow-up periods, a node-level analysis has indicated strong interconnectedness among companies. In contrast, during volatility, there are dynamic fluctuations in network dynamics. Cluster-level analysis reveals that firms become more essential connectors and actively engaged, with increased centrality. A global analysis shows that companies are more likely to form partnerships with counterparts possessing similar degrees during times of market volatility compared to periods of stability or follow-up periods. To assess the resilience of the constructed networks, we employed Markov chain analysis and examined the maximal connected component (MCC); the study findings suggest that the network is more susceptible to volatility in the observed second period, while demonstrating greater resilience in the follow-up period indicating recovery of financial markets. Graphic abstract

Date: 2023
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DOI: 10.1140/epjb/s10051-023-00628-6

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