Core-periphery dynamics in market-conditioned financial networks: A conditional p-threshold mutual information...

Area of Science:

• Financial economics
• Network science
• Complexity science

Background:

• The COVID-19 pandemic caused unprecedented global economic disruption.
• Understanding financial market dynamics during crises is crucial for stability.
• Existing network frameworks may not fully capture nonlinear dependencies during extreme events.

Purpose of the Study:

• To investigate financial market structure reorganization during the COVID-19 crash.
• To analyze nonlinear dependencies among stocks in four major economies.
• To assess systemic vulnerability and shock transmission mechanisms.

Main Methods:

• Utilized a conditional p-threshold mutual information (MI) based Minimum Spanning Tree (MST) network framework.
• Identified crash periods using Hellinger distance and Hilbert spectrum analysis.
• Applied permutation-based significance testing to validate stock-level dependencies.

Main Results:

• Market networks became more integrated with shorter path lengths and higher centrality during the crash.
• Declining core concentration and increasing periphery fragility were observed, facilitating shock transmission.
• Post-crash recovery was partial and non-uniform, with evidence of persistent structural effects and increased aftershock frequency.

Conclusions:

• The conditional p-threshold MI framework effectively captures nonlinear interdependencies and systemic vulnerability.
• Financial markets exhibit common crisis-related dynamics and structural reorganization during severe downturns.
• The study highlights persistent structural effects and increased volatility post-crisis.