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Research on systemic risk measurement based on temporal financial knowledge graph: Evidence from China

Author

Listed:
  • Z. Liu
  • Y. Zhang

    (Audencia Business School)

  • H. Chen

Abstract

The inherent complexity and interconnectedness of systemic financial risks necessitate sophis ticated measurement frameworks, especially as new financial forms emerge and the economic environment becomes more complex. This paper aims to develop a novel composite systemic risk model using the financial knowledge graph for risk measurement. Notably, our research in corporates traditional financial institutions and FinTech entities, extending beyond conventional financial risk analytical boundaries. Empirical results show the model boosts risk measurement accuracy and is responsive to classic contingencies and inclusive finance policies. FinTech and securities sectors prove more susceptible to systemic risk than banking and insurance, with FinTech being easily influenced by high-tech enterprise support and FinTech-related regulatory policies, and securities by monetary policy. Moreover, institutions' performance is consistent across risk event types, while top risk contributors are evenly distributed across diverse financial sectors and account for the lion's share of the industry's systemic risk and exhibit risk aggregation.

Suggested Citation

  • Z. Liu & Y. Zhang & H. Chen, 2025. "Research on systemic risk measurement based on temporal financial knowledge graph: Evidence from China," Post-Print hal-05306849, HAL.
    • Handle: RePEc:hal:journl:hal-05306849
    DOI: 10.1016/j.pacfin.2025.102924
    Note: View the original document on HAL open archive server: https://hal.science/hal-05306849v1
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