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Systemic risk spillovers incorporating investor sentiment: Evidence from an improved TENET analysis

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  • Zhao, Xia
  • Hu, Qing
  • Song, Yuping
  • Huang, Jiefei

Abstract

This study investigates systemic risk spillovers across 49 Chinese financial institutions through incorporating investor sentiment. We enhance the tail-event driven network (TENET) model to better capture incremental changes and absorption effects in risk spillovers. Three distinct methods are employed to construct the investor sentiment index, enabling us to identify the most effective one. Our research reveals the following results. (1) The improved TENET model incorporating investor sentiment could better track real-time changes in systemic risk. (2) Risk spillover dynamics vary across sectors, while risk spill-in patterns remain consistent. (3) Intra-sector risk spillovers typically dominate, but extreme events amplify inter-sector spillovers. (4) The impact of the China-US trade war and the COVID-19 pandemic on risk spillovers across the financial sector is different. These findings provide regulatory implications for managing systemic risk during periods of extreme stress.

Suggested Citation

  • Zhao, Xia & Hu, Qing & Song, Yuping & Huang, Jiefei, 2025. "Systemic risk spillovers incorporating investor sentiment: Evidence from an improved TENET analysis," Economic Modelling, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:ecmode:v:151:y:2025:i:c:s0264999325001798
    DOI: 10.1016/j.econmod.2025.107184
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