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The Impact of Industry‐Level Loan Similarity on Systemic Risk in the Banking Sector

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  • Kaiming Zhang
  • Jingjing Zhang
  • Jialin Guo

Abstract

After the global financial crisis, systemic risk, especially in the banking sector, has grown increasingly important, stressing the need for better risk management. This study examines how industry‐level loan similarity (ILLS) among banks propagates systemic risk through indirect linkages in the Chinese banking system. We have constructed a novel interbank relationship network (ILLS) based on the loan data of 42 listed banks in China from 2013 to 2022. By employing a combination of the GARCH copula quantile regression model (GCQRM) and the dynamic spatial Durbin model (DSDM), the study validated the significant role of the ILLS in explaining interbank systemic risk spillover. Our results indicate that ILLS is a potent spillover channel: banks with similar industry‐level loan portfolios exacerbate each other's distress, and the systemic risk caused by indirect spillovers through ILLS is significantly higher than the systemic risk arising from their own financial factors. According to our results, regulators should monitor the overlap in banks' industry‐level loan concentration and take macroprudential measures to prevent systemic risks arising from common risk exposures.

Suggested Citation

  • Kaiming Zhang & Jingjing Zhang & Jialin Guo, 2025. "The Impact of Industry‐Level Loan Similarity on Systemic Risk in the Banking Sector," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(8), pages 4181-4200, December.
    • Handle: RePEc:wly:mgtdec:v:46:y:2025:i:8:p:4181-4200
    DOI: 10.1002/mde.70009
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