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Financial Institution Multiscale Systemic Risk Spillover Network: A Wavelet Packet-MCQRNN Method

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  • Siyu Ren
  • Xu Zhang

Abstract

This paper proposes a novel network topology method that combines wavelet packet decomposition with a monotonic composite quantile regression neural network (MCQRNN). Using market data from 82 listed financial institutions spanning 2015–2025 and incorporating macroeconomic variables into the modeling framework, it aims to accurately identify the nonlinear features of multiscale systemic risk spillovers under different market conditions. The results reveal that the systemic risk spillover network of financial institutions exhibits clear multiscale frequency domain characteristics. In the risk spillover network, the banking sector consistently acts as a risk transmitter across all frequency bands, while other financial sectors serve as primary risk absorbers.

Suggested Citation

  • Siyu Ren & Xu Zhang, 2026. "Financial Institution Multiscale Systemic Risk Spillover Network: A Wavelet Packet-MCQRNN Method," Complexity, Hindawi, vol. 2026, pages 1-13, July.
    • Handle: RePEc:hin:complx:2352898
    DOI: 10.1155/cplx/2352898
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