系统管理学报

系统管理学报 ›› 2026, Vol. 35 ›› Issue (4): 1131-1144.DOI: 10.3969/j.issn.2097-4558.2026.04.019

• 企业管理与公司金融 • 上一篇    

中小企业对影子银行风险溢出效应驱动因素分析——基于频域尺度多层时序网络视角

刘超1，李居超1，张婷婷1，李国成2   

  1. 1. 北京工业大学 经济与管理学院，北京 100124；2. 中共青海省委党校，西宁 810001
  • 收稿日期:2025-06-25 修回日期:2025-08-29 出版日期:2026-07-28 发布日期:2026-08-04
  • 基金资助:
    国家自然科学基金面上项目（72372003，62073007，61773029）

Driving Factors of SME Risk Spillover Effects on Shadow Banking: A Multilayer Temporal Network Perspective Across Frequency Domains

Liu Chao1，Li Juchao1，Zhang Tingting1，Li Guocheng2   

  1. 1. School of Economics and Management，Beijing University of Technology，Beijing 100124，China；2. Party School of Qinghai Provincial Committee of CPC，Xining 810001，China
  • Received:2025-06-25 Revised:2025-08-29 Online:2026-07-28 Published:2026-08-04

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摘要： 准确识别中小企业对影子银行风险溢出效应的驱动因素，对于维护中国金融体系的安全与稳定具有重要意义。本文首先构建了中小企业对影子银行风险溢出效应及其网络中心性影响机制的理论模型；然后基于2018年1月2日至2024年9月30日沪深 A 股上市中小企业与影子银行收盘价日数据，运用ARMA-TGARCH-时变 Copula-CoVaR模型，分别考察了专精特新中小企业与传统中小企业对影子银行的风险溢出效应。在此基础上，借助多层时序网络模型，从频域尺度视角进一步分析了两类企业在短期、中期和长期3个频域维度下的多层波动溢出网络特征向量中心性。随后，从网络中心性、宏观经济与微观个体3个层面，系统探究了中小企业对影子银行风险溢出的驱动因素，并运用双重机器学习模型检验了各驱动因素与风险溢出之间的作用关系。研究发现：专精特新中小企业对影子银行的风险溢出效应高于传统中小企业；而在时域及频域（短期、中期、长期）尺度下，传统中小企业间的波动溢出效应均高于专精特新中小企业。从驱动因素而言，网络中心性、经济政策不确定性、企业流动性及影子银行融资规模，均显著推动了两类中小企业对影子银行的风险溢出。此外，投资者情绪仅对专精特新中小企业的风险溢出具有显著提升作用，通货膨胀水平则仅显著提升传统中小企业的风险溢出效应。

关键词: 中小企业, 影子银行, 风险溢出, 多层时序网络

Abstract: Accurately identifying the driving factors of the spillover effects of small and medium-sized enterprises (SMEs) on shadow banking is of great significance for maintaining the safety and stability of China’s financial system. Based on daily closing price data of China’s A-share listed SMEs and shadow banking institutions from January 2, 2018 to September 30, 2024, the ARMA-TGARCH-time-varying Copula-CoVaR model is employed to examine the risk spillover effects of “specialized and sophisticated” SMEs versus traditional SMEs. A multilayer temporal network model is used to explore eigenvector centrality characteristics of multilayer volatility spillover networks across short-, medium-, and long-term frequency domains. Furthermore, the driving factors of SMEs risk spillover from three dimensions: network centrality, macroeconomics, and microeconomics levels is systematically explored. A double/debiased machine learning model is then used to test the relationships between these factors and risk spillovers. The findings show that specialized and sophisticated SMEs exhibit stronger risk spillover effects on shadow banking than traditional SMEs. However, traditional SMEs display stronger volatility spillover effects among themselves across time and frequency domains. Network centrality, economic policy uncertainty, corporate liquidity, and shadow banking financing scale significantly increase risk spillover for both SME types. In addition, investor sentiment significantly increases spillovers only for specialized and sophisticated SMEs, while inflation significantly strengthens spillovers only for traditional SMEs.

Key words: small and medium-sized enterprises (SMEs), shadow banking, risk spillover, multilayer temporal network

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