Operations Research and Management Science ›› 2026, Vol. 35 ›› Issue (2): 172-178.DOI: 10.12005/orms.2026.0058

• Application Research • Previous Articles     Next Articles

Research on Structure of Time-varying Risk Spillover and its Impact on Stock Market

QIU Longmiao1, ZHOU Donghai2, LIU Xiaoxing1,2   

  1. 1. School of Economics and Management, Southeast University, Nanjing 211189, China;
    2. School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
  • Received:2025-02-17 Online:2026-02-25 Published:2026-07-08

股票市场时变风险溢出结构及其影响研究

邱龙淼1, 周东海2, 刘晓星1,2   

  1. 1.东南大学 经济管理学院,江苏 南京 211189;
    2.东南大学 网络空间安全学院,江苏 南京 211189
  • 通讯作者: 周东海(1995-),男,湖南邵阳人,博士研究生,研究方向:金融科技与金融工程。Email: 230219299@seu.edu.cn。
  • 作者简介:邱龙淼(1986-),男,湖北黄石人,博士研究生,研究方向:金融风险管理。
  • 基金资助:
    国家社会科学基金重大项目(24&ZD117);国家自然科学基金面上项目(72173018);国家重点研发计划项目(2021QY2100)

Abstract: In October 2023, General Secretary Xi Jinping stressed at the Central Financial Work Conference that the financial sector was a lifeblood of a nation’s economy and crucial component of a country’s core competitiveness. Cross-industry risk contagion refers to the process of spreading and diffusing risks among different industries, and it has become an important factor affecting China’s financial stability. At present, the deep mosaic of the industrial chain, mixed operation of enterprises and investment diversification have intensified the interdependence of industries, and the risks of industries are easily transmitted through multiple channels in time of crisis, forming systemic risks.
This paper responds positively to the initiative of preventing cross-industry risks and analyzes the time-varying risk spillover among Chinese stock industries and its affecting factors from a structural perspective. In this paper, by applying the complex network method and the time-varying weighted directed network method, we construct the total volatility spillover index and directional volatility spillover index between the financial industry and real economy industry to reflect the volatility spillover mechanism between industries. This paper deeply analyzes the characteristics of the cross-industry network structure, and uses unsupervised learning methods to classify industries with similar characteristics and reveal the similarities and differences of industry segments. Then, this paper innovatively extends the risk topology network framework to realize the effective screening of risk spillover structure and its similarity in different periods. Finally, this paper combines multiple regression measures and machine learning methods to analyze the impact of the operating conditions of China’s real economy on the risk linkages among Chinese stock industries, which provides an important perspective and tool for building a robust financial market environment.
It is found that the cross-industry risk linkage effect is significantly enhanced under large risk event shocks, and the structural change in the spillover effects of each industry is particularly drastic under the impact of the COVID-19 epidemic. In terms of the structural characteristics of cross-industry spillovers, the industrials and consumer discretionary industries form a closely linked core and are the main net exporters of risk. In addition, the consumer staples, health care and information technology industries, as well as the energy, real estate and financial industries, share similar risk characteristics and spillover capabilities. Maintaining economic growth, expanding domestic demand, and stabilizing production are important ways to effectively reducing risk linkages across industries. These measures can enhance the resilience of the economic system and reduce the sensitivity of external shocks to cross-industry risk transmission.
This paper suggests that structured regulation should be highly emphasized in preventing and controlling financial risks across industries. At the same time, the momentum of financial stabilization measures should be fully stimulated, especially the growth of production capacity and the level of economic activity in industrials. Product price stability should be promoted through price monitoring, market regulation and policy guidance; and the stability of consumption growth should be promoted by raising income levels, strengthening consumer rights protection and improving market information disclosure, thereby enhancing the stability of the financial system and reducing the risk linkage effect among industries.

Key words: stock market, risk spillover structure, influencing factor, complex network

摘要: 跨行业的风险传染已经成为影响中国金融稳定的重要因素,从结构性的角度分析中国股票行业间的时变风险溢出及其影响因素,对于完善金融风险防范的结构设计、提升风险防控的全局性具有重要意义。对此,在构建股票行业间的风险溢出网络基础上研究不同时期的溢出结构和风险板块的差异性,并多维度分析驱动行业间风险联动的因素。结果表明:行业间风险联动效应随着风险事件冲击而显著增强,新冠疫情冲击对各行业溢出效应的结构性改变尤为强烈,医疗保健行业受风险影响显著。溢出结构特征显示,工业和可选消费行业是联系紧密的核心风险净输出者,日常消费、医疗保健与信息技术行业,能源、房地产与金融行业具有相似的特征和溢出能力。保持促增长、增内需、稳生产是降低行业间风险联动的重要途径。本文建议跨行业的金融风险防控要重视结构化监管,激发金融稳定性措施动能,降低行业风险联动。

关键词: 股票市场, 风险溢出结构, 影响因素, 复杂网络

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