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产业链协同发展对增强国内大循环内生动力和可靠性、提升国际循环质量和水平具有重要战略意义。但产业链关联可能引发债务风险跨行业传染和系统性风险共振，是关乎国家经济和金融安全的重要课题。本文在兼顾信用风险非线性相依性和尾部风险驱动机制基础上，构建全行业风险传染网络，从产业链视角探究债务风险跨行业传染路径和机理。基于我国行业信用利差数据研究发现：债务风险传染网络结构呈动态变化，2020年和2022年债务风险跨行业传染尤为突出，呈全网共振模式；特别地，2020年以来银行业与实体行业债务风险呈显著正相关；重要风险传染节点多为中游和下游行业，重要风险分散节点则集中于上游及部分下游行业；中游行业系统性风险贡献始终保持较高水平，下游行业则保持较低水平。进一步实证检验显示，中游行业或投入–产出高关联行业具有易感染、风险难分散特性，是经济系统中的重要风险聚集点。本研究可为产业链债务风险动态监管和精准防控提供重要参考依据，对防范和化解系统性金融风险具有一定现实意义。
Abstract：The coordinated development of industrial chains is of strategic importance for enhancing the dynamism and reliability of the domestic circulation and for improving the quality and level of international circulation. However, debt-risk contagion in industrial chains may trigger systemic risk resonance, thus an important issue concerning China's economic and financial security. Based on the non-linear interdependence of credit risks in industries and the tail-risk driven mechanism, we develop an industry-wide debt risk contagion network, and explore how debt risk spreads from the perspective of the industrial chain. We use industry-level credit-spread data from China's bond market for the empirical analysis. The results show that the structure of the debt-risk contagion network changed dynamically. The cross-industry transmission of debt risks in 2020 and 2022 is prominent, showing network-wide resonance. Particularly, the debt risk of the banking industry has been significantly positively correlated with that of the non-financial industries since 2020. In addition, important risk contagion nodes are mostly concentrated in the middle and downstream industries, and important risk dispersion nodes are more concentrated in the upstream and some downstream industries. Finally, we find the systemic risk contribution of the midstream industries has always remained at a high level, while the downstream industries remain at a lower level. Further empirical testing also shows that midstream industries or those with large input-output linkage sizes are more likely to be infected. Therefore, these industries are important risk clustering nodes in the whole economic system. The findings of this paper not only provide empirical evidence for dynamic monitoring and precise control of debt risk across industrial chains but also have important implications for preventing and mitigating systemic risks.
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(1)数据源自Wind数据库（https://www.wind.com.cn/portal/zh/Home/index.html）及中国银行间市场交易商协会（https://www.nafmii.org.cn）。
(1)Wind数据库（https://www.wind.com.cn/portal/zh/Home/index.html）提供的行业信用利差数据起始时间为2015年1月、截止时间为2023年3月，国家统计局数据库（https://data.stats.gov.cn/index.htm）提供2020年投入–产出表。基于数据可获取性，研究样本区间设置为2015—2022年。中债金融估值中心数据库网址为https://www.ccdc.com.cn/cbpc,Choice数据库网址为https://choice.eastmoney.com。
(2)因篇幅所限，行业信用利差描述性统计及ADF检验结果以附表1展示，见《统计研究》网站所列附件。下同。
(3)利用服从χ2(2)分布的SVR统计检验量对“行业间有无相关性”进行检验（Chen等，2019），结果显示当置信水平为85%时，绝大部分年份的弱相关统计量小于10%显著性临界值（可判断为不相关），强相关统计量则大于1%显著性临界值（可判断为相关）。当置信水平继续增加时，大部分年份弱相关统计量显著增加并超过5%显著性临界值，强相关统计量均大于1%显著性临界值，从而导致弱相关和强相关无法被有效区分。
(1)因篇幅所限，2015—2022年的行业相似度矩阵以附图1展示。
(1)力导向布局法可参考Das(2016)、Chen等（2019）的研究。因篇幅所限，债务风险传染网络以附图2展示。
(2)因篇幅所限，正毗邻均值矩阵A+和负毗邻均值矩阵A-以附图3展示。
(3)因篇幅所限，各行业系统性风险贡献排序以附图4展示。
(1)考虑到信用利差日变化率较小且宏观景气指数仅有月数据，本节采用周数据进行回归分析并对宏观景气指数进行插值处理。
(1)因篇幅所限，完整的分位数回归结果以附图5展示。
(1)因篇幅所限，基于产业链层级的完整分位数回归结果以附图6展示。
(1)因篇幅所限，基于产业链关联度的完整分位数回归结果以附图7展示。
基本信息:
DOI：10.19343/j.cnki.11-1302/c.2026.02.002
中图分类号:F124
引用信息:
[1]黄苒,胡蝶,赵雅琪.产业链视角下债务风险跨行业传染与形成机理——基于Copula相依结构和尾部风险驱动网络的研究[J].统计研究,2026,43(02):18-33.DOI:10.19343/j.cnki.11-1302/c.2026.02.002.
基金信息:
国家自然科学基金面上项目“基于供应链多层级结构分析的企业债务违约风险建模及多场景应用”（72171100）; 教育部人文社会科学研究规划基金项目“基于供应链依赖视角的中小企业违约风险测度和融资优化研究”（18YJA790037）
2026-02-25
2026-02-25