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多时间尺度下行业间系统性金融风险溢出及拓扑结构分析
引用本文:刘超,郭亚东. 多时间尺度下行业间系统性金融风险溢出及拓扑结构分析[J]. 中国管理科学, 2022, 30(10): 46-59. DOI: 10.16381/j.cnki.issn1003-207x.2020.0324
作者姓名:刘超  郭亚东
作者单位:1.北京工业大学经济与管理学院,北京100124; 2.北京现代制造业发展基地,北京100124
基金项目:国家自然科学基金资助项目(61773029,61273230);北京市属高校高水平教师队伍建设支持计划长城学者培养计划项目(CIT&TCD20170304)
摘    要:系统性金融风险频发,其表现出的风险溢出效应受到国内外学者广泛关注。通过极大重叠离散小波变换和溢出指数方法,从静态和动态视角定量研究不同时间尺度和阶段下我国市场行业间系统性金融风险溢出特性,并构建多时间尺度和不同风险阶段下风险溢出网络,分析其拓扑结构演化规律。实证结果发现:行业间总体风险溢出水平较高,市场整体风险联动性强;原始尺度下国防军工、农林牧渔、有色金属行业为主要的风险溢出行业,资本市场及相关行业、化工及机电、部分服务业、医药生物行业始终是主要的风险接受行业;不同时间尺度下行业间风险溢出特性会发生明显的改变,且随着时间尺度的增加,整体风险溢出特性开始减弱;内生危机时行业间风险溢出水平要高于外生危机;同一社区下的行业之间会优先发生风险的传递。所得结果可为监管者和投资者提供一定的决策参考。

关 键 词:小波变换  行业指数  波动溢出  网络拓扑分析  复杂网络,
收稿时间:2020-03-02
修稿时间:2020-05-18

Systemic Financial Risk Spillover and Its Topology Analysis of Sector Indexes in China under a Multi-Scale View
LIU Chao,GUO Ya-dong. Systemic Financial Risk Spillover and Its Topology Analysis of Sector Indexes in China under a Multi-Scale View[J]. Chinese Journal of Management Science, 2022, 30(10): 46-59. DOI: 10.16381/j.cnki.issn1003-207x.2020.0324
Authors:LIU Chao  GUO Ya-dong
Affiliation:1. School of Economics and Management, Beijing University of Technology, Beijing 100124, China;2. Research Base of Beijing Modern Manufacturing Development, Beijing 100124, China
Abstract:In recent years, systemic financial risks frequently break out, which causes great attention from scholars at home and abroad. At present, most scholars are concerned with the spillover effects between two sectors from a static perspective. To uncover the spillover effect among sectors in China, the industry indexes of Shenwan Index are selected, which includes 28 sectors as the research object. Firstly, from static and dynamic perspectives, the characteristics of systemic financial risk spillover effect of economic sectors in different time scales and time periods are studied by using the maximum overlap discrete wavelet transform method and the spillover index method. Then, the networks of risk spillover effect in different time scales and time periods are constructed to analyze its topology evolution laws. Experimental results are as follows: the level of risk spillover of economic sectors is relatively high, and the connectedness of the overall market is strong; the main risk spillover sectors include the national defense industry, agriculture, forestry, animal husbandry, fishery, and non-ferrous metals at the original scale; the main risk acceptance sectors involve capital markets, chemical, electromechanical, service industries, and biopharmaceutical industry; the characteristics of risk spillover effect of economic sectors vary significantly under different time scales, and the risk spillover effect tends to be weak with the increase in time scales; the risk spillover effect of endogenous crisis is higher than that of exogenous crisis; the risk spillover effect favors for the sectors in the same community. Our conclusions provide theoretical supports for decision-makers. Regulators should pay more attention to the main risk spillover sectors under different time scales and protect the main risk acceptance sectors to make sure they run smoothly. For investors, it is necessary to recognize the risk spillover characteristics of industries in different time scales, evaluate the impact of the main risk spillover industries, and make rational investments.
Keywords:wavelet transform   industry indexes   volatility spillover   topology analysis   complex network,
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