网络连通性与股票市场金融传染——来自美国-金砖五国股市的经验分析

Network Connectedness and Financial Contagion in Stock Market—Empirical Analysis from US-BRICS Stock Market

本文借助金融网络的连通结构解释金融风险传染机制。以金砖五国(BRICS)与美国1996年12月至2019年10月月频数据为样本,基于VAR-ADCC-EGARCH(1,1)模型测度了美国与金砖五国之间的非对称动态相关系数(ADCC),以此表征US-BRICS金融市场间的金融传染,并基于DY-VAR框架对股票市场网络连通性进行测度,建立并估计了非对称动态相关系数对网络连通性的分位回归模型,从网络视角揭示了金融风险传染的网络机制。结果表明,美国对金砖五国存在显著的金融传染,各国间的金融网络连通性具有显著的时变性,金融市场网络连通性对风险传染具有显著的非对称正向影响,金融网络连通性对高风险传染的影响远远大于对低风险传染的影响;金融网络连通性不仅是美国与金砖五国金融传染的直接影响因素,还在很大程度上强化了石油价格(经济基本面传染)、黄金价格与金融压力(投资者行为传染)的影响,构成了石油价格、黄金价格与金融压力影响金融传染的调节机制。通过监测股票市场网络结构特征的动态演化,可以对股票市场系统性风险进行预警。

This paper explains the contagion mechanism of financial risk with the help of the connected structure of financial network.Taking the monthly data of BRICS and US from December 1996 to October 2019 as a sample,the asymmetric dynamic correlation coefficient(ADCC)between US and BRICS was measured based on VAR-ADCC-EGARCH(1,1)model,this paper presents the financial contagion between the US-BRICS financial markets,and measures the network connectivity of the stock market based on the DY-VAR framework,and establish and estimate the quantile regression model of asymmetric dynamic correlation coefficient on network connectivity,this paper reveals the network mechanism of risk contagion from the perspective of network.The results show that the United States has significant financial contagion to the BRICS,and the financial network connectivity among BRICS has significant time-varying characteristics,and the impact of financial network connectivity on high-risk contagion is much greater than that on low-risk contagion.Further research found that financial network connectivity is not only a direct factor affecting financial contagion between the US and the BRICS,it also strengthened the influence of oil price(economic fundamentals contagion)and financial pressure,and gold price(investor behavior contagion),and constituted the adjustment mechanism of oil price,gold price and financial pressure influencing financial contagion.The authority can pre-warn the stock market crisis by monitoring the dynamic evolution of the network structure characteristics.