基于复杂网络的新冠疫情下股票市场波动模型研究

Research on the Model of the Stock Market Volatility during COVID-19 Period Based on Complex Networks

新冠疫情对金融系统造成巨大冲击,其中股票市场是中国金融市场风险的主要传染源之一.本文基于复杂网络研究股票市场波动性,提出将关节点定向攻击的方法运用于股票网络,主要思想是以迭代的方式删除最易受攻击的关节点,这些点的删除会增加最多网络中连通分支的数量,最终得到维持网络稳定的核心稳定性结构.文章基于已实现波动率和阈值法构建删减股票网络,将研究周期分为平稳发展期和风险波动期,对比分析其拓扑性质,通过中心性指标和关节点定向攻击挖掘出需重点监控的股票,以防止风险的大规模传播或并发性风险的发生,有助于监管者维护金融市场平稳运行.

COVID-19 has caused huge impact to financial system,among which stock market is one of the main sources of infection.This paper investigates stock market volatility based on complex networks,proposing the method of applying articulation point-targeted attack(APTA)in the model of the stock market volatility.The strategy of APTA is removing the most destructive articulation points(AP)that will result in most nodes disconnected from the giant connected component(GCC)by iterating,and eventually uncovering the residual giant bicomponent(RGB)that maintains the structural stability of the network.This paper models stock network based on realized volatility and thresholds,separates research period into steady-developing period and risk-fluctuating period,compares and analyzes the topological properties.Network centrality indexes and APTA are used to discover the important stocks that need to be supervised specially,thus avoiding the big-scale spread of risks or concurrent risks,and helping the supervisors maintain financial stability.