This study measures systemic importance of financial institutions based on network centralities and links them to institutions’ characteristics. We focus on the lower tail dependence networks constructed by combining Clayton copula model and planar maximally filtered graph method. Considering different centrality measures’ correlations, we obtain the comprehensive centrality index about systemic importance by principal component analysis. The centrality measures can capture cross-sectional differences and time-series variations of systemic importance. The financial institutions with higher leverage, lower price earning ratio, lower total assets turnover rate and lower return on equity tend to have higher systemic importance based on tail dependence.

这项研究基于网络中心性来衡量金融机构的系统重要性，并将它们与机构的特征联系起来。我们专注于通过结合Clayton copula模型和平面最大滤波图方法构造的较低尾部依赖网络。考虑到不同集中度度量的相关性，我们通过主成分分析获得关于系统重要性的综合集中度指标。中心度度量可以捕获具有系统重要性的横截面差异和时间序列变化。具有较高杠杆率，较低本益比，较低总资产周转率和较低净资产收益率的金融机构往往会因依赖尾部而具有较高的系统重要性。