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Centrality-based measures of financial institutions' systemic importance: A tail dependence network view

机译:基于中心的金融机构的系统性重要性:尾依赖网络视图

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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. (C) 2020 Elsevier B.V. All rights reserved.
机译:本研究基于网络中心性来衡量金融机构的系统重要性,并将其与机构特征联系起来。重点研究了结合Clayton copula模型和平面最大滤波图方法构造的低尾相关网络。考虑到不同的中心性测度之间的相关性,通过主成分分析得到了系统重要性的综合中心性指数。中心性度量可以捕捉具有系统重要性的横截面差异和时间序列变化。基于尾部依赖,杠杆率较高、市盈率较低、总资产周转率较低、净资产收益率较低的金融机构往往具有较高的系统重要性。(C) 2020爱思唯尔B.V.版权所有。

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