摘要
相对于依赖市场价格数据的标准计量统计方法,基于机构间双边敞口网络拓扑结构的金融网络模型更有助于系统重要性金融机构的识别和系统性风险评估。本文构建了贴近现实的CDS市场网络模型,并基于单个违约机构传染机制的分析,借鉴特征向量中心度和PageRank算法思想,研究建立了系统重要性金融机构识别的度量模型。本文所采用的排名技术算法在应对大规模金融网络数据时具灵活性和可行性。测试结果显示,监管当局不仅要关注"太大而不能倒"的机构,更须将金融网络中"关联太紧密而不能倒"的中心节点作为问题认真加以对待。
Financial network models based on the network topology of bilateral exposures helps to identify the systemically important institution rather than rely solely on statistical correlations on market price-based data for financial institutions.We introduce a methodology for analyzing the default of an institution,metrics for detecting the systemic importance of institution inspired by eigenvector centrality and PageRank.The ranking techniques testifies its flexibility and feasibility to deal with a large-scale financial network dataset.The results suggest that the debate on too-big-to-fail institutions should include the even more serious issue of too-interconnected-to-fail.
出处
《系统工程》
CSSCI
北大核心
2017年第4期1-8,共8页
Systems Engineering
基金
国家自然科学基金资助项目(71173241
71473275)
教育部新世纪人才基金资助项目(NCET-10-0830)
