基于PageRank的系统重要性金融机构识别模型
发布时间：2018-09-08 14:52
【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。
[Abstract]:Compared with the standard statistical method which relies on the market price data, the financial network model based on the topological structure of the inter-agency bilateral exposure network is more helpful to identify systemically important financial institutions and to assess the systemic risk. In this paper, a realistic CDS market network model is constructed, and based on the analysis of the contagion mechanism of a single default institution, the measurement model of systemically important financial institution identification is established based on the feature vector centrality and PageRank algorithm. The ranking algorithm used in this paper is flexible and feasible in dealing with large scale financial network data. The test results show that regulators should not only focus on "too big to fail" institutions, but also take the central nodes of the financial network "too closely connected to fail" as a problem.
【作者单位】： 中南大学商学院;中国人民银行郑州培训学院;
【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)
【分类号】：F831.2
，
本文编号：2230846
[Abstract]:Compared with the standard statistical method which relies on the market price data, the financial network model based on the topological structure of the inter-agency bilateral exposure network is more helpful to identify systemically important financial institutions and to assess the systemic risk. In this paper, a realistic CDS market network model is constructed, and based on the analysis of the contagion mechanism of a single default institution, the measurement model of systemically important financial institution identification is established based on the feature vector centrality and PageRank algorithm. The ranking algorithm used in this paper is flexible and feasible in dealing with large scale financial network data. The test results show that regulators should not only focus on "too big to fail" institutions, but also take the central nodes of the financial network "too closely connected to fail" as a problem.
【作者单位】： 中南大学商学院;中国人民银行郑州培训学院;
【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)
【分类号】：F831.2
，
本文编号：2230846
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