摘要翻译：
金融机构网络的系统性风险评估一般需要机构间金融风险敞口的信息。在债务秩算法的框架下，引入了一种只需节点属性（如总资产和总负债）作为输入的系统风险估计的近似方法。我们证明，这种近似捕捉到了以债务等级衡量的系统风险的很大一部分。此外，利用蒙特卡罗模拟，我们研究了可以放大系统风险的网络结构。事实上，如果市场是流动的，一般意义上的拓扑结构都不是更稳定的[1]，但更大的复杂性对整体稳定性是有害的[2]。我们发现，标量分类性度量与系统风险水平有很好的相关性。特别是，具有高系统性风险的网络结构是标量分类的，这意味着风险银行大多暴露于其他风险银行。低系统风险的网络结构是标量不协调的，存在风险银行与稳定银行的相互作用。
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英文标题：
《Controlling systemic risk - network structures that minimize it and node
  properties to calculate it》
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作者：
Sebastian M. Krause, Hrvoje \v{S}tefan\v{c}i\'c, Vinko Zlati\'c, Guido
  Caldarelli
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  Evaluation of systemic risk in networks of financial institutions in general requires information of inter-institution financial exposures. In the framework of Debt Rank algorithm, we introduce an approximate method of systemic risk evaluation which requires only node properties, such as total assets and liabilities, as inputs. We demonstrate that this approximation captures a large portion of systemic risk measured by Debt Rank. Furthermore, using Monte Carlo simulations, we investigate network structures that can amplify systemic risk. Indeed, while no topology in general sense is {\em a priori} more stable if the market is liquid [1], a larger complexity is detrimental for the overall stability [2]. Here we find that the measure of scalar assortativity correlates well with level of systemic risk. In particular, network structures with high systemic risk are scalar assortative, meaning that risky banks are mostly exposed to other risky banks. Network structures with low systemic risk are scalar disassortative, with interactions of risky banks with stable banks.
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PDF链接：
https://arxiv.org/pdf/1902.08483