英文标题：
《Classical mechanics of economic networks》
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作者：
Nima Dehmamy, Sergey V. Buldyrev, Shlomo Havlin, H. Eugene Stanley,
  Irena Vodenska
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最新提交年份：
2014
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英文摘要：
  Financial networks are dynamic. To assess their systemic importance to the world-wide economic network and avert losses we need models that take the time variations of the links and nodes into account. Using the methodology of classical mechanics and Laplacian determinism we develop a model that can predict the response of the financial network to a shock. We also propose a way of measuring the systemic importance of the banks, which we call BankRank. Using European Bank Authority 2011 stress test exposure data, we apply our model to the bipartite network connecting the largest institutional debt holders of the troubled European countries (Greece, Italy, Portugal, Spain, and Ireland). From simulating our model we can determine whether a network is in a \"stable\" state in which shocks do not cause major losses, or a \"unstable\" state in which devastating damages occur. Fitting the parameters of the model, which play the role of physical coupling constants, to Eurozone crisis data shows that before the Eurozone crisis the system was mostly in a \"stable\" regime, and that during the crisis it transitioned into an \"unstable\" regime. The numerical solutions produced by our model match closely the actual time-line of events of the crisis. We also find that, while the largest holders are usually more important, in the unstable regime smaller holders also exhibit systemic importance. Our model also proves useful for determining the vulnerability of banks and assets to shocks. This suggests that our model may be a useful tool for simulating the response dynamics of shared portfolio networks.
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中文摘要：
金融网络是动态的。为了评估它们对全球经济网络的系统重要性并避免损失，我们需要考虑链路和节点的时间变化的模型。利用经典力学和拉普拉斯决定论的方法，我们开发了一个模型，可以预测金融网络对冲击的响应。我们还提出了一种衡量银行系统重要性的方法，我们称之为BankRank。利用欧洲银行管理局2011年压力测试风险敞口数据，我们将我们的模型应用于连接陷入困境的欧洲国家（希腊、意大利、葡萄牙、西班牙和爱尔兰）最大机构债务持有人的双边网络。通过模拟我们的模型，我们可以确定网络是处于“稳定”状态，在这种状态下冲击不会造成重大损失，还是处于“不稳定”状态，在这种状态下会发生毁灭性的破坏。将扮演物理耦合常数角色的模型参数与欧元区危机数据进行拟合表明，在欧元区危机之前，该系统大多处于“稳定”状态，而在危机期间，它转变为“不稳定”状态。我们的模型产生的数值解与危机事件的实际时间线非常吻合。我们还发现，虽然最大的持有者通常更重要，但在不稳定的政权中，较小的持有者也表现出系统重要性。我们的模型也被证明有助于确定银行和资产对冲击的脆弱性。这表明我们的模型可能是模拟共享投资组合网络响应动力学的有用工具。
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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：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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