英文标题：
《Sensitivity and Computational Complexity in Financial Networks》
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作者：
Brett Hemenway and Sanjeev Khanna
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最新提交年份：
2016
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英文摘要：
  Modern financial networks exhibit a high degree of interconnectedness and determining the causes of instability and contagion in financial networks is necessary to inform policy and avoid future financial collapse. In the American Economic Review, Elliott, Golub and Jackson proposed a simple model for capturing the dynamics of complex financial networks. In Elliott, Golub and Jackson\'s model, each institution in the network can buy underlying assets or percentage shares in other institutions (cross-holdings) and if any institution\'s value drops below a critical threshold value, its value suffers an additional failure cost.   This work shows that even in simple model put forward by Elliott, Golub and Jackson there are fundamental barriers to understanding the risks that are inherent in a network. First, if institutions are not required to maintain a minimum amount of self-holdings, an $\\epsilon$ change in investments by a single institution can have an arbitrarily magnified influence on the net worth of the institutions in the system. This sensitivity result shows that if institutions have small self-holdings, then estimating the market value of an institution requires almost perfect information about every cross-holding in the system. Second, we show that even if a regulator has complete information about all cross-holdings in the system, it may be computationally intractable to even estimate the number of failures that could be caused by an arbitrarily small shock to the system. Together, these results show that any uncertainty in the cross-holdings or values of the underlying assets can be amplified by the network to arbitrarily large uncertainty in the valuations of institutions in the network.
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中文摘要：
现代金融网络表现出高度的互联性，确定金融网络中不稳定和传染的原因对于制定政策和避免未来金融崩溃是必要的。在《美国经济评论》（American Economic Review）中，Elliott、Golub和Jackson提出了一个简单的模型来捕捉复杂金融网络的动态。在Elliott、Golub和Jackson的模型中，网络中的每个机构都可以购买其他机构的基础资产或百分比股份（交叉持股），如果任何机构的价值降至临界阈值以下，其价值将遭受额外的失败成本。这项工作表明，即使在Elliott、Golub和Jackson提出的简单模型中，也存在理解网络固有风险的基本障碍。首先，如果不要求机构保持最低限度的自我持股，那么单个机构投资的$\\epsilon$变化可能会对系统中机构的净值产生任意放大的影响。这一敏感度结果表明，如果机构拥有少量的自我持股，那么估计一个机构的市场价值需要关于系统中每个交叉持股的几乎完美信息。第二，我们表明，即使监管者拥有关于系统中所有交叉持股的完整信息，在计算上也可能难以估计系统受到任意小的冲击可能导致的故障数量。综上所述，这些结果表明，交叉持股或基础资产价值的任何不确定性都可能被网络放大为网络中机构估值的任意大不确定性。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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