英文标题：
《DebtRank: A microscopic foundation for shock propagation》
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作者：
Marco Bardoscia, Stefano Battiston, Fabio Caccioli, Guido Caldarelli
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最新提交年份：
2015
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英文摘要：
  The DebtRank algorithm has been increasingly investigated as a method to estimate the impact of shocks in financial networks, as it overcomes the limitations of the traditional default-cascade approaches. Here we formulate a dynamical \"microscopic\" theory of instability for financial networks by iterating balance sheet identities of individual banks and by assuming a simple rule for the transfer of shocks from borrowers to lenders. By doing so, we generalise the DebtRank formulation, both providing an interpretation of the effective dynamics in terms of basic accounting principles and preventing the underestimation of losses on certain network topologies. Depending on the structure of the interbank leverage matrix the dynamics is either stable, in which case the asymptotic state can be computed analytically, or unstable, meaning that at least one bank will default. We apply this framework to a dataset of the top listed European banks in the period 2008 - 2013. We find that network effects can generate an amplification of exogenous shocks of a factor ranging between three (in normal periods) and six (during the crisis) when we stress the system with a 0.5% shock on external (i.e. non-interbank) assets for all banks.
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中文摘要：
作为一种估计金融网络中冲击影响的方法，DebtRank算法已经被越来越多地研究，因为它克服了传统违约级联方法的局限性。在这里，我们通过迭代单个银行的资产负债表身份，并通过假设从借款人到贷款人的冲击转移的简单规则，为金融网络的不稳定性建立了一个动态的“微观”理论。通过这样做，我们推广了DebtRank公式，既从基本会计原则的角度解释了有效动态，又防止低估某些网络拓扑上的损失。根据银行间杠杆矩阵的结构，动态要么是稳定的，在这种情况下，渐近状态可以通过分析计算得出，要么是不稳定的，这意味着至少有一家银行会违约。我们将此框架应用于2008-2013年期间欧洲顶级上市银行的数据集。我们发现，当我们对所有银行的外部（即非银行间）资产施加0.5%的冲击时，网络效应会产生一个范围在三（正常时期）到六（危机期间）之间的外部冲击放大。
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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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