系统性风险的度量：稳健的排序技术方法
Measuring Systemic Risk: Robust Ranking Techniques Approach
摘要：
在这项研究中，我们引入了一个强大的指标，以确定系统重要性金融机构（SIFI）在金融网络中，考虑到共同的特质冲击和传染，通过交易对手暴露。我们开发了一个有效的算法来排名金融机构制定一个不动点问题，并将其减少到一个非光滑凸优化问题。然后，我们研究了所提出的度量的底层分布，并通过使用不同的金融网络结构来分析算法的性能。总体而言，我们的研究结果表明，金融网络中的机构的互联水平和地位是衡量系统性风险和识别SIFIs的重要因素。结果表明，增加一个机构的外和内的程度连接的水平可以有不同的影响，其系统排名。此外，在实证方面，我们使用每个国家最大银行的面板数据集来研究导致识别全球系统性重要银行（G-SIB）的因素。我们的实证结果支持了理论模型的主要发现。
英文摘要：
In this research, we introduce a robust metric to identify Systemically Important Financial Institution (SIFI) in a financial network by taking into account both common idiosyncratic shocks and contagion through counterparty exposures. We develop an efficient algorithm to rank financial institutions by formulating a fixed point problem and reducing it to a non-smooth convex optimization problem. We then study the underlying distribution of the proposed metric and analyze the performance of the algorithm by using different financial network structures. Overall, our findings suggest that the level of interconnection and position of institutions in the financial network are important elements to measure systemic risk and identify SIFIs. Results show that increasing the levels of out- and in-degree connections of an institution can have a diverse impact on its systemic ranking. Additionally, on the empirical side, we investigate the factors which lead to the identification of Global Systemic Important Banks (G-SIB) by using a panel dataset of the largest banks in each country. Our empirical results supports the main findings of the theoretical model.
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