Robust Financial Networks

Feihong Hu (), Daniel Mitchell () and Stathis Tompaidis
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Feihong Hu: Information, Risk and Operations Management Department, McCombs School of Business, University of Texas at Austin, Austin, Texas 78705
Daniel Mitchell: Information, Risk and Operations Management Department, McCombs School of Business, University of Texas at Austin, Austin, Texas 78705; Center for Analytics and Transformative Technologies, McCombs School of Business, University of Texas at Austin, Austin, Texas 78705

Operations Research, 2024, vol. 72, issue 5, 1827-1842

Abstract: We study networks of financial institutions where only aggregate information on liabilities is available. We introduce the robust liability network, that is, the network with the worst expected losses among all networks with the same aggregate liabilities and assets. We provide an algorithm to identify the robust liability network and, using aggregate data provided by bank holding companies to the Federal Reserve in form FR Y-9C, determine robust liability networks for U.S. banks under various network configurations. We find that the robust liability network is sparse, with links between institutions that hold highly correlated portfolios. We illustrate the potential of our approach in two ways: We study the evolution of robust liability networks around the onset of the COVID-19 pandemic and evaluate the importance of network structure for financial institutions that are subject to a regulation that limits risk-taking based on each institution’s conditional value-at-risk. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/opre.2022.0272 .

Keywords: Financial Engineering; banks; regulation; financial networks (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:5:p:1827-1842

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