Filtering for risk assessment of interbank network |
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| Institution: | 1. School of Business, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA;2. Lally School of Management, Rensselaer Polytechnic Institute, 110 8th Street, Pittsburgh Building, Troy, NY 12180, USA;3. Division of Economic and Risk Analysis, US Securities and Exchange Commission, 100 F St NE, Washington DC 20549, USA;4. Department of Electrical, Computer & Systems Engineering, Rensselaer Polytechnic Institute, Jonsson Engineering Center 6048, Troy, NY 12180, USA;1. Carey Business School, Johns Hopkins University, 100 International Drive, Baltimore, MD 21202, United States;2. Department of Information Systems and Business Analytics, Florida International University, Miami, FL 33199, United States;1. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA;2. School of Information Science and Technology, Osaka University, Suita, Japan;3. Department of Economics, Rice University, Houston, TX, USA;4. Department of Economics, University of Arkansas, Fayetteville, AR, USA;1. College of Auditing and Evaluation, Nanjing Audit University, Nanjing, Jiangsu Province, 211815, China;2. Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada;3. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA 01609, USA;1. Universidade Federal de Minas Gerais, Departamento de Ciência da Computação, Belo Horizonte, Brazil;2. Universidade Federal de Lavras, Departamento de Ciência da Computação, Lavras, Brazil;1. Department of Finance, National Central University, Taiwan;2. Department of Finance, National Taiwan University, Taiwan;3. Department of International Business, National Taiwan University, Taiwan;4. Chinese Academy of Mathematics and Systems Science, China |
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| Abstract: | Our paper contributes to the recent macroprudential policy addressing the resilience of financial systems in terms of their interconnectedness. We argue that beneath an interbank market, there is a fundamental latent network that affects the liquidity distributions among banks. To investigate the interbank market, we propose a framework that identifies such latent network using a statistical learning procedure. The framework reverse engineers overnight signals observed as banks conduct their reserve management on a daily basis. Our simulation-based results show that possible disruptions in funds supply are highly affected by the interconnectedness of the latent network. Hence, the proposed framework serves as an early warning system for regulators to monitor the overnight market and to detect ex-ante possible disruptions based on the inherent network characteristics. |
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