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Filtering for risk assessment of interbank network

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  • Simaan, Majeed
  • Gupta, Aparna
  • Kar, Koushik

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.

Suggested Citation

  • Simaan, Majeed & Gupta, Aparna & Kar, Koushik, 2020. "Filtering for risk assessment of interbank network," European Journal of Operational Research, Elsevier, vol. 280(1), pages 279-294.
    • Handle: RePEc:eee:ejores:v:280:y:2020:i:1:p:279-294
    DOI: 10.1016/j.ejor.2019.06.049
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    Citations

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    Cited by:

    1. Tang, Qihe & Tong, Zhiwei & Xun, Li, 2022. "Insurance risk analysis of financial networks vulnerable to a shock," European Journal of Operational Research, Elsevier, vol. 301(2), pages 756-771.
    2. Morteza Alaeddini & Philippe Madiès & Paul J. Reaidy & Julie Dugdale, 2023. "Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 573-654, April.
    3. Yfanti, Stavroula & Karanasos, Menelaos & Zopounidis, Constantin & Christopoulos, Apostolos, 2023. "Corporate credit risk counter-cyclical interdependence: A systematic analysis of cross-border and cross-sector correlation dynamics," European Journal of Operational Research, Elsevier, vol. 304(2), pages 813-831.
    4. Gupta, Aparna & Wang, Runzu & Lu, Yueliang, 2021. "Addressing systemic risk using contingent convertible debt – A network analysis," European Journal of Operational Research, Elsevier, vol. 290(1), pages 263-277.
    5. Mayank Kejriwal & Yuesheng Luo, 2022. "On the Empirical Association between Trade Network Complexity and Global Gross Domestic Product," Papers 2211.13117, arXiv.org.

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