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Identifying systemically important financial institutions: a network approach

Author

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  • Pablo Rovira Kaltwasser

    (University of Leuven
    National Bank of Belgium)

  • Alessandro Spelta

    (University of Pavia
    Complexity Lab in Economics)

Abstract

The Basel Committee on Banking Supervision has proposed a methodology to identify Systemically Important Financial Institutions based on a series of indicators that should account for the externalities that these institutions place into the system. In this article we argue that the methodology chosen by Basel III maintains the micro-prudential focus of Basel I and II. We show how the PageRank algorithm that operates behind the Google search engine can be modified and applied to identify Systemically Important Financial Institutions. Being a feedback measure of systemic importance, the PageRank algorithm evaluates more than individual exposures. The algorithm is able to capture the risks that individual institutions place into the system, while at the same time, taking into account how the exposures at the system-wide level affect the ranking of individual institutions. In accordance to the Basel III framework, we are able to distinguish between systemic importance due to exposures born on the asset and on the liability side of the balance sheet of banks.

Suggested Citation

  • Pablo Rovira Kaltwasser & Alessandro Spelta, 2019. "Identifying systemically important financial institutions: a network approach," Computational Management Science, Springer, vol. 16(1), pages 155-185, February.
  • Handle: RePEc:spr:comgts:v:16:y:2019:i:1:d:10.1007_s10287-018-0327-8
    DOI: 10.1007/s10287-018-0327-8
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    2. HORIKAWA Takumi & MATSUI Yujiro & GEMMA Yasufumi, 2021. "A Network Analysis of the JGB Repo Market," Bank of Japan Working Paper Series 21-E-14, Bank of Japan.
    3. Martínez-Ventura, Constanza & Mariño-Martínez, Ricardo & Miguélez-Márquez, Javier, 2023. "Redundancy of Centrality Measures in Financial Market Infrastructures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(4).
    4. Clemente, Gian Paolo & Cornaro, Alessandra, 2022. "A multilayer approach for systemic risk in the insurance sector," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    5. Anastasios Demertzidis, 2019. "Interbank transactions on the intraday frequency: -Different market states and the effects of the financial crisis-," MAGKS Papers on Economics 201932, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    6. Gian Paolo Clemente & Alessandra Cornaro, 2020. "Assessing Systemic Risk in the Insurance Sector via Network Theory," Papers 2011.11394, arXiv.org.
    7. Ardekani, Aref Mahdavi & Distinguin, Isabelle & Tarazi, Amine, 2020. "Do banks change their liquidity ratios based on network characteristics?," European Journal of Operational Research, Elsevier, vol. 285(2), pages 789-803.

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    More about this item

    Keywords

    Systemic risk; Interbank market; Complex networks;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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