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SinkRank: An algorithm for identifying systemically important banks in payment systems

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  • Soramäki, Kimmo
  • Cook, Samantha

Abstract

The ability to accurately estimate the extent to which the failure of a bank disrupts the financial system is very valuable for regulators of the financial system. One important part of the financial system is the interbank payment system. This paper develops a robust measure, SinkRank, that accurately predicts the magnitude of disruption caused by the failure of a bank in a payment system and identifies banks most affected by the failure. SinkRank is based on absorbing Markov chains, which are well-suited to model liquidity dynamics in payment systems. Because actual bank failures are rare and the data is not generally publicly available, the authors test the metric by simulating payment networks and inducing failures in them. They test SinkRank on several types of payment networks, including Barabási-Albert types of scale-free networks modeled on the Fedwire system, and find that the failing bank's SinkRank is highly correlated with the resulting disruption in the system overall; moreover, the SinkRank algorithm can identify which individual banks would be most disrupted by a given failure.

Suggested Citation

  • Soramäki, Kimmo & Cook, Samantha, 2013. "SinkRank: An algorithm for identifying systemically important banks in payment systems," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-27.
  • Handle: RePEc:zbw:ifweej:201328
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    File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2013-28
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    File URL: https://www.econstor.eu/bitstream/10419/77858/1/750624124.pdf
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    References listed on IDEAS

    as
    1. Galbiati, Marco & Soramäki, Kimmo, 2011. "An agent-based model of payment systems," Journal of Economic Dynamics and Control, Elsevier, vol. 35(6), pages 859-875, June.
    2. Docherty, Peter & Wang, Gehong, 2010. "Using synthetic data to evaluate the impact of RTGS on systemic risk in the Australian payments system," Journal of Financial Stability, Elsevier, vol. 6(2), pages 103-117, June.
    3. Schulz, Christian, 2011. "Liquidity requirements and payment delays - participant type dependent preferences," Working Paper Series 1291, European Central Bank.
    4. Angelini, P. & Maresca, G. & Russo, D., 1996. "Systemic risk in the netting system," Journal of Banking & Finance, Elsevier, vol. 20(5), pages 853-868, June.
    5. Morten L. Bech & Christine Preisig & Kimmo Soramäki, 2008. "Global trends in large-value payments," Economic Policy Review, Federal Reserve Bank of New York, issue Sep, pages 59-81.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Aldasoro, Iñaki & Alves, Iván, 2015. "Multiplex interbank networks and systemic importance: An application to European data," SAFE Working Paper Series 102 [rev.], Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    2. Carlos León & Jhonatan Pérez & Luc Renneboog, 2014. "A multi-layer network of the sovereign securities market," BORRADORES DE ECONOMIA 012036, BANCO DE LA REPÚBLICA.
    3. Bernardo Bravo-Benitez & Biliana Alexandrova-Kabadjova & Serafin Martinez-Jaramillo, 2016. "Centrality Measurement of the Mexican Large Value Payments System from the Perspective of Multiplex Networks," Computational Economics, Springer;Society for Computational Economics, vol. 47(1), pages 19-47, January.
    4. Sam Langfield & Kimmo Soramäki, 2016. "Interbank Exposure Networks," Computational Economics, Springer;Society for Computational Economics, vol. 47(1), pages 3-17, January.
    5. repec:eee:finsta:v:35:y:2018:i:c:p:17-37 is not listed on IDEAS
    6. Kuzubaş, Tolga Umut & Saltoğlu, Burak & Sever, Can, 2016. "Systemic risk and heterogeneous leverage in banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 358-375.
    7. Carlos León & Constanza Martínez & Freddy Cepeda, 2015. "Short-Term Liquidity Contagion in the Interbank Market," Borradores de Economia 920, Banco de la Republica de Colombia.
    8. repec:eee:finsta:v:33:y:2017:i:c:p:346-365 is not listed on IDEAS
    9. León, Carlos & Berndsen, Ron J., 2014. "Rethinking financial stability: Challenges arising from financial networks’ modular scale-free architecture," Journal of Financial Stability, Elsevier, vol. 15(C), pages 241-256.
    10. Bernardo Bravo-Benitez & Biliana Alexandrova-Kabadjova & Serafin Martinez-Jaramillo, 2016. "Centrality Measurement of the Mexican Large Value Payments System from the Perspective of Multiplex Networks," Computational Economics, Springer;Society for Computational Economics, vol. 47(1), pages 19-47, January.
    11. repec:eee:finsta:v:35:y:2018:i:c:p:75-92 is not listed on IDEAS
    12. Peter Sarlin & Carlos León & Clara Machado & Andrés Murcia, 2016. "Assessing Systemic Importance With a Fuzzy Logic Inference System," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 121-153, January.
    13. Seungjin Baek & Kimmo Soramäki & Jaeho Yoon, 2014. "Network Indicators for Monitoring Intraday Liquidity in BOK-Wire+," Working Papers 2014-1, Economic Research Institute, Bank of Korea.

    More about this item

    Keywords

    payment systems; systemic importance; graph theory; Markov distance; absorbing system;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G01 - Financial Economics - - General - - - Financial Crises
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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