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Algorithm for identifying systemically important banks in payment systems


  • Soramäki, Kimmo
  • Cook, Samantha


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. The authors use two metrics to evaluate the magnitude of the disruption: the duration of delays in the system (Congestion) aggregated over all banks and the average reduction in available funds of the other banks due to the failing bank (Liquidity dislocation). The authors test SinkRank on Barabasi-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 technology can identify which individual banks would be most disrupted by a given failure.

Suggested Citation

  • Soramäki, Kimmo & Cook, Samantha, 2012. "Algorithm for identifying systemically important banks in payment systems," Economics Discussion Papers 2012-43, Kiel Institute for the World Economy (IfW).
  • Handle: RePEc:zbw:ifwedp:201243

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    References listed on IDEAS

    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. Soramäki, Kimmo & Bech, Morten L. & Arnold, Jeffrey & Glass, Robert J. & Beyeler, Walter E., 2007. "The topology of interbank payment flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 317-333.
    4. Galos, Peter & Soramäki, Kimmo, 2005. "Systemic risk in alternative payment system designs," Working Paper Series 508, European Central Bank.
    5. Schulz, Christian, 2011. "Liquidity requirements and payment delays - participant type dependent preferences," Working Paper Series 1291, European Central Bank.
    6. Beyeler, Walter E. & Glass, Robert J. & Bech, Morten L. & Soramäki, Kimmo, 2007. "Congestion and cascades in payment systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 693-718.
    7. 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.
    8. 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.
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    More about this item


    Systemic risk; interbank payment system; liquidity; Markov chains; simulation;

    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
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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