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Simulating retaliation in payment systems: Can banks control their exposure to a failing participant?


  • Elisabeth Ledrut


This paper assesses the impact of an operational failure at one of the biggest participants in the Dutch interbank payment system, varying the time at which the disruption takes place. Liquidity levels equal historical levels. The impact of such a disruption is quantified in terms of the additional liquidity needed in order to settle all payments than can settle given the banks' intraday reserves and collateral facilities. Assuming the disruption lasts for the remainder of the day, banks are faced with costs, as they need to borrow this additional liquidity overnight from the market or from the central bank. As could be expected, the second-round effect (the number of unsettled payments among healthy banks) of an operational disruption is highest when it occurs early during the day and lasts for the remainder of the day. So are the additional overnight liquidity needed and the costs of overnight credit. Furthermore, the paper introduces different possible reaction patterns from the stricken bank's counterparties. These counterparties can react according to two basic rules: they stop sending payments to the stricken bank either after some pre-determined time or after their exposure to the stricken bank reaches a certain level. From a cost perspective, reacting is more effective when determined by the individual exposure of the stricken banks' counterparties. However, even an immediate reaction does not prevent banks from running losses following the failure of a major participant. This leads to a reflection about the bilateral relations between the stricken bank, considered to be a node in a partial star network, and the other banks. How much each payment system participant can control its exposure to the stricken bank depends on the degree of reciprocity in the value of bilateral payments.

Suggested Citation

  • Elisabeth Ledrut, 2007. "Simulating retaliation in payment systems: Can banks control their exposure to a failing participant?," DNB Working Papers 133, Netherlands Central Bank, Research Department.
  • Handle: RePEc:dnb:dnbwpp:133

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    References listed on IDEAS

    1. Mazars, E. & Woelfel, G., 2005. "Analysis, by simulation, of the impact of a technical default of a payment system participant," Financial Stability Review, Banque de France, issue 6, pages 113-124, June.
    2. 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.
    3. Arco van Oord & Howie Lin, 2005. "Molling Inter- and Intraday Payment Flows," DNB Working Papers 074, Netherlands Central Bank, Research Department.
    4. Darcey McVanel, 2005. "The Impact of Unanticipated Defaults in Canada's Large Value Transfer System," Staff Working Papers 05-25, Bank of Canada.
    5. Lelyveld, Iman van & Liedorp, Franka, 2004. "Interbank Contagion in the Dutch Banking Sector," MPRA Paper 651, University Library of Munich, Germany, revised 11 Jul 2005.
    6. Leinonen, Harry, 2005. "Liquidity, risks and speed in payment and settlement systems : a simulation approach," Scientific Monographs, Bank of Finland, number 2005_031, November.
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    Cited by:

    1. Sean O'Connor & James Chapman & Kirby Millar, 2008. "Liquidity Efficiency and Distribution in the LVTS: Non-Neutrality of System Changes under Network Asymmetry," Discussion Papers 08-11, Bank of Canada.
    2. Ronald Heijmans, 2009. "Simulations in the Dutch interbank payment system: A sensitivity analysis," DNB Working Papers 199, Netherlands Central Bank, Research Department.
    3. Schulz, Christian, 2011. "Liquidity requirements and payment delays - participant type dependent preferences," Working Paper Series 1291, European Central Bank.

    More about this item


    payment system; operational disruption; liquidity;

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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