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Disruptions in Large-Value Payment Systems: An Experimental Approach

Author

Listed:
  • Klaus Abbink

    (Monash University)

  • Ronald Bosman

    (VU University)

  • Ronald Heijmans

    (De Nederlandsche Bank)

  • Frans van Winden

    (CREED, University of Amsterdam)

Abstract

This experimental study investigates the individual behavior of banks in a large-value payment system. More specifically, we look at (i) the reactions of banks to disruptions in the payment system, (ii) the way in which the history of disruptions affects the behavior of banks (path dependency), and (iii) the effect of more concentration in the payment system (heterogeneous market versus a homogeneous market). The game used in this experiment is a stylized version of a model of Bech and Garratt (2006) in which each bank can choose between paying in the morning (efficient) or in the afternoon (inefficient). The results show that there is significant path dependency in terms of disruption history. Also, the chance of disruption influences the behavior of the participants. Once the system is moving towards the inefficient equilibrium, it does not easily move back to the efficient one. Furthermore, there is a clear leadership effect in the heterogeneous market.

Suggested Citation

  • Klaus Abbink & Ronald Bosman & Ronald Heijmans & Frans van Winden, 2017. "Disruptions in Large-Value Payment Systems: An Experimental Approach," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 63-95, December.
    • Handle: RePEc:ijc:ijcjou:y:2017:q:4:a:3
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    Citations

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    Cited by:

    1. Virgilio B Pasin & Anna Wyllie, 2025. "Net Send Limits in the Lynx Payment System: Usage and Implications," Discussion Papers 2025-13, Bank of Canada.
    2. Neville Arjani & Ronald Heijmans, 2020. "Is there anybody out there? Detecting operational outages from LVTS transaction data," Working Papers 683, DNB.
    3. Jan Paulick & Ron Berndsen & Martin Diehl & Ronald Heijmans, 2024. "No more tears without tiers? The impact of indirect settlement on liquidity use in TARGET2," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(2), pages 425-458, May.
    4. Ronald Heijmans & Chen Zhou, 2019. "Outlier detection in TARGET2 risk indicators," DNB Working Papers 624, Netherlands Central Bank, Research Department.
    5. Ronald Heijmans & Richard Heuver, 2011. "Is this bank ill? The diagnosis of doctor TARGET2," DNB Working Papers 316, Netherlands Central Bank, Research Department.
    6. Ronald Heijmans & Richard Heuver & Clement Levallois & Iman van Lelyveld, 2014. "Dynamic visualization of large transaction networks: the daily Dutch overnight money market," DNB Working Papers 418, Netherlands Central Bank, Research Department.
    7. Tong Mu & He Yi, 2017. "Topology of Complex Networks and Demand of Intraday Liquidity: Based on the Real-Time Gross Settlement System," Central European Economic Journal, Sciendo, vol. 2(49), pages 50-61, December.
    8. Evangelos Benos & Gerardo Ferrara & Pedro Gurrola-Perez, 2017. "The impact of de-tiering in the United Kingdom’s large-value payment system," Bank of England working papers 676, Bank of England.
    9. Bewaji, Oluwasegun, 2024. "A computational model of bilateral credit limits in payment systems and other financial market infrastructures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(1).
    10. Sabetti, Leonard & Heijmans, Ronald, 2021. "Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).
    11. Arifovic, Jasmina & Jiang, Janet Hua, 2019. "Strategic uncertainty and the power of extrinsic signals– evidence from an experimental study of bank runs," Journal of Economic Behavior & Organization, Elsevier, vol. 167(C), pages 1-17.
    12. Peter Heemeijer & Ronald Heijmans, 2015. "Central bank intervention in large value payment systems: An experimental approach," DNB Working Papers 466, Netherlands Central Bank, Research Department.
    13. Shaun Byck & Ronald Heijmans, 2020. "How much liquidity would a liquidity-saving mechanism save if a liquidity-saving mechanism could save liquidity? A simulation approach for Canada's large-value payment system Shaun Byck," Working Papers 682, DNB.

    More about this item

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D70 - Microeconomics - - Analysis of Collective Decision-Making - - - General
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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