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Exploring Agent-Based Methods for the Analysis of Payment Systems: A Crisis Model for StarLogo TNG

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  • Luca Arciero

  • Claudia Biancotti

  • Leandro D'Aurizio

  • Claudio Impenna

Abstract

This paper presents an exploratory agent-based model of a real time gross settlement (RTGS) payment system. Banks are represented as agents who exchange payment requests, which are then settled according to a set of simple rules. The model features the main elements of a real-life system, including a central bank acting as liquidity provider, and a simplified money market. A simulation exercise using synthetic data of BI-REL (the Italian RTGS) predicts the macroscopic impact of a disruptive event on the flow of interbank payments. In our reduced-scale system, three hypothetical distinct phases emerge after the disruptive event: 1) a liquidity sink effect is generated and the participants' liquidity expectations turn out to be excessive; 2) an illusory thickening of the money market follows, along with increased payment delays; and, finally 3) defaulted obligations dramatically rise. The banks cannot staunch the losses accruing on defaults, even after they become fully aware of the critical event, and a scenario emerges in which it might be necessary for the central bank to step in as liquidity provider.

Suggested Citation

  • Luca Arciero & Claudia Biancotti & Leandro D'Aurizio & Claudio Impenna, 2009. "Exploring Agent-Based Methods for the Analysis of Payment Systems: A Crisis Model for StarLogo TNG," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-2.
    • Handle: RePEc:jas:jasssj:2008-12-2
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    1. repec:ofr:report:12-1 is not listed on IDEAS
    2. Krug, Sebastian & Wohltmann, Hans-Werner, 2016. "Shadow banking, financial regulation and animal spirits: An ACE approach," Economics Working Papers 2016-08, Christian-Albrechts-University of Kiel, Department of Economics.
    3. Richard Bookstaber, 2012. "Using Agent-Based Models for Analyzing Threats to Financial Stability," Working Papers 12-03, Office of Financial Research, US Department of the Treasury.
    4. Krug, Sebastian, 2018. "The interaction between monetary and macroprudential policy: Should central banks 'lean against the wind' to foster macro-financial stability?," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-69.
    5. 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.
    6. Leonardo dos Santos Pinheiro & Flavio Codeco COelho, 2017. "An Agent-based Model of Contagion in Financial Networks," Papers 1703.07513, arXiv.org.
    7. Office of Financial Research (ed.), 2012. "2012 Annual Report," Reports, Office of Financial Research, US Department of the Treasury, number 12-01.
    8. Krug, Sebastian, 2015. "The interaction between monetary and macroprudential policy: Should central banks "lean against the wind" to foster macrofinancial stability?," Economics Working Papers 2015-08, Christian-Albrechts-University of Kiel, Department of Economics.
    9. Pablo S. Castro & Ajit Desai & Han Du & Rodney Garratt & Francisco Rivadeneyra, 2021. "Estimating Policy Functions in Payments Systems Using Reinforcement Learning," Staff Working Papers 21-7, Bank of Canada.

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    Keywords

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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