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An agent-based model of payment systems

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  • Galbiati, Marco
  • Soramäki, Kimmo

Abstract

We lay out and simulate a multi-agent, multi-period model of an RTGS payment system. At the beginning of the day, banks choose how much costly liquidity to allocate to the settlement process. Then, they use it to execute an exogenous, random stream of payment orders. If a bank's liquidity stock is depleted, payments are queued until new liquidity arrives from other banks, imposing costs on the delaying bank. We study the equilibrium level of liquidity posted in the system, performing some comparative statics and obtaining insights on the efficiency of alternative system configurations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:dyncon:v:35:y:2011:i:6:p:859-875
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
    3. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 67-116.
    4. Evangelos Benos & Rodney J. Garratt & Peter Zimmerman, 2014. "The Role of Counterparty Risk in CHAPS Following the Collapse of Lehman Brothers," International Journal of Central Banking, International Journal of Central Banking, vol. 10(4), pages 143-172, December.
    5. Ball, Alan & Denbee, Edward & Manning, Mark & Wetherilt, Anne, 2011. "Financial Stability Paper No 11: Intraday Liquidity - Risk and Regulation," Bank of England Financial Stability Papers 11, Bank of England.
    6. De Caux, Robert & Brede, Markus & McGroarty, Frank, 2016. "Payment prioritisation and liquidity risk in collateralised interbank payment systems," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 139-150.
    7. Office of Financial Research (ed.), 2012. "Office of Financial Research 2012 Annual Report," Reports, Office of Financial Research, US Department of the Treasury, number 12-1, March.
    8. Biliana Alexandrova-Kabadjova & Francisco Solís-Robleda, 2013. "Managing Intraday Liquidity: The Mexican Experience," Working Papers 2013-01, Banco de México.
    9. Co-Pierre Georg, 2010. "The effect of the interbank network structure on contagion and financial stability," Global Financial Markets Working Paper Series 12-2010, Friedrich-Schiller-University Jena.
    10. 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.
    11. Yuri Biondi & Feng Zhou, 2017. "Interbank Credit and the Money Manufacturing Process. A Systemic Perspective on Financial Stability," Papers 1702.08774, arXiv.org.
    12. Triepels, Ron & Daniels, Hennie, 2016. "A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions," Discussion Paper 2016-037, Tilburg University, Center for Economic Research.
    13. Carlos Castro & Juan Sebastian Ordoñez, 2012. "A Network model of systemic risk: identifying the sources of dependence across institutions," DOCUMENTOS DE TRABAJO 009651, UNIVERSIDAD DEL ROSARIO.
    14. 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).
    15. repec:zbw:ifweej:20187 is not listed on IDEAS
    16. 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.
    17. B. Craig & D. Salakhova & M. Saldias, 2018. "Payments delay: propagation and punishment," Working papers 671, Banque de France.
    18. Co-Pierre Georg & Jenny Poschmann, 2010. "Systemic risk in a network model of interbank markets with central bank activity," Jena Economic Research Papers 2010-033, Friedrich-Schiller-University Jena.
    19. Mauro Napoletano & Jean-Luc Gaffard & Zakaria Babutsidze, 2012. "Agent Based Models A New Tool for Economic and Policy Analysis: A New Tool for Economic and Policy Analysis," Sciences Po publications 3, Sciences Po.
    20. Mauro Napoletano & Jean-Luc Gaffard & Zakaria Babutsidze, 2012. "Agent Based Models A New Tool for Economic and Policy Analysis," Working Papers hal-01070338, HAL.
    21. Jonnathan Cáceres Santos and René Aldazosa Inchauste, 2013. "Analizando el riesgo sistémico en Bolivia: una aplicación de modelos de topología de redes y simulación al funcionamiento del Sistema de Pagos de Alto Valor," Revista de Análisis del BCB, Banco Central de Bolivia, vol. 17(2(2012)-1), pages 45-80, January.
    22. Jakub Gorka, 2013. "Ryzyko w systemie platniczym. (Risk in the payment system.)," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 11(42), pages 111-123.

    More about this item

    Keywords

    Payment systems Liquidity RTGS Agent-based modelling Learning Fictitious play;

    JEL classification:

    • C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other

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