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An agent-based model of payment systems

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  • Galbiati, Marco

    ()
    (Bank of England)

  • Soramaki, Kimmo

    ()
    (Helsinki University of Technology)

Abstract

This paper lays out and simulates 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. The paper studies the equilibrium level of liquidity posted in the system, performing some comparative statics and obtaining: i) a liquidity demand curve which links liquidity to delay costs and ii) insights on the efficiency of alternative system configurations.

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Bibliographic Info

Paper provided by Bank of England in its series Bank of England working papers with number 352.

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Length: 26 pages
Date of creation: Aug 2008
Date of revision:
Handle: RePEc:boe:boeewp:0352

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Related research

Keywords: Payment systems; liquidity; RTGS; agent-based modelling; learning; fictitious play.;

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References

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  1. Angelini, Paolo, 1998. "An analysis of competitive externalities in gross settlement systems," Journal of Banking & Finance, Elsevier, vol. 22(1), pages 1-18, January.
  2. Chakravorti, Sujit, 2000. "Analysis of systemic risk in multilateral net settlement systems," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(1), pages 9-30, January.
  3. Charles M. Kahn & William Roberds, 1997. "Payment system settlement and bank incentives," Proceedings 537, Federal Reserve Bank of Chicago.
  4. Dindos, Martin & Mezzetti, Claudio, 2006. "Better-reply dynamics and global convergence to Nash equilibrium in aggregative games," Games and Economic Behavior, Elsevier, vol. 54(2), pages 261-292, February.
  5. Bech, Morten L. & Garratt, Rod, 2003. "The intraday liquidity management game," Journal of Economic Theory, Elsevier, vol. 109(2), pages 198-219, April.
  6. Drew Fudenberg & David K. Levine, 1996. "The Theory of Learning in Games," Levine's Working Paper Archive 624, David K. Levine.
  7. Kukushkin, Nikolai S., 2004. "Best response dynamics in finite games with additive aggregation," Games and Economic Behavior, Elsevier, vol. 48(1), pages 94-110, July.
  8. 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.
  9. Simon Buckle & Erin Campbell, 2003. "Settlement bank behaviour and throughput rules in an RTGS payment system with collateralised intraday credit," Bank of England working papers 209, Bank of England.
  10. Marco Galbiati & Simone Giansante, 2010. "Emergence of networks in large value payment systems (LVPSs)," Department of Economic Policy, Finance and Development (DEPFID) University of Siena 0110, Department of Economic Policy, Finance and Development (DEPFID), University of Siena.
  11. John P. Jackson & Mark J. Manning, 2007. "Central Bank intraday collateral policy and implications for tiering in rtgs payment systems," DNB Working Papers 129, Netherlands Central Bank, Research Department.
  12. 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.
  13. Morten L. Bech & Rodney J. Garratt, 2012. "Illiquidity in the Interbank Payment System Following Wide‐Scale Disruptions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(5), pages 903-929, 08.
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Citations

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Cited by:
  1. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," Working Papers 07/2012, University of Verona, Department of Economics.
  2. 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, Max-Planck-Institute of Economics.
  3. 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.
  4. Hellqvist , Matti & Laine, Tatu, 2012. "Diagnostics for the financial markets – computational studies of payment system: Simulator Seminar Proceedings 2009–2011," Scientific Monographs E:45/2012, Bank of Finland.
  5. 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.
  6. 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.
  7. 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.
  8. 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, vol. 7(28), pages 1-27.

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