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Designing large value payment systems: an agent based approach

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  • Jing Yang
  • Sheri Markose
  • Amadeo Alentorn

Abstract

In this paper, we report on the main building blocks of an ongoing project to develop a computational agent-based simulator for a generic real-time large-value interbank payment system with a central processor that can implement different rules for payment settlement. The main types of payment system in their polar forms are Real Time Gross Settlement (RTGS) and Deferred Net Settlement (DNS). DNS generates large quantities of settlement risk; in contrast, the elimination of settlement risk in RTGS comes with excessive demands for liquidity on banks. This could lead them to adopt various delaying tactics to minimise liquidity needs with free-riding and other ‘bad’ equilibria as potential outcomes. The introduction of hybrid systems with real-time netting is viewed as a means by which liquidity costs can be reduced while settlement risk is unchanged. Proposed reforms for settlement rules make it imperative to have a methodology to assess the efficiency of the different variants along three dimensions: the cost of liquidity to the individual banks and the system as a whole, settlement risk at both bank and system levels, and how early in the day payments are processed, since this proxies the impact of an operational incident. In this paper, we build a simulator for interbank payments capable of handling real time payment records along with autonomous bank behaviour and show that it can be used to evaluate different payment system designs against these three criteria

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 396.

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Date of creation: 11 Nov 2005
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Handle: RePEc:sce:scecf5:396

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Web page: http://comp-econ.org/
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Keywords: Agent based modeling; Real Time Gross Settlement; Deferred Net Settlement; Agent-based simulation; Payment Concentration; Liquidity; Systemic Risk;

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  1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
  2. Bech, Morten L. & Garratt, Rod, 2001. "The Intraday Liquidity Management Game," University of California at Santa Barbara, Economics Working Paper Series qt0m6035wg, Department of Economics, UC Santa Barbara.
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Cited by:
  1. 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 2.
  2. Leonardo Bartolini & Spence Hilton & James McAndrews, 2008. "Settlement delays in the money market," Staff Reports 319, Federal Reserve Bank of New York.

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