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 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. The main advantage of agent - based modeling is that we can dynamically see what happens to the major variables involved. 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. The methodology presented differs from traditional payment systems simulations featuring deterministic streams of payments dealt with in a centralized manner with static behavior on the part of banks. The paper is within a recent stream of empirical research that attempts to model RTGS with agent – based techniques.
Published in 2009 in: Journal of Artificial Societies and Social Simulation, v. 12, 1
