INTRODUCTION

According to a consolidated definition, “a pay­ment system consists of a set of instruments, banking procedures and, typically, interbank funds transfer systems that ensure the circulation

DOI: 10.4018/978-1-4666-6268-1.ch012 of money among the economic agents” (Bank for International Settlement, 2001).

Banks lie at the heart of the payment system allowing economic agents to deliver funds to their counterparties either through bilateral arrange­ments (i.e. correspondent banking) or through mul­tilateral arrangements, the Interbank Fund Transfer Systems (IFTSs). IFTSs are formal arrangements among banks (but also comprising other eligible participants) and a settlement agent who holds settlement account on behalf of participants and provides them with the settlement asset need to comply with their reciprocal obligations. Due to their systemic importance, large value IFTSs are mainly managed by central banks, which allow participants to use cash deposited at their reserve accounts to fund their payment obligations. With a view to reducing systemic risk, central banks have been progressively adopting Real Time Gross Settlement (RTGS) systems where each payment instruction is settled individually on gross basis as soon as it enters the system, provided that the sender bank has sufficient liquidity on its account. RTGS systems, while virtually eliminating credit risk, impose participants to bear a significant li­quidity cost, since they need to maintain adequate settlement asset in their accounts in order to process smoothly their payment instructions without any significant delay². Besides the balance held at their reserve accounts, banks may rely on other liquidity sources: they can condition the submis­sion of outgoing payment to incoming payments from their counterparties; they can access the daylight credit usually granted from the central bank; they can also borrow and lend funds on the money market to manage mismatching between the incoming and outgoing payments.

.

Investigating how the RTGS participants choose their liquidity mix represent a key issue for central banking, which-so far-has been basically investigated through the standard tools available at policy makers and academics: neoclassical theoretical models and simulations tools.

As shown below in detail, these approaches have been proven to offer useful insights on the functioning of RTGS systems, but they do not seem fully able to cope with all the difficulties, which emerge when studying the behaviour of banks in a RTGS large value payment system: in particular, the neoclassical theoretical models face difficulties in dealing with the fact that: 1) payment flows in RTGS systems do not follow any nice distribution from the exponential family but complex dynamics for which is difficult to derive analytical solutions; 2) the spillover effects each bank’s decision produces on the rest of the system which prevent any system participant from solving its liquidity demand problem in isolation; 3) the incentives for banks to choose their liquidity mix (e.g. pay early versus late) are heavily influenced by the system design which has become increasingly complex due to the introduction of sophisticated liquidity saving features; 4) the players are affected by a high degree of heterogeneity.

On the other hand, the empiric al literature based on simulation tools has not properly addressed the endogeneity issues, which arise in this context, where banks change their behaviour according to the evolution of the underlying environment.

Against this background, the toolkit available at policymakers and academics for analysing the functioning of RTGS systems needs to be enhanced: as we are going to show throughout this chapter, Agent Based Modelling represents an alternative approach, which appears to be very attractive. In fact, Agent B ased Modelling makes it possible to represent each economic agent, i.e. each individual element of the system by way of a set of micro-level rules governing its behaviour both in isolation and in interaction with other agents, which evolve in parallel allowing the emergence of credible macro-level dynamics under minimal assumptions on individual behaviour and without the need to optimise any utility function.

The rest of the chapter is organised as fol­lows: the next section presents a brief review of the literature on large-value payments system spotting how the introduction of RTGS system has made more complex the task of academics and policy makers and calling for the application of Agent Based Modeling as a way to overcome the weaknesses of the standard analytical tools. Section 3 reviews a selection of the main existing contributions dealing with RTGS systems relying on Agent Based Model. The following paragraph puts forward some ideas to improve the reviewed ABM analysis of RTGS systems and, finally, the last section offers concluding remarks.

2.