Unconventional monetary policy measures included fixed-rate full allotment since October 2008; swap agreements with other central banks (e.g., Federal Reserve, Swiss National Bank); extension of the collateral framework; extension of the duration of the refinancing operations (e.g., year tenders starting July 2009 and three-year tenders starting December 2011); the introduction of the Covered Bond Purchase Program (May 2009), the Securities Market Program (May 2010), and the Outright Monetary Transactions (September 2012).

Unconventional monetary policy measures included fixed-rate full allotment since October 2008; swap agreements with other central banks (e.g., Federal Reserve, Swiss National Bank); extension of the collateral framework; extension of the duration of the refinancing operations (e.g., year tenders starting July 2009 and three-year tenders starting December 2011); the introduction of the Covered Bond Purchase Program (May 2009), the Securities Market Program (May 2010), and the Outright Monetary Transactions (September 2012).

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 then 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. In our reducedscale 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.

Agent-based models (ABMs) are quite new in the modeling landscape; they emerged on the scene in the 1990s. ABMs have a clear advantage over other approaches: they create the capacity to manage learning processes in agents and discover novelties in their behavior. In addition to bounded rationality assumptions, ABMs share a number of peculiar characteristics: first of all, a bottom-up perspective is assumed where the properties of macro-dynamics are emergent properties of micro-dynamics involving individuals as heterogeneous agents who live in complex systems that evolve through time. To apply this framework to financial crisis analysis, a simplified implementation of the SWARM protocol (www.swarm.org), based on Python, is introduced. The result is the Swarm-Like Agent Protocol in Python (SLAPP). Using SLAPP, it is possible to focus on natural phenomena and social behavior. In the case of this chapter, the authors focus on the banking system, recreating the interactions of a community of financial institutions that act in the payment system and in the interbank market for short-term liquidity.

With the advent of Large Value Interbank Fund Transfer Systems operating on an RTGS basis, the bank liquidity management problem has become a crucial issue in payment system analysis for its interrelations with the key monetary policy variables, namely, the short-term interbank interest rate. The analysis of the RTGS system is far from being a trivial task due—mostly—to the complexity and the endogeneity. These stem from the multiplicity of heterogeneous participants (complexity) usually joining a system, whose decisions produce a spillover effect on the rest of the system, which prevents any participant from solving its liquidity demand problem in isolation (endogeneity). Agent Based Models seem to present a few advantages in analysing the payment system in comparison to microfounded ones, as well as to standard simulations: behavioural rules can be assigned to a multiplicity of banks defining the lending or borrowing timing as well as the liquidity sources. Therefore, Agent Based Modelling seems to represent an additional instrument by which to analyse the connection between the payment system and the functioning of one of the most important liquidity sources, the interbank money market.