Fitness model for the Italian interbank money market

G. De Masi, G. Iori, G. Caldarelli
2006 Physical Review E  
We use the theory of complex networks in order to quantitatively characterize the formation of communities in a particular financial market. The system is composed by different banks exchanging on a daily basis loans and debts of liquidity. Through topological analysis and by means of a model of network growth we can determine the formation of different group of banks characterized by different business strategy. The model based on Pareto's law makes no use of growth or preferential attachment
more » ... nd it reproduces correctly all the various statistical properties of the system. We believe that this network modeling of the market could be an efficient way to evaluate the impact of different policies in the market of liquidity. Coevolution and interaction between different agents is known to be one of the ingredients of the so-called complex systems. Several examples can be found in social ͓1,2͔, biological ͓3-6͔, economical ͓7͔, and technological systems ͓8͔. Any of these systems is composed by a set of agents competing and sometimes receiving reciprocal advantage interacting each other. In the above situation both coalition and competition are at the basis of the process of co-evolution and self-organization of the system. While this class of problems has been traditionally studied in game theory, more recently it has been introduced an approach based on graph theory ͓9,10͔ By using networks ͓11,12͔, we can characterize quantitatively the interaction between agents by means of a series of topological quantities. The case of study presented here is composed by banks operating in the Italian market ͓13͔. Banks try to maximize their returns given some constraints from the European Central Bank. This complex interaction results in a differentiation of the strategies that is well described by means of graph cliques. More specifically banks of the same size tend to form a cluster and to adopt a similar business strategy.
doi:10.1103/physreve.74.066112 pmid:17280126 fatcat:shan3teqovb2vjj3462wvxqsty