  This paper provides the first empirical network analysis of the Argentine
interbank money market. Its main topological features are examined applying
graph theory, focusing on the unsecured overnight loans settled from 2003 to
2017. The network, where banks are the nodes and the operations between them
represent the links, exhibits low density, a higher reciprocity than comparable
random graphs, short average distances and its clustering coefficient remains
above that of a random network of equal size. Furthermore, the network is
prominently disassortative. Its structural metrics experienced significant
volatility, in correlation with the economic activity fluctuations and
regulatory shifts. Signs of nodes' random-like behavior are detected during
contractions. The degree distributions fit better to a Lognormal distribution
than to a Poisson or a Power Law. Additionally, different node centrality
measures are computed. It is found that a higher centrality enables a node to
settle more convenient bilateral interest rates compared to the average market
rate, identifying a statistical and economically significant effect by means of
a regression analysis. These results constitute a relevant input for systemic
risk assessment and provide solid empirical foundations for future theoretical
modelling and shock simulations, especially in the context of underdeveloped
financial systems.
