The recent stream of literature of systemic risk in financial markets  emphasized the key importance of considering the complex interconnections among financial institutions. Much efforts has been put to model the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets---either using real data, reconstruction techniques or simple toy networks. Here we address the more general problem of how the shock propagation dynamics depends on the topological details of the underlying network. To this end , we consider different  network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks with varying density and mesoscale structures, and vary as well the details of the shock propagation dynamics. We show that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can thus aid in the design of regulatory policies to improve the robustness of financial markets.
A growing body of studies on systemic risk in financial markets has emphasized the key importance of taking into consideration the complex interconnections among financial institutions. Much effort has been put in modeling the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets - either using real network data, reconstruction techniques or simple toy networks. Here we address the more general problem of how shock propagation dynamics depends on the topological details of the underlying network. To this end we consider different realistic network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks of varying density and with different block structures, and diversify as well in the details of the shock propagation dynamics. We confirm that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can aid in the design of regulatory policies to improve the robustness of financial markets.
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