
This paper introduces a formulation of the optimal network compression problem for financial systems. This general formulation is presented for different levels of network compression or rerouting allowed from the initial interbank network. We prove that this problem is, generically, NP-hard. We focus on objective functions generated by systemic risk measures under shocks to the financial network. We use this framework to study the (sub)optimality of the maximally compressed network. We conclude by studying the optimal compression problem for specific networks; this permits us to study, e.g., the so-called robust fragility of certain network topologies more generally as well as the potential benefits and costs of network compression. In particular, under systematic shocks and heterogeneous financial networks the robust fragility results of Acemoglu et al. (2015) no longer hold generally.
34 pages, 10 figures
FOS: Computer and information sciences, Computer Science - Machine Learning, financial networks, finance, portfolio compression, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Machine Learning (cs.LG), systematic shocks, FOS: Economics and business, Financial networks (including contagion, systemic risk, regulation), Risk Management (q-fin.RM), systemic risk, FOS: Electrical engineering, electronic engineering, information engineering, Quantitative Finance - Risk Management
FOS: Computer and information sciences, Computer Science - Machine Learning, financial networks, finance, portfolio compression, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Machine Learning (cs.LG), systematic shocks, FOS: Economics and business, Financial networks (including contagion, systemic risk, regulation), Risk Management (q-fin.RM), systemic risk, FOS: Electrical engineering, electronic engineering, information engineering, Quantitative Finance - Risk Management
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