Robust Risk Quantification via Shock Propagation in Financial Networks

Given limited network information, we consider robust risk quantification under the Eisenberg–Noe model for financial networks. To be more specific, motivated by the fact that the structure of the interbank network is not completely known in practice, we propose a robust optimization approach to obtain worst-case default probabilities and associated capital requirements for a specific group of banks (e.g., systemically important financial institutions) under network information uncertainty. Using this tool, we analyze the effects of various incomplete network information structures on these worst-case quantities and provide regulatory insights into the collection of actionable network information. All claims are numerically illustrated using data from the European banking system.

Funding: The work of D. Ahn was supported by the Hong Kong Research Grants Council, University Grants Committee [Early Career Scheme Grant 24210420]. N. Chen acknowledges funding support from the Hong Kong Research Grants Council, University Grants Committee [General Research Fund Grant 14207918 and General Research Fund Grant 14208620]. The work by K.-K. Kim was supported by the National Research Foundation of Korea [Grant NRF-2019R1A2C1003144].

Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2020.0722.