Conference Agenda

I use network modelling of systemic risk to set macroprudential buffers from an operational perspective. I focus on the countercyclical capital buffer, an instrument designed to protect the banking sector from periods of excessive growth associated with a build-up of system-wide risk. I construct an indicator of financial vulnerability with a model of fire sales, which captures the spillover losses in the system caused by deleveraging and joint liquidation of illiquid assets. Using data on the U.S. bank holding companies, I show that the indicator is informative about the build-up of vulnerability and can be useful for setting the countercyclical capital buffer.

This paper investigates systemic risk measures for stochastic financial networks of explicitly modelled bilateral liabilities. We extend the notion of systemic risk measures from Biagini, Fouque, Fritelli and Meyer-Brandis (2019) to graph structured data. In particular, we focus on an aggregation function that is derived from a market clearing algorithm proposed by Eisenberg and Noe (2001). In this setting, we show the existence of an optimal random allocation that distributes the overall minimal bailout capital and secures the network. We study numerical methods for the approximation of systemic risk and optimal random allocations. We propose to use permutation equivariant architectures of neural networks like graph neural networks (GNNs) and a class that we name (extended) permutation equivariant neural networks ((X)PENNs). We compare their performance to several benchmark allocations. The main feature of GNNs and (X)PENNs is that they are permutation equivariant with respect to the underlying graph data. In numerical experiments we find evidence that these permutation equivariant methods are superior to other approaches.