Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction

TL;DR

This paper introduces a maximum entropy-based method to assess systemic risk from fire sales spillover in financial markets using limited data, enabling regulators to evaluate bank vulnerabilities and systemicness over time.

Contribution

It develops a novel approach to estimate systemic risk metrics with partial information, improving risk assessment when detailed portfolio data are unavailable.

Findings

Effective in assessing systemic risk with limited data

Able to detect changes in systemicness and vulnerability over time

Validated on US bank data from 2001-2013

Abstract

Assessing systemic risk in financial markets is of great importance but it often requires data that are unavailable or available at a very low frequency. For this reason, systemic risk assessment with partial information is potentially very useful for regulators and other stakeholders. In this paper we consider systemic risk due to fire sales spillover and portfolio rebalancing by using the risk metrics defined by Greenwood et al. (2015). By using the Maximum Entropy principle we propose a method to assess aggregated and single bank's systemicness and vulnerability and to statistically test for a change in these variables when only the information on the size of each bank and the capitalization of the investment assets are available. We prove the effectiveness of our method on 2001-2013 quarterly data of US banks for which portfolio composition is available.