Petrone, Daniele;
Rodosthenous, Neofytos;
Latora, Vito;
(2022)
An AI approach for managing financial systemic risk via bank bailouts by taxpayers.
Nature Communications
, 13
(1)
, Article 6815. 10.1038/s41467-022-34102-1.
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Abstract
Bank bailouts are controversial governmental decisions, putting taxpayers’ money at risk to avoid a domino effect through the network of claims between financial institutions. Yet very few studies address quantitatively the convenience of government investments in failing banks from the taxpayers’ standpoint. We propose a dynamic financial network framework incorporating bailout decisions as a Markov Decision Process and an artificial intelligence technique that learns the optimal bailout actions to minimise the expected taxpayers’ losses. Considering the European global systemically important institutions, we find that bailout decisions become optimal only if the taxpayers’ stakes exceed some critical level, endogenously determined by all financial network’s characteristics. The convenience to intervene increases with the network’s distress, taxpayers’ stakes, bank bilateral credit exposures and crisis duration. Moreover, the government should optimally keep bailing-out banks that received previous investments, creating moral hazard for rescued banks that could increase their risk-taking, reckoning on government intervention.
Type: | Article |
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Title: | An AI approach for managing financial systemic risk via bank bailouts by taxpayers |
Location: | England |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1038/s41467-022-34102-1 |
Publisher version: | https://doi.org/10.1038/s41467-022-34102-1 |
Language: | English |
Additional information: | © 2023 Springer Nature Limited. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10162956 |
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