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An Artificial Intelligence Approach to Regulating Systemic Risk

We apply an artificial intelligence approach to simulate the impact of financial market regulations on systemic risk—a topic vigorously discussed since the financial crash of 2007–09. Experts often disagree on the efficacy of these regulations to avert another market collapse, such as the collateral...

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Autores principales: O'Halloran, Sharyn, Nowaczyk, Nikolai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861246/
https://www.ncbi.nlm.nih.gov/pubmed/33733096
http://dx.doi.org/10.3389/frai.2019.00007
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author O'Halloran, Sharyn
Nowaczyk, Nikolai
author_facet O'Halloran, Sharyn
Nowaczyk, Nikolai
author_sort O'Halloran, Sharyn
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description We apply an artificial intelligence approach to simulate the impact of financial market regulations on systemic risk—a topic vigorously discussed since the financial crash of 2007–09. Experts often disagree on the efficacy of these regulations to avert another market collapse, such as the collateralization of interbank (counterparty) derivatives trades to mitigate systemic risk. A limiting factor is the availability of proprietary bank trading data. Even if this hurdle could be overcome, however, analyses would still be hampered by segmented financial markets where banks trade under different regulatory systems. We therefore adapt a simulation technology, combining advances in graph theoretic models and machine learning to randomly generate entire financial systems derived from realistic distributions of bank trading data. We then compute counterparty credit risk under various scenarios to evaluate and predict the impact of financial regulations at all levels—from a single trade to individual banks to systemic risk. We find that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations. Moreover, the concentration of credit risk does not necessarily correlate monotonically with systemic risk. While the analysis focuses on counterparty credit risk, the method generalizes to other risks and metrics in a straightforward manner.
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spelling pubmed-78612462021-03-16 An Artificial Intelligence Approach to Regulating Systemic Risk O'Halloran, Sharyn Nowaczyk, Nikolai Front Artif Intell Artificial Intelligence We apply an artificial intelligence approach to simulate the impact of financial market regulations on systemic risk—a topic vigorously discussed since the financial crash of 2007–09. Experts often disagree on the efficacy of these regulations to avert another market collapse, such as the collateralization of interbank (counterparty) derivatives trades to mitigate systemic risk. A limiting factor is the availability of proprietary bank trading data. Even if this hurdle could be overcome, however, analyses would still be hampered by segmented financial markets where banks trade under different regulatory systems. We therefore adapt a simulation technology, combining advances in graph theoretic models and machine learning to randomly generate entire financial systems derived from realistic distributions of bank trading data. We then compute counterparty credit risk under various scenarios to evaluate and predict the impact of financial regulations at all levels—from a single trade to individual banks to systemic risk. We find that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations. Moreover, the concentration of credit risk does not necessarily correlate monotonically with systemic risk. While the analysis focuses on counterparty credit risk, the method generalizes to other risks and metrics in a straightforward manner. Frontiers Media S.A. 2019-05-29 /pmc/articles/PMC7861246/ /pubmed/33733096 http://dx.doi.org/10.3389/frai.2019.00007 Text en Copyright © 2019 O'Halloran and Nowaczyk. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
O'Halloran, Sharyn
Nowaczyk, Nikolai
An Artificial Intelligence Approach to Regulating Systemic Risk
title An Artificial Intelligence Approach to Regulating Systemic Risk
title_full An Artificial Intelligence Approach to Regulating Systemic Risk
title_fullStr An Artificial Intelligence Approach to Regulating Systemic Risk
title_full_unstemmed An Artificial Intelligence Approach to Regulating Systemic Risk
title_short An Artificial Intelligence Approach to Regulating Systemic Risk
title_sort artificial intelligence approach to regulating systemic risk
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861246/
https://www.ncbi.nlm.nih.gov/pubmed/33733096
http://dx.doi.org/10.3389/frai.2019.00007
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