Reinforcement learning policy recommendation for interbank network stability
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Title
Reinforcement learning policy recommendation for interbank network stabilityDate
2023-06-07Publisher
ElsevierISSN
1572-3089Bibliographic citation
Brini, A., Tedeschi, G., & Tantari, D. (2023). Reinforcement learning policy recommendation for interbank network stability. Journal of Financial Stability, 101139.Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/acceptedVersionSubject
Abstract
In this paper, we analyze the effect of a policy recommendation on the performance of an artificial interbank market. Financial institutions stipulate lending agreements following a public recommendation and their ... [+]
In this paper, we analyze the effect of a policy recommendation on the performance of an artificial interbank market. Financial institutions stipulate lending agreements following a public recommendation and their individual information. The former is modeled by a reinforcement learning optimal policy that maximizes the system’s fitness and gathers information on the economic environment. The policy recommendation directs economic actors to create credit relationships through the optimal choice between a low interest rate or a high liquidity supply. The latter, based on the agents’ balance sheet, allows determining the liquidity supply and interest rate that the banks optimally offer their clients within the market. Thanks to the combination between the public and the private signal, financial institutions create or cut their credit connections over time via a preferential attachment evolving procedure able to generate a dynamic network. Our results show that the emergence of a core–periphery interbank network, combined with a certain level of homogeneity in the size of lenders and borrowers, is essential to ensure the system’s resilience. Moreover, the optimal policy recommendation obtained through reinforcement learning is crucial in mitigating systemic risk. [-]
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Journal of Financial Stability Volume 67, August 2023, 101139Related data
Data will be made available on request.Funder Name
European Union | Ministerio de Ciencia, Innovación y Universidades | GNFM-Indam
Project code
PE00000014 | RTI2018-096927-B-100
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1572-3089/© 2023 Elsevier B.V. All rights reserved.
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