Deep treasury management for banks
Retail banks use Asset Liability Management (ALM) to hedge interest rate risk associated with differences in maturity and predictability of their loan and deposit portfolios. The opposing goals of profiting from maturity transformation and hedging interest rate risk while adhering to numerous regula...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2023.1120297/full |
| _version_ | 1797866017065533440 |
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| author | Holger Englisch Thomas Krabichler Konrad J. Müller Marc Schwarz |
| author_facet | Holger Englisch Thomas Krabichler Konrad J. Müller Marc Schwarz |
| author_sort | Holger Englisch |
| collection | DOAJ |
| description | Retail banks use Asset Liability Management (ALM) to hedge interest rate risk associated with differences in maturity and predictability of their loan and deposit portfolios. The opposing goals of profiting from maturity transformation and hedging interest rate risk while adhering to numerous regulatory constraints make ALM a challenging problem. We formulate ALM as a high-dimensional stochastic control problem in which monthly investment and financing decisions drive the evolution of the bank's balance sheet. To find strategies that maximize long-term utility in the presence of constraints and stochastic interest rates, we train neural networks that parametrize the decision process. Our experiments provide practical insights and demonstrate that the approach of Deep ALM deduces dynamic strategies that outperform static benchmarks. |
| first_indexed | 2024-04-09T23:18:19Z |
| format | Article |
| id | doaj.art-3630673b7c1444c5b9f1d04fded12ad2 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-09T23:18:19Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-3630673b7c1444c5b9f1d04fded12ad22023-03-22T05:23:49ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122023-03-01610.3389/frai.2023.11202971120297Deep treasury management for banksHolger Englisch0Thomas Krabichler1Konrad J. Müller2Marc Schwarz3Department of Treasury, Thurgauer Kantonalbank, Weinfelden, SwitzerlandCentre for Banking and Finance, Eastern Switzerland University of Applied Sciences, St. Gallen, SwitzerlandMaster Programme UZH ETH in Quantitative Finance, Zürich, SwitzerlandEntris Banking, Berne, SwitzerlandRetail banks use Asset Liability Management (ALM) to hedge interest rate risk associated with differences in maturity and predictability of their loan and deposit portfolios. The opposing goals of profiting from maturity transformation and hedging interest rate risk while adhering to numerous regulatory constraints make ALM a challenging problem. We formulate ALM as a high-dimensional stochastic control problem in which monthly investment and financing decisions drive the evolution of the bank's balance sheet. To find strategies that maximize long-term utility in the presence of constraints and stochastic interest rates, we train neural networks that parametrize the decision process. Our experiments provide practical insights and demonstrate that the approach of Deep ALM deduces dynamic strategies that outperform static benchmarks.https://www.frontiersin.org/articles/10.3389/frai.2023.1120297/fullAsset Liability Management (ALM)deep hedgingdeep stochastic controldynamic strategiesmachine learning in financereinforcement learning |
| spellingShingle | Holger Englisch Thomas Krabichler Konrad J. Müller Marc Schwarz Deep treasury management for banks Frontiers in Artificial Intelligence Asset Liability Management (ALM) deep hedging deep stochastic control dynamic strategies machine learning in finance reinforcement learning |
| title | Deep treasury management for banks |
| title_full | Deep treasury management for banks |
| title_fullStr | Deep treasury management for banks |
| title_full_unstemmed | Deep treasury management for banks |
| title_short | Deep treasury management for banks |
| title_sort | deep treasury management for banks |
| topic | Asset Liability Management (ALM) deep hedging deep stochastic control dynamic strategies machine learning in finance reinforcement learning |
| url | https://www.frontiersin.org/articles/10.3389/frai.2023.1120297/full |
| work_keys_str_mv | AT holgerenglisch deeptreasurymanagementforbanks AT thomaskrabichler deeptreasurymanagementforbanks AT konradjmuller deeptreasurymanagementforbanks AT marcschwarz deeptreasurymanagementforbanks |