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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2212.05235 |
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| _version_ | 1866914004486586368 |
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| author | Xiao, Shuhua Ma, Jiali Xia, Li Zhu, Shushang |
| author_facet | Xiao, Shuhua Ma, Jiali Xia, Li Zhu, Shushang |
| contents | In the financial system, bailout strategies play a pivotal role in mitigating substantial losses resulting from systemic risk. However, the lack of a closed-form objective function to the optimal bailout problem poses significant challenges in its resolution. This paper conceptualizes the optimal bailout (capital injection) problem as a black-box optimization task, where the black box is modeled as a fixed-point system consistent with the E-N framework for measuring systemic risk in the financial system. To address this challenge, we propose a novel framework, "Prediction-Gradient-Optimization" (PGO). Within PGO, the Prediction employs a neural network to approximate and forecast the objective function implied by the black box, which can be completed offline; For the online usage, the Gradient step derives gradient information from this approximation, and the Optimization step uses a gradient projection algorithm to solve the problem effectively. Extensive numerical experiments highlight the effectiveness of the proposed approach in managing systemic risk. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_05235 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network Xiao, Shuhua Ma, Jiali Xia, Li Zhu, Shushang Risk Management In the financial system, bailout strategies play a pivotal role in mitigating substantial losses resulting from systemic risk. However, the lack of a closed-form objective function to the optimal bailout problem poses significant challenges in its resolution. This paper conceptualizes the optimal bailout (capital injection) problem as a black-box optimization task, where the black box is modeled as a fixed-point system consistent with the E-N framework for measuring systemic risk in the financial system. To address this challenge, we propose a novel framework, "Prediction-Gradient-Optimization" (PGO). Within PGO, the Prediction employs a neural network to approximate and forecast the objective function implied by the black box, which can be completed offline; For the online usage, the Gradient step derives gradient information from this approximation, and the Optimization step uses a gradient projection algorithm to solve the problem effectively. Extensive numerical experiments highlight the effectiveness of the proposed approach in managing systemic risk. |
| title | Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network |
| topic | Risk Management |
| url | https://arxiv.org/abs/2212.05235 |