Deep Hedging to Manage Tail Risk
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916814446919680 |
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| author | Ma, Yuming |
| author_facet | Ma, Yuming |
| contents | Extending Buehler et al.'s 2019 Deep Hedging paradigm, we innovatively employ deep neural networks to parameterize convex-risk minimization (CVaR/ES) for the portfolio tail-risk hedging problem. Through comprehensive numerical experiments on crisis-era bootstrap market simulators -- customizable with transaction costs, risk budgets, liquidity constraints, and market impact -- our end-to-end framework not only achieves significant one-day 99% CVaR reduction but also yields practical insights into friction-aware strategy adaptation, demonstrating robustness and operational viability in realistic markets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Hedging to Manage Tail Risk Ma, Yuming Portfolio Management Machine Learning Optimization and Control Computational Finance Risk Management 91G70 91G20 91G60 Extending Buehler et al.'s 2019 Deep Hedging paradigm, we innovatively employ deep neural networks to parameterize convex-risk minimization (CVaR/ES) for the portfolio tail-risk hedging problem. Through comprehensive numerical experiments on crisis-era bootstrap market simulators -- customizable with transaction costs, risk budgets, liquidity constraints, and market impact -- our end-to-end framework not only achieves significant one-day 99% CVaR reduction but also yields practical insights into friction-aware strategy adaptation, demonstrating robustness and operational viability in realistic markets. |
| title | Deep Hedging to Manage Tail Risk |
| topic | Portfolio Management Machine Learning Optimization and Control Computational Finance Risk Management 91G70 91G20 91G60 |
| url | https://arxiv.org/abs/2506.22611 |