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Deep Hedging to Manage Tail Risk

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  • Yuming Ma

Abstract

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.

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  • Yuming Ma, 2025. "Deep Hedging to Manage Tail Risk," Papers 2506.22611, arXiv.org.
    • Handle: RePEc:arx:papers:2506.22611
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    File URL: https://arxiv.org/pdf/2506.22611
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    References listed on IDEAS

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    1. David Wu & Sebastian Jaimungal, 2023. "Robust Risk-Aware Option Hedging," Applied Mathematical Finance, Taylor & Francis Journals, vol. 30(3), pages 153-174, May.
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