English(EN) Adversarial Training for Deep Hedging in Nonstationary Markets

新的对抗性训练框架增强了非平稳市场中的深度对冲能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了WRAP（Wasserstein-Reweighting Adversarial Perturbation），一个新颖的对抗性训练框架，旨在提高非平稳市场中的深度对冲策略。该方法通过采用双预算分布鲁棒优化公式，解决了历史市场数据可能无法准确反映未来条件这一挑战。WRAP允许对手重新加权轨迹并扰动其路径，从而形成更鲁棒的对冲策略，该策略在各种市场动态的实验中显示出显著的收益。 AI

影响增强了人工智能驱动的金融交易策略在应对不可预测的市场波动方面的鲁棒性。

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arXiv cs.LG TIER_1 English(EN) · Philipp J. Schneider, Lukas Looser, Antoine Garin, Shuhan Liu, Daniel Kuhn · 2026-10-07 04:00

Adversarial Training for Deep Hedging in Nonstationary Markets

arXiv:2610.07162v1 Announce Type: new Abstract: Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions. We propose WRAP (Wasserstein-Reweighting Adversarial P…

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Adversarial Training for Deep Hedging in Nonstationary Markets

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