用深度学习优化投资组合尾部风险对冲，提升真实市场下的稳健性。
Deep Hedging to Manage Tail Risk
- 用神经网络参数化风险最小化，实现动态尾部风险对冲
- 在危机市场模拟中实现单日99% CVaR显著降低
- 考虑交易成本与市场影响，适合金融机构实操应用
在Buehler等（2019）深度对冲范式基础上，我们创新性地利用深度神经网络参数化凸风险最小化（CVaR/ES），解决投资组合尾部风险对冲问题。通过在危机时期可定制的市场模拟器上进行全面数值实验——支持交易成本、风险预算、流动性约束和市场冲击——我们的端到端框架不仅实现了单日99% CVaR的显著降低，还揭示了摩擦感知策略适应的实用洞见，证明了在现实市场中的鲁棒性与可操作性。
原文摘要 · Abstract (English)
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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