发表机构
University of Oxford; Oxford-Man Institute of Quantitative Finance（牛津大学; 牛津-曼氏量化金融研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种端到端深度学习框架，通过可微风险敏感性惩罚实现期权组合的希腊字母中性，在纳斯达克100期权上提升样本外风险调整后表现并降低方向性暴露。
AI 中文摘要
我们提出了一种用于系统性期权交易端到端深度学习框架，该框架通过显式控制组合层面的风险暴露直接嵌入对冲行为。虽然经过训练以优化风险调整后表现的神经网络已被证明优于传统的基于规则的策略，但此类方法对所得组合相对于特定基础风险因子的敏感性仍不敏感。我们提出了一种通用训练目标，该目标将性能驱动损失与可微分的风险敏感性惩罚相结合，强制对选定风险维度保持中性。与通过模拟市场动态近似最优对冲策略的强化学习方法不同，我们的框架完全基于历史数据运行，并在单一学习问题中联合优化风险调整后收益和定向风险约束。我们在静态德尔塔中性跨式期权组合上实例化该框架，惩罚针对一阶方向性暴露，并评估两种惩罚变体——暴露归一化惩罚和希腊字母比率漂移惩罚。纳斯达克100股票期权的实证结果表明，适当校准的正则化同时改善了相对于未正则化基线的样本外风险调整后表现，同时降低了已实现的方向性暴露。
英文摘要
We present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of portfolio-level risk exposures. While neural networks trained to optimize risk-adjusted performance have been shown to outperform traditional rules-based strategies, such approaches remain agnostic to the sensitivities of the resulting portfolios with respect to specific underlying risk factors. We propose a general training objective that combines a performance-driven loss with a differentiable risk-sensitivity penalty, enforcing neutrality to selected risk dimensions. Unlike reinforcement learning methods that approximate optimal hedging policies via simulated market dynamics, our framework operates entirely on historical data and jointly optimizes risk-adjusted returns and targeted risk constraints in a single learning problem. We instantiate the framework on static delta-neutral straddle portfolios with the penalty directed at first-order directional exposure, and evaluate two penalty variants -- an exposure-normalized penalty and a Greek-ratio drift penalty. Empirical results on Nasdaq 100 equity options demonstrate that appropriately calibrated regularization simultaneously improves out-of-sample risk-adjusted performance relative to an unregularized baseline while reducing realized directional exposure.
