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用于对冲期权组合的风险正则化深度学习

文章 arXiv papers · 作者: Wee Ling Tan et al.
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总结

本研究提出一种基于历史数据的系统化期权交易深度学习框架，将投资组合风险敏感度直接纳入训练目标。其目标函数结合以表现为导向的损失项，以及针对特定风险因子敞口的可微惩罚项。这样可促使学习到的投资组合满足选定的对冲约束，同时优化风险调整后收益，而无需依赖模拟市场动态来学习对冲策略。

该框架在纳斯达克100股票期权的静态 Delta 中性跨式组合上进行评估，惩罚项针对一阶方向性敞口。作者比较了按敞口归一化的惩罚项和希腊字母比率漂移惩罚项。他们报告称，与未正则化的基准相比，适当的正则化可提升样本外风险调整后表现，同时降低实际方向性敞口。证据仅限于所述投资组合和市场；本文未说明样本期、表现指标、惩罚项校准细节或交易成本处理方式。

核心观点

  • 训练目标将收益优化与针对选定投资组合风险敞口的可微惩罚项结合。
  • 该框架基于历史数据学习，而非依赖模拟市场动态。
  • 研究实现对象为纳斯达克100期权的静态 Delta 中性跨式组合。
  • 两种惩罚项设计均针对一阶方向性敞口。
  • 报告称，经过校准的惩罚项可改善样本外风险调整后表现，并降低实际方向性敞口。

标签

期权机器学习风险管理仓位规模回测
全文
# Taming the Greeks: Option Portfolios with Inductive Biases


# Taming the Greeks: Option Portfolios with Inductive Biases









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。