Deep hedging is a promising direction in quantitative finance, incorporating models and techniques from deep learning research. While giving excellent hedging strategies, models inherently requires careful treatment in designing architectures for neural networks. To mitigate such difficulties, we introduce SigFormer, a novel deep learning model that combines the power of path signatures and transformers to handle sequential data, particularly in cases with irregularities. Path signatures effectively capture complex data patterns, while transformers provide superior sequential attention. Our proposed model is empirically compared to existing methods on synthetic data, showcasing faster learning and enhanced robustness, especially in the presence of irregular underlying price data. Additionally, we validate our model performance through a real-world backtest on hedging the SP 500 index, demonstrating positive outcomes.

翻译：深度对冲是量化金融中的一个有前景的方向，它融合了深度学习研究中的模型与技术。尽管能提供出色的对冲策略，但模型在神经网络架构设计上天然需要谨慎处理。为缓解这些困难，我们引入了SigFormer，一种新颖的深度学习模型，它结合了路径签名和变换器的优势，用于处理序列数据，尤其是在存在不规则性的情况下。路径签名能有效捕捉复杂的数据模式，而变换器则提供了卓越的序列注意力机制。我们通过合成数据将所提模型与现有方法进行实证比较，展示了更快的学习速度和增强的鲁棒性，尤其在处理不规则的底层价格数据时。此外，我们通过对标普500指数进行实际回测来验证模型性能，展示了积极的结果。