  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.
