Derivative hedging and pricing are important and continuously studied topics
in financial markets. Recently, deep hedging has been proposed as a promising
approach that uses deep learning to approximate the optimal hedging strategy
and can handle incomplete markets. However, deep hedging usually requires
underlying asset simulations, and it is challenging to select the best model
for such simulations. This study proposes a new approach using artificial
market simulations for underlying asset simulations in deep hedging. Artificial
market simulations can replicate the stylized facts of financial markets, and
they seem to be a promising approach for deep hedging. We investigate the
effectiveness of the proposed approach by comparing its results with those of
the traditional approach, which uses mathematical finance models such as
Brownian motion and Heston models for underlying asset simulations. The results
show that the proposed approach can achieve almost the same level of
performance as the traditional approach without mathematical finance models.
Finally, we also reveal that the proposed approach has some limitations in
terms of performance under certain conditions.

深度对冲是一种利用深度学习来逼近最优对冲策略的方法，本研究提出了一种在深度对冲中使用人工市场模拟的新方法，可达到与传统方法几乎相同的效果，且无需使用数学金融模型。