IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, Oct. 22, 2024
IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr 2024)
Derivative hedging and pricing are important and continuously studied topics in financial markets. As a recent promising approach, deep hedging has been proposed, which uses deep learning to approximate the optimal hedging strategy and can handle incomplete markets. However, deep hedging usually needs underlying asset simulations, and it is not easy to select the best model for the simulations. This paper proposes a new approach to using artificial market simulations for underlying asset simulations in deep hedging. Artificial market simulations can replicate 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 performance as the traditional approach without mathematical finance models. However, we also revealed that the proposed approach has some limitations in terms of performance under some conditions.
deep hedging; artificial market simulation; financial markets; derivative; hedging;
@inproceedings{Hirano2024-cifer,
title={{Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Assets Simulators}},
author={Masanori Hirano},
booktitle={IEEE Symposium on Computational Intelligence for Financial Engineering and Economics},
publisher={IEEE},
year={2024}
}