Option Dynamic Hedging Using Reinforcement Learning

Cong Zheng; Jiafa He; Can Yang

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arXiv · 2306.10743

Option Dynamic Hedging Using Reinforcement Learning

Cong Zheng·Jiafa He·Can Yang

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Abstract

This work focuses on the dynamic hedging of financial derivatives, where a reinforcement learning algorithm is designed to minimize the variance of the delta hedging process. In contrast to previous research in this area, we apply uncertainty estimation technology to measure the uncertainty of the agent's decision, which can further reduce unnecessary wear and tear in the hedging process and control model overconfidence that may lead to significant losses. Numerical experiments show the superiority of our strategy in Monte Carlo simulations and SP 500 option data.

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BibTeX RIS

Cong Zheng, Jiafa He, Can Yang. 2023-06-19. Option Dynamic Hedging Using Reinforcement Learning. https://arxiv.org/abs/2306.10743

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