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This paper introduces a potential application of deep learning and artificialintelligence in finance, particularly its application in hedging. The majorgoal encompasses two objectives. First, we present a framework of a directpolicy search reinforcement agent replicating a simple vanilla European calloption and use the agent for the model-free delta hedging. Through the firstpart of this paper, we demonstrate how the RNN-based direct policy search RLagents can perform delta hedging better than the classic Black-Scholes model inQ-world based on parametrically generated underlying scenarios, particularlyminimizing tail exposures at higher values of the risk aversion parameter. Inthe second part of this paper, with the non-parametric paths generated bytime-series GANs from multi-variate temporal space, we illustrate its deltahedging performance on various values of the risk aversion parameter via thebasic RNN-based RL agent introduced in the first part of the paper, showingthat we can potentially achieve higher average profits with a rather evidentrisk-return trade-off. We believe that this RL-based hedging framework is amore efficient way of performing hedging in practice, addressing some of theinherent issues with the classic models, providing promising/intuitive hedgingresults, and rendering a flexible framework that can be easily paired withother AI-based models for many other purposes.

Deep Hedging, Generative Adversarial Networks, and Beyond