  This paper introduces a potential application of deep learning and artificial
intelligence in finance, particularly its application in hedging. The major
goal encompasses two objectives. First, we present a framework of a direct
policy search reinforcement agent replicating a simple vanilla European call
option and use the agent for the model-free delta hedging. Through the first
part of this paper, we demonstrate how the RNN-based direct policy search RL
agents can perform delta hedging better than the classic Black-Scholes model in
Q-world based on parametrically generated underlying scenarios, particularly
minimizing tail exposures at higher values of the risk aversion parameter. In
the second part of this paper, with the non-parametric paths generated by
time-series GANs from multi-variate temporal space, we illustrate its delta
hedging performance on various values of the risk aversion parameter via the
basic RNN-based RL agent introduced in the first part of the paper, showing
that we can potentially achieve higher average profits with a rather evident
risk-return trade-off. We believe that this RL-based hedging framework is a
more efficient way of performing hedging in practice, addressing some of the
inherent issues with the classic models, providing promising/intuitive hedging
results, and rendering a flexible framework that can be easily paired with
other AI-based models for many other purposes.
