Option Hedging with Risk Averse Reinforcement Learning
2020 ACM International Conference on AI in Finance
8 Pages Posted: 4 Nov 2020
Date Written: May 29, 2020
In this paper we show how risk-averse reinforcement learning can be used to hedge options. We apply a state-of-the-art risk-averse algorithm: Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment, considering realistic factors such as discrete time and transaction costs.
Realism makes the problem twofold: the agent must both minimize volatility and contain transaction costs, these tasks usually being in competition.
We use the algorithm to train a sheaf of agents each characterized by a different risk aversion, so to be able to span an efficient frontier on the volatility-p&l space.
The results show that the derived hedging strategy not only outperforms the Black & Scholes delta hedge, but is also extremely robust and flexible, as it can efficiently hedge options with different characteristics and work on markets with different behaviors than what was used in training.
Keywords: Financial Options, Portfolio Optimization, Transaction Costs, Machine Learning, Reinforcement Learning, Deep Hedging
JEL Classification: C02, C15, C45, C61, G11
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