Option Hedging with Risk Averse Reinforcement Learning

TL;DR

This paper demonstrates how risk-averse reinforcement learning, specifically TRVO, can be effectively used to hedge options by balancing volatility minimization and transaction costs, outperforming traditional methods.

Contribution

It introduces a novel application of TRVO to option hedging, considering realistic market factors and producing robust, adaptable strategies that outperform classical approaches.

Findings

Outperforms Black & Scholes delta hedge in simulations

Produces robust hedging strategies across different market conditions

Efficiently balances volatility reduction and transaction costs

Abstract

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.