A Risk Sensitive Contract-unified Reinforcement Learning Approach for Option Hedging

TL;DR

This paper introduces a risk-sensitive reinforcement learning method for option hedging that learns directly from market data, reducing tail risk and improving profit outcomes without relying on parametric models.

Contribution

It presents a novel contract-unified reinforcement learning approach that simultaneously learns tail risk measures and optimal hedging strategies from historical data.

Findings

Lower tail risk in out-of-sample tests

Higher mean final P&L compared to delta hedging

Model does not require parametric assumptions

Abstract

We propose a new risk sensitive reinforcement learning approach for the dynamic hedging of options. The approach focuses on the minimization of the tail risk of the final P&L of the seller of an option. Different from most existing reinforcement learning approaches that require a parametric model of the underlying asset, our approach can learn the optimal hedging strategy directly from the historical market data without specifying a parametric model; in addition, the learned optimal hedging strategy is contract-unified, i.e., it applies to different options contracts with different initial underlying prices, strike prices, and maturities. Our approach extends existing reinforcement learning methods by learning the tail risk measures of the final hedging P&L and the optimal hedging strategy at the same time. We carry out comprehensive empirical study to show that, in the out-of-sample tests, the proposed reinforcement learning hedging strategy can obtain statistically significantly lower tail risk and higher mean of the final P&L than delta hedging methods.