Reinforced Learning for Hedging: Transfer Learning at Work

By Svetlana Borovkova & Alexandru Giurca / June 2021

We apply reinforcement learning algorithms to option hedging and demonstrate that: 

  • Reinforcement learning “agents” outperform Black-Scholes – based hedging strategies in presence of trading costs and stochastic volatility;
  • Agents show robust performance for a variety of option strikes and maturities – even those they have never seen before; 
  • Agents can transfer knowledge acquired on synthetic data to the real-world hedging if their training environment is versatile and includes stochastic volatility and jumps in the underlying price process.