Reinforcement Learning for Credit Index Option Hedging
Francesco Mandelli Marco Pinciroli Michele Trapletti Edoardo Vittori
In this paper, we focus on finding the optimal hedging strategy of a credit
index option using reinforcement learning. We take a practical approach, where
the focus is on realism i.e. discrete time, transaction costs; even testing our
policy on real market data. We apply a state of the art algorithm, the Trust
Region Volatility Optimization (TRVO) algorithm and show that the derived
hedging strategy outperforms the practitioner's Black & Scholes delta hedge.