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.
@article{arxiv.2307.09844,
title = {Reinforcement Learning for Credit Index Option Hedging},
author = {Francesco Mandelli and Marco Pinciroli and Michele Trapletti and Edoardo Vittori},
journal= {arXiv preprint arXiv:2307.09844},
year = {2023}
}