# Reinforcement Learning for Credit Index Option Hedging

arXiv: https://arxiv.org/abs/2307.09844 · HTML: https://mlsanity.com/paper/2307.09844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Risk-Averse Trust Region Optimization for Reward-Volatility Reduction](https://mlsanity.com/paper/1912.03193/) (2019)
- [Deep Hedging of Derivatives Using Reinforcement Learning](https://mlsanity.com/paper/2103.16409/) (2021)
- [QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds](https://mlsanity.com/paper/1712.04609/) (2017)
- [Option Hedging with Risk Averse Reinforcement Learning](https://mlsanity.com/paper/2010.12245/) (2020)
- [The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios](https://mlsanity.com/paper/1801.06077/) (2018)

## Abstract (arXiv)

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.