Reinforcement Learning for Credit Index Option Hedging
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 19 Jul 2023.
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8 of 21 references.
References · 8
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2018Deep Hedgingcited by 106
- 2019Risk-Averse Trust Region Optimization for Reward-Volatility Reductioncited by 53
- 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
- 2017QLBS: Q-Learner in the Black-Scholes(-Merton) Worldscited by 23
- 2020Option Hedging with Risk Averse Reinforcement Learningcited by 13
- 2018The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolioscited by 8
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