Reinforcement Learning for Credit Index Option Hedging

Francesco Mandelli, Marco Pinciroli, Michele Trapletti, Edoardo Vittori
7/19/2023
q-fin.TRcs.LGq-fin.CP

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Abstract

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.

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Cite this paper

@article{mandelli2023reinforcement,
  title  = {Reinforcement Learning for Credit Index Option Hedging},
  author = {Francesco Mandelli and Marco Pinciroli and Michele Trapletti and Edoardo Vittori},
  year   = {2023},
  eprint = {2307.09844},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.09844v1}
}

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