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A Reinforcement Learning Algorithm For Option Hedging

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Abstract

We propose an algorithm, based on Reinforcement Learning, to hedge the payoff on a European call option. The algorithm is first tested in a model where the problem has a well known analytic solution, so that we can compare the strategy obtained by the algorithm to the theoretical optimal one. In a more realistic case, considering transaction costs, the algorithm outperforms the standard delta hedging strategy.

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  • Federico Giorgi & Stefano Herzel & Paolo Pigato, 2024. "A Reinforcement Learning Algorithm For Option Hedging," CEIS Research Paper 586, Tor Vergata University, CEIS, revised 17 Dec 2024.
    • Handle: RePEc:rtv:ceisrp:586
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    1. Flavio Angelini & Stefano Herzel, 2015. "Evaluating discrete dynamic strategies in affine models," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 313-326, February.
    2. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    3. Stefan C. Endres & Carl Sandrock & Walter W. Focke, 2018. "A simplicial homology algorithm for Lipschitz optimisation," Journal of Global Optimization, Springer, vol. 72(2), pages 181-217, October.
    4. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
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