Hedging of Financial Derivative Contracts via Monte Carlo Tree Search

Oleg Szehr

Papers from arXiv.org

Abstract: The construction of approximate replication strategies for pricing and hedging of derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for hedging under realistic market conditions have attracted significant interest. While research in the derivatives area mostly focused on variations of $Q$-learning, in artificial intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,... This article introduces Monte Carlo Tree Search as a method to solve the stochastic optimal control problem behind the pricing and hedging tasks. As compared to $Q$-learning it combines Reinforcement Learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of maximizing the utility of investor's terminal wealth without setting up an auxiliary mathematical framework.

Date: 2021-02, Revised 2021-04
New Economics Papers: this item is included in nep-cmp, nep-cwa and nep-fmk
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Citations: View citations in EconPapers (2)

Published in Journal of Computational Finance, Volume 27, Number 2, Pages: 47-80, 2023

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2102.06274

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