Hedging of Financial Derivative Contracts via Monte Carlo Tree Search

TL;DR

This paper introduces Monte Carlo Tree Search as a novel reinforcement learning approach for hedging derivative contracts, demonstrating its efficiency and effectiveness over traditional Q-learning methods in financial markets.

Contribution

It applies Monte Carlo Tree Search to the financial hedging problem, combining reinforcement learning with tree search to improve policy learning in incomplete markets.

Findings

MCTS outperforms Q-learning in sample efficiency.

MCTS learns stronger hedging policies faster.

MCTS effectively maximizes investor wealth without complex models.

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.