RLOP: RL Methods in Option Pricing from a Mathematical Perspective
This work addresses option pricing for financial practitioners, but it appears incremental as it builds on existing methods without demonstrating broad improvements.
The authors tackled the problem of applying reinforcement learning to option pricing by developing two mathematical models, RLOP and modified QLBS, and compared the learned optimal hedging strategy against the Black-Scholes prediction, though no concrete numerical results were provided.
Abstract In this work, we build two environments, namely the modified QLBS and RLOP models, from a mathematics perspective which enables RL methods in option pricing through replicating by portfolio. We implement the environment specifications (the source code can be found at https://github.com/owen8877/RLOP), the learning algorithm, and agent parametrization by a neural network. The learned optimal hedging strategy is compared against the BS prediction. The effect of various factors is considered and studied based on how they affect the optimal price and position.