RLOP: RL Methods in Option Pricing from a Mathematical Perspective. (arXiv:2205.05600v1 [q-fin.PR])

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 …

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