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RLOP: RL Methods in Option Pricing from a Mathematical Perspective

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  • Ziheng Chen

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.

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  • Ziheng Chen, 2022. "RLOP: RL Methods in Option Pricing from a Mathematical Perspective," Papers 2205.05600, arXiv.org.
    • Handle: RePEc:arx:papers:2205.05600
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    File URL: http://arxiv.org/pdf/2205.05600
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    1. Saeed Marzban & Erick Delage & Jonathan Yumeng Li, 2021. "Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures," Papers 2109.04001, arXiv.org.
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