论文标题
RLOP：从数学角度来选择定价的RL方法
RLOP: RL Methods in Option Pricing from a Mathematical Perspective
论文作者
论文摘要
在这项工作中摘要，我们从数学角度构建了两个环境，即修改的QLB和RLOP模型，从而使RL方法可以通过Portfolio复制来实现选项定价。我们实施环境规范（可以在https://github.com/owen8877/rlop上找到源代码），学习算法和通过神经网络的代理参数化。将学习的最佳对冲策略与BS预测进行了比较。根据它们如何影响最佳价格和位置，考虑和研究了各种因素的效果。
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.
