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arXiv · 2412.01224

Option Pricing with Convolutional Kolmogorov-Arnold Networks

Zeyuan Li·Qingdao Huang
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Abstract

With the rapid advancement of neural networks, methods for option pricing have evolved significantly. This study employs the Black-Scholes-Merton (B-S-M) model, incorporating an additional variable to improve the accuracy of predictions compared to the traditional Black-Scholes (B-S) model. Furthermore, Convolutional Kolmogorov-Arnold Networks (Conv-KANs) and Kolmogorov-Arnold Networks (KANs) are introduced to demonstrate that networks with enhanced non-linear capabilities yield superior fitting performance. For comparative analysis, Conv-LSTM and LSTM models, which are widely used in time series forecasting, are also applied. Additionally, a novel data selection strategy is proposed to simulate a real trading environment, thereby enhancing the robustness of the model.

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Zeyuan Li, Qingdao Huang. 2024-12-02. Option Pricing with Convolutional Kolmogorov-Arnold Networks. https://arxiv.org/abs/2412.01224

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