On CSI 300 index options, gradient boosting ensembles (LGBM, XGBoost, NGBoost) achieve the lowest RMSE in most experiments, but the training set includes data from after the test period, invalidating the temporal realism claim.
Option Pricing with Convolutional Kolmogorov-Arnold Networks
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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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Option Pricing Using Ensemble Learning
On CSI 300 index options, gradient boosting ensembles (LGBM, XGBoost, NGBoost) achieve the lowest RMSE in most experiments, but the training set includes data from after the test period, invalidating the temporal realism claim.