Neural Network-Enhanced Monte Carlo Simulation for Efficient European Option Pricing

This study explores a hybrid method to enhance the efficiency of Monte Carlo simulations for European option pricing by incorporating neural network models. Traditional Monte Carlo Methods require high computational costs when dealing with high-dimensional financial derivatives due to the increased demand for simulation paths to achieve prediction accuracy. By integrating a feedforward neural network, this research aims to predict future price paths based on intermediate simulations, effectively reducing the number of paths while maintaining pricing accuracy. The proposed model employs residual block, regularization, early stopping, and other techniques to capture the nonlinear relationships between price paths and payoffs. Based on the trained model, the number of simulation paths required is successfully reduced from 10,000 to around 4,000 5,000 without significantly reducing prediction accuracy while achieving a balance in computation costs. The method exhibits stable predictive performance and computational efficiency, providing a potential solution for reducing the overhead of pricing complex financial derivatives. Future work could explore more advanced neural network architectures and hybrid with other techniques to further improve performance and efficiency.