An Efficient Machine Learning Framework for Option Pricing via Fourier Transform

Battle Count: 112

Highly relevant for quantitative trading desks that need to price large option portfolios rapidly. The framework addresses the critical need for fast recalibration in dynamic markets. The 99.82% execution time improvement (NN) and 96.19% (GBDT) over SOA make real-time portfolio pricing feasible. The method is particularly useful for derivatives desks pricing thousands of options simultaneously. However, it is limited to path-independent options and requires pre-computed training data, which may limit applicability for exotic derivatives. The numerical stability advantage over FFT for deep OTM options is practically significant for risk management.

The SOA algorithm requires careful implementation of Fourier transforms, numerical integration (Simpson's rule), and parameter optimization via grid search. The ML pipeline involves generating 2 million synthetic option contracts, feature engineering (normalization, encoding), and training three different model types. NN training requires GPU resources and careful hyperparameter tuning (learning rate schedule, 3000 epochs). The FFT-based framework requires handling grid spacing constraints and interpolation. Overall, the system is moderately complex but well-specified mathematically. The main challenge is the data generation bottleneck and ensuring numerical stability across the full parameter space.

The paper provides detailed algorithmic descriptions, parameter settings (Tables 1-4), network architecture (11 layers, 128 neurons, Leaky ReLU), training hyperparameters (batch size 256, 3000 epochs, learning rate schedule), and grid-search procedures for SOA/CMA parameters. However, no code repository or implementation details for the ML training pipeline are provided. The SOA algorithm is fully specified mathematically, but the random option contract generator and exact data generation procedure lack pseudocode. Benchmark comparisons use Monte Carlo methods for HM and EVGP, which introduces stochastic variability.