A GPU-accelerated Neural Networks Approximation Scheme for Heston Option Pricing Model

Solving differential equations is crucial in diverse fields, including finance, where accurate pricing of financial products is essential for risk management and market analysis. The Heston model, known for its superior ability to capture market volatility, has been hampered by the computational bottleneck of its differential equation solution. This paper presents a groundbreaking machine learning-based framework that leverages GPU acceleration to significantly enhance the speed and accuracy of Heston model calculations. By strate-gically partitioning the process into a neural network-based approximation and an evolutionary algorithm-based calibration, this framework achieves remarkable speedups compared to tra-ditional numerical methods. Furthermore, its GPU-accelerated vectorized operations pave the way for realtime application in high-frequency trading environments. This innovative framework holds substantial promise for revolutionizing the field of financial option pricing by offering both unparalleled speed and superior accuracy.