An Efficient Machine Learning Framework for Option Pricing via Fourier Transform

TL;DR

This paper introduces a hybrid framework combining the smooth offset algorithm with machine learning models to enable rapid and stable option pricing under exponential Lévy models, significantly accelerating computations.

Contribution

The paper presents a novel hybrid approach that integrates SOA with ML models for fast, stable option pricing, overcoming limitations of traditional Fourier methods.

Findings

Surrogate models achieve order-of-magnitude speed-up

Framework overcomes Fourier transform limitations

Models maintain accuracy in out-of-the-money options

Abstract

The increasing need for rapid recalibration of option pricing models in dynamic markets places stringent computational demands on data generation and valuation algorithms. In this work, we propose a hybrid algorithmic framework that integrates the smooth offset algorithm (SOA) with supervised machine learning models for the fast pricing of multiple path-independent options under exponential L\'evy dynamics. Building upon the SOA-generated dataset, we train neural networks, random forests, and gradient boosted decision trees to construct surrogate pricing operators. Extensive numerical experiments demonstrate that, once trained, these surrogates achieve order-of-magnitude acceleration over direct SOA evaluation. Importantly, the proposed framework overcomes key numerical limitations inherent to fast Fourier transform-based methods, including the consistency of input data and the instability in deep out-of-the-money option pricing.