Pricing Options with Exponential Levy Neural Network

TL;DR

This paper introduces the exponential Levy neural network (ELNN), a novel non-parametric model combining neural networks with exponential Levy processes to improve option pricing accuracy and stability.

Contribution

The paper presents the first non-parametric exponential Levy model using neural networks, enhancing option pricing methods with better performance and practical applicability.

Findings

ELNN outperforms Merton and Kou models in fitting S&P 500 options.

ELNN provides more stable and accurate option price estimates.

The model effectively integrates neural networks with traditional Levy processes.

Abstract

In this paper, we propose the exponential Levy neural network (ELNN) for option pricing, which is a new non-parametric exponential Levy model using artificial neural networks (ANN). The ELNN fully integrates the ANNs with the exponential Levy model, a conventional pricing model. So, the ELNN can improve ANN-based models to avoid several essential issues such as unacceptable outcomes and inconsistent pricing of over-the-counter products. Moreover, the ELNN is the first applicable non-parametric exponential Levy model by virtue of outstanding researches on optimization in the field of ANN. The existing non-parametric models are too vulnerable to be employed in practice. The empirical tests with S\&P 500 option prices show that the ELNN outperforms two parametric models, the Merton and Kou models, in terms of fitting performance and stability of estimates.