  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.
