@article {10.3844/jmssp.2019.185.195,
article_type = {journal},
title = {A Hybrid Artificial Neural Network Model for Option Pricing},
author = {Simiyu, Hanningtone Meshack and Waititu, Anthony Gichuhi and Akinyi, Jane Aduda},
volume = {15},
year = {2019},
month = {Aug},
pages = {185-195},
doi = {10.3844/jmssp.2019.185.195},
url = {https://thescipub.com/abstract/jmssp.2019.185.195},
abstract = {In the absence of a well-defined input selection technique associated with the pure ANN models, Option pricing using pure ANN models while relaxing the assumption of constant volatility remains a challenge. The conservative drill espoused has been to make allowances for a large number of input lags with the confidence that the ability of ANN to integrate suppleness and redundancy generates a more robust model. This is to say that the nonexistence of input selection criteria notwithstanding, the models have been developed without due consideration to the effect that the choice of input selection technique would have on model complexity, learning difficulty and performance measures. In this study, we deviate from the conventional techniques applied by the pure ANN option price models and adopt the hybrid model in which the volatility component is handled using some celebrated time series models, with specificity to the ANN-GJR-GARCH model - a hybrid of the ANN and a time series hybrid. The hybrid ANN option pricing model is then framed and tested with the forecasts of the ANN-GJR-GARCH model as a volatility input alongside two other inputs - time to maturity and moneyness. Finally, we compare the performance of the hybrid model developed with that of a pure ANN model. Results indicate that the hybrid model outperforms the pure ANN model not only in forecasting but also in the training time and model complexity.},
journal = {Journal of Mathematics and Statistics},
publisher = {Science Publications}
}