Journal of Mathematics and Statistics

Comparative Analysis of the Artificial Neural Networks Options Pricing Model Under Constant and Time-Variant Volatilities

Hanningtone Meshack Simiyu, Anthony Gichuhi Waititu and Jane Aduda Akinyi

DOI : 10.3844/jmssp.2019.158.175

Journal of Mathematics and Statistics

Volume 15, 2019

Pages 158-175

Abstract

Option pricing using artificial neural networks (ANN) model while relaxing the assumption of constant volatility still remains a challenge. The conventional practice for pure ANN models has been to either model volatility using the very ANN model and have the model output fed as an input to the ANN option pricing model, or to make allowances for a large number of lags directly as inputs to the option pricing model with the belief that the ability of ANN to incorporate flexibility and redundancy creates a more robust model. This has been done in spite of a well-known fact-that financial time series data harbors a set of characteristics such as volatility clustering, leptokurtosis and leverage effects-features that ANNs in their pure forms have proved inadequate in capturing. Consequently, this study sought to follow the conventional methods employed by other studies and developed two pure ANN option pricing models-one with constant volatility and the other while violating the assumption of constant volatility with an aim of establishing whether significant differences exist in the outputs of the two models. The intraday data for the AAPL stock option for the period between December 2016 and March 2017 with 56,238 data points was used in validating the developed models. Results indicate that the ANN model (with varying volatility) makes better predictions than the model with constant volatility. However, the difference between the performance of the two models is not significant at 0.05 level of significance.

Copyright

© 2019 Hanningtone Meshack Simiyu, Anthony Gichuhi Waititu and Jane Aduda Akinyi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.