Welcome to the InfoSci Platform
Reference Hub8
An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price: The Case of the Australian Option Market

An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price: The Case of the Australian Option Market

Hooman Abdollahi
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 19
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802853|DOI: 10.4018/IJAMC.2020040105
Cite Article Cite Article

MLA

Abdollahi, Hooman. "An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price: The Case of the Australian Option Market." IJAMC vol.11, no.2 2020: pp.99-117. http://doi.org/10.4018/IJAMC.2020040105

APA

Abdollahi, H. (2020). An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price: The Case of the Australian Option Market. International Journal of Applied Metaheuristic Computing (IJAMC), 11(2), 99-117. http://doi.org/10.4018/IJAMC.2020040105

Chicago

Abdollahi, Hooman. "An Adaptive Neuro-Based Fuzzy Inference System (ANFIS) for the Prediction of Option Price: The Case of the Australian Option Market," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.2: 99-117. http://doi.org/10.4018/IJAMC.2020040105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Option price prediction has been an important issue in the finance literature within recent years. Affected by numerous factors, option price forecasting remains a challenging problem. In this study, a novel hybrid model for forecasting option price consisting of parametric and non-parametric methods is presented. This method is composed of three stages. First, the conventional option pricing methods such as Binomial Tree, Monte Carlo, and Finite Difference are used to primarily calculate the option prices. Next, the author employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) in which the parameters are trained with particle swarm optimization to minimize the prediction errors associated with parametric methods. To select the best input data for the ANFIS structure, which has high mutual information associated with the future option price, the proposed method uses an entropy approach. Experimental examples with data from the Australian options market demonstrate the effectivity of the proposed hybrid model in enhancing the prediction accuracy compared to another method.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.