An Application of Artificial Neural Networks in Forecasting Future Oil Price Return Volatilities

Shafiee Hasanabadi, Hamed
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Faculty of Graduate Studies and Research, University of Regina

This study focuses on a novel application of Artificial Neural Networks (ANNs) in Financial Engineering. Here Artificial Neural Networks are applied for simulating both direct and inverse of some financial models. This study comprises of four parts. In first two parts, the ANNs are applied to forecast via forward/direct functions the future volatilities of crude oil future prices. In parts three and four, the ANNs are to simulate the inverse functions of option and compound options pricing models. Considering the recent importance of commodities in the world economy, it is very important to have a precise prediction of the price volatilities. In order to forecast crude oil futures prices return volatilities, two types of the ANNs have been applied in this study. The results of these ANNs are compared with the GARCH model, which is a commonly used model for volatility modeling and prediction. In this part of the study, the crude oil future prices data from the NYMEX are used for volatility modeling. The results prove that the ANNs (Both types of the used ANNs in this study) are performing better than the traditional GARCH model in crude oil future prices volatilities. In second study, the forecast of the crude oil future prices return volatility based on the information from the intra markets variables is attempted. According to the recent allegation that speculators participations affect the market trends and commodity prices, the speculation activity impact on the volatility prediction is analyzed. The historical value of some explanatory variables other than the historical volatilities has been used to forecast the future volatilities of crude oil. Results of this part of the study prove that the introduced variables can predict the future volatilities and both types of ANNs, which have been used in this study, are performing better than the traditional GARCH model.

A Thesis Submitted to the Faculty of Graduate Studies & Research in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Industrial Systems Engineering, University of Regina. xiv, 307 p.