Abstract

This thesis proposes a new model for forecasting nominal oil prices inspired by the financial volatility literature. We propose a multiplicative components model where the conditional expectation of the oil price is decomposed into two components: a long term component that is derived by market fundamentals and a short term component which takes into account the information in futures prices and how they have departed from actual spot prices in the immediate past. In an extensive out-of-sample exercise we compare the performance of our model to an array of models: random walk, AR(1), ARMA(1,1), VAR(1), VECM(1) and Brent Futures. To assess its out-of-sample predictive ability, we use the unconditional predictive ability test of Diebold and Mariano (1995), the conditional predictive ability test of Giacomini and White (2006) and the model confidence set (MCS) of Hansen et al. (2011). The model outperforms all benchmarks in accuracy using the mean absolute error loss function and has the least bias according to the Mincer and Zarnowitz (1969) test. The results suggest that the multiplicative components model could potentially be a leading forecasting model for oil prices.

Department

Economics Department

Degree Name

MA in Economics

Graduation Date

Fall 1-16-2018

Submission Date

January 2018

First Advisor

Noureldin, Diaa

Committee Member 1

Bassiouny, Aliaa

Committee Member 2

Bouaddi, Mohammed

Extent

58 p.

Document Type

Master's Thesis

Rights

The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy. The author has granted the American University in Cairo or its agents a non-exclusive license to archive this thesis, dissertation, paper, or record of study, and to make it accessible, in whole or in part, in all forms of media, now or hereafter known.

Institutional Review Board (IRB) Approval

Not necessary for this item

Available for download on Thursday, February 02, 2023

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