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Prediction of the volatility of Return Crude Oils
Price Using Time Series Models
Maizura Binti Mohd Mahat
Supervisor: Dr. Hanafi Bin A.Rahim
Becholar of Science in Financial Mathematics
School of Informatics and Applied Mathematics
This article used of the volatility model for crude oil returns in relation to its ability to
identify and predict volatile facts on the data. The study uses monthly data for the closing
price used from November 1993 to November 2017. In this context, we assess the
existence of the volatility of crude oil returns using conditional volatility models. AR and
GARCH (1.1) models are better models for capturing volatility rather than the EGARCH
and IGARCH models. The AR and GARCH (1.1) models also provide superior performance
in sample volatility forecasts. We conclude that the AR and GARCH models (1.1) are
useful for modeling and forecasting forecasts in the volatility of crude oil return volatility.
819 | UMT UNDERGRADUATE RESEARCH DAY 2018