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Box-Jenkins modelling and forecasting of Brent crude oil price

Author

Listed:
  • Mensah, Emmanuel Kwasi
Abstract
The volatility in the crude oil price in the international market has risen much interest into the investigation of its price swing. In this project, we examine the dynamics of the monthly Brent oil price for the last two decades using the Box Jenkins ARIMA techniques and show that such model is not able to capture the volatility inherent in the crude oil price for an accurate forecast. We first divided the data into two. The first seventeen years used for the model construction and the last three years validating forecasting accuracy. The data is first differenced for stationarity and autocorrelation and residuals techniques used to select different ARIMA models for analysis. The performance of different models were compared and the result shows that a non-parsimonious ARIMA (1,1,1) model was the best forecasting model amidst the volatilities in the oil price.

Suggested Citation

  • Mensah, Emmanuel Kwasi, 2015. "Box-Jenkins modelling and forecasting of Brent crude oil price," MPRA Paper 67748, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:67748
    as

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    References listed on IDEAS

    as
    1. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    2. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
    3. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Brent crude oil; ARIMA; stationarity; forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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