ARMA and/or SETAR Estimation and Out-of-Sample Forecast of the Mean-Reversion Between Brent Crude Oil and Gasoline Prices on the Ghanaian Market

Authors

DOI:

https://doi.org/10.23755/rm.v51i0.1581

Keywords:

Mean-reversion, Ex-pump, Ex-refinery

Abstract

The study investigates the existence of long-run equilibrium or mean-reversion using bivariate analysis of paired prices, as well as to test for linear and nonlinear threshold-type mean-reversion of bivariate relationships. The coefficient parameters of (non)linear VECM and threshold parameter value are estimated, and the forecast performance accuracies of the SETAR are compared to linear models of the mean-reversion process.
The two-regime SETAR model gives a much better prediction of cointegration relation than linear AR model. In the test for the two-regime SETAR model for the cointegration relation against a three-regime model, the two-regime model cannot be rejected at any reasonable significance level. The 2-SETAR exhibits significant constant and trending intervention features of the price build-up process. The asymmetric behavior remained the dominant feature of our mean reversion, which was also apparent. Although the MAPE is somewhat higher than the AR and ARMA processes, the threshold models outperformed the AR and ARMA processes. In summary, the mean reversion property is heavily reliant on the events that occurred in the preceding four bi-weekly pricing-periods in the swift unusual directions. In contrast, in the slow usual direction, it relies on the occurrence of the same in the only bi-weekly pricing-periods immediately preceding it.

Author Biographies

  • Emmanuel deGraft Johnson Owusu Ansah, Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Ghana

    My research interests are Computational Statistics, Uncertainty Quantification, Data Science (Machine Learning and Visualisation), and Bayesian Statistics with emphasis on applications in reliability, risk and hazard assessment, general modeling in engineering, applied sciences, Finance, and regional climate prediction. In particular, I am interested in developing tools to examine the complex nature of these fields to bring insights and solutions. Much of my work in this area involves modeling (reliability, risk, predictive, prescriptive) framework processes, with a Bayesian description of input parameters through quantification of parametric uncertainty and its impact on decision-making processes driven by data.

    Additionally, I am involved in scientific visualization in engineering and the applicability of Artificial Intelligence technologies toward on-farm agricultural problems and industry optimisation domains.

  • Jonathan Ayiku, Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Ghana
    Jonathan is a PhD candidate in Statistics specialised in Time Series modeling and Econometrics. He holds MPhil in Mathematical Statistics and serve as a Graduate Assistant in the Kwame Nkrumah University of Science and Technology
  • Simon Kojo Appiah, Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Ghana
    Simon Kojo Appiah holds a PhD degree in applied satistics from Edith Cowan University, Australia, MPhil in Applied Mathematics and BSc in Mathematics from Kwame Nkrumah University of Science and Technology, Ghana. He is currently an Associate Professor in the Department of Statistics and Actuarial Science at Kwame Nkrumah University of Science and Technology, Ghana with several years of teaching and research experience in mathematics and statistics. His research activities have been in various mathematical and statistical modelling with special interests in mathematical statistics, spatial statistics, time series/regression analysis, optimazation/operations research, and actuarial mathematics, which have led to interdisciplinary collaborative research in public health/disease control, environmental and road/pedestrian safety, and insurance/pension fund. He has supervised several graduate theses in applied mathematics and statistics, some of which led to collaborative publications. His collaborative research include spatial/spatio-temporal tranmission of infectious diseases, soil contamination, hotspot analysis of criminal activities,  pedestrian-vehicle crash severity, and pension fund. He has authored and co-authored several teaching materials, which are enhancing teaching and learning of undergraduate statistics course units in Ghana. He has about 35 refereed journal papers, 5 conference/workshop papers, 2 theses including http://ro.ecu.edu.au/theses/1398/ and 2 book publications.
  • Emmanuel Harris, Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Ghana
    Emmanuel Harris is a senior lecturer in the Statistics with interest in Time Series Analysis Financial Modelling Statistical Modelling Enviromental Modelling Machine Learning.

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Published

2024-06-17

Issue

Vol. 51 (2024)

Section

Volume 51-2024

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