Comparative Analysis of ARIMA and GARCH Models for Forecasting Oil Prices and Exchange Rates in Nigeria
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Abstract
Forecasting future crude oil prices and exchange rates is essential because fluctuations in exchange rates and crude oil prices tend to have an impact on the economy. The study aimed at modelling and forecasting exchange rates and oil prices using some alternative models. The objectives of the study are to estimate suitable time series (ARIMA and GARCH) models for forecasting exchange rate in Nigeria; to estimate suitable time series (ARIMA and GARCH) models for forecasting crude oil prices in Nigeria and lastly, to compare the performance of the estimated models. The data used for the study were collected from the Central Bank of Nigeria Website and the United States Energy Administration. The data collected were analysed using the Augmented Dickey-Fuller (ADF) test, ARIMA, and GARCH. From the results of the analysis, the GARCH (1,2)-t emerged as the optimal model for modelling and forecasting monthly exchange rates in Nigeria while the ARIMA (2,1,0) emerged as the optimal model for modelling and forecasting monthly oil prices in Nigeria. Based on these findings it was recommended that policy makers may wish to adopt GARCH (1,2)-t and ARIMA (2,1,0) for forecasting exchange rate volatility and oil price respectively.
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Adetunji, A. A., Adejumo, A. O. & Omowaye, A. J. (2016). Data transformation and ARIMA models: A study of the exchange rate of Nigeria Naira to US Dollar. FUW Trends in Science & Technology Journal, 1(1), 299 – 306.
Al Shammre, A. S., & Chidmi, B. (2023). Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models. Energies, 16(11), 1 - 24. DOI: https://doi.org/10.3390/en16114451
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327. DOI: https://doi.org/10.1016/0304-4076(86)90063-1
Dritsaki, C. (2018). The performance of hybrid ARIMA-GARCH modelling and forecasting oil price. International Journal of Energy Economics and Policy, 8(3), 14-21.
Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007. DOI: https://doi.org/10.2307/1912773
Etuk, E. H., Uchendu, B. A & Dimka, M. Y (2016). Box – Jenkins method-based additive simulating model for daily Ugx – NGN exchange rates. Academic Journal of Applied Mathematical Sciences, 2(2): 11 – 18.
Fondo, K. S., Onago, A. A., Kiti, L. A., & Otulo, C. W. (2021). Modelling of Petroleum Prices in Kenya Using Autoregressive Integrated Moving Average and Vector Autoregressive Models. IOSR Journal of Mathematics, 17(6), 18–27.
Gabriel, A. C. (2022). Modelling Naira-Rupee Exchange Rate: An ARIMA Framework. Asian Journal of Economics, Finance and Management, 8(1), 24-33.
Gasper, L. & Mbwambo, H. (2023). Forecasting crude oil prices by using ARIMA model: evidence from Tanzania. Journal of Accounting, Finance and Auditing Studies, 9(2): 158-175. DOI: https://doi.org/10.32602/jafas.2023.017
Hina, H & Qayyum, A (2015). Exchange rate determination and out-of-sample forecasting: cointegration analysis. Munich Personal RePEc Archive (MPRA), Paper No. 61997.
Khashei, M & Bijari, M (2011). Exchange rate forecasting better with hybrid artificial neural network models. Math, Computer Science, 1(1): 103 – 125.
Ngandu, S. N (2008). Exchange rates and unemployment. South African Journal of Economics, 76(2): 205 – 221. DOI: https://doi.org/10.1111/j.1813-6982.2008.00188.x
Nwankwo, S. C (2014). Autoregressive Integrated Moving Average (ARIMA) model for the exchange rate (Naira to Dollar), MCSER Publishing. Academic Journal of Interdisciplinary Studies, 3(4): 429 – 433.
Nwankwo, S. C. (2014). Autoregressive integrated moving average (ARIMA) model for the exchange rate (Naira to Dollar). Academic Journal of Interdisciplinary Studies, 3(4), 429. DOI: https://doi.org/10.5901/ajis.2014.v3n4p429
Nyongesa, D. N., & Wagala, A. (2016). Non-linear time series modelling of the diesel prices in Kenya. International Journal of Academic Research in Economics and Management Sciences, 5(4). DOI: https://doi.org/10.6007/IJAREMS/v5-i4/2368
Nyoni, T. (2018). Modelling and forecasting Naira/USD exchange rate in Nigeria: A Box-Jenkins ARIMA approach. Munich Personal RePEc Archive. 1 – 35.
Odukoya, O. U. & Adio, M. A. (2022). Time Series Analysis of Exchange Rate Nigerian Naira to US Dollar (1981-2016). International Journal of Innovative Science and Research Technology, 7(3), 1043 – 1063.
Schmidt, L. (2021). Volatility forecasting performance of GARCH models: A study on Nordic indices during COVID-19. Master’s thesis.
Selvi, J. J., Kaviya Shree, R., & Krishnan, J. (2018). Forecasting Crude Oil Price Using ARIMA Models. International Journal of Advanced Research in Science and Engineering, 7(5), 334–343.
Suleiman, M., Muhammad, I., Adamu, A. Z., Zakari, Y., Iliyasu, R., Muhammad, A., ... & Abdu, M. (2023). Modelling Nigeria Crude Oil Prices using ARIMA Time Series Models. Journal of Science and Technology Research, 5(1): 230 – 241.
Tanko, A. N., Musa, G. K., & Auta, M. S. (2023). On the use of ARIMA and GARCH in modelling Nigeria’s naira–us dollar monthly exchange rates. Global Journal of Pure and Applied Sciences, 29(1), 73-82. DOI: https://doi.org/10.4314/gjpas.v29i1.9
Wiri, L., & Tuaneh, G. L. (2019). Modelling the Nigeria crude oil prices using ARIMA, pre-intervention and post-intervention models. Asian Journal of Probability and Statistics, 3(1), 1-12. DOI: https://doi.org/10.9734/ajpas/2019/v3i130083
Xiang, Y. (2022). Using the ARIMA-GARCH model to analyze the fluctuation law of International Oil Price. Mathematical Problems in Engineering. 1 – 7. DOI: https://doi.org/10.1155/2022/3936414
Yahaya, E., Etuk, H., & Emeka, A. (2021). Comparative performance of ARIMA and GARCH model in forecasting crude oil price data. Asian Journal of Probability and Statistics, 15(4): 251-275. DOI: https://doi.org/10.9734/ajpas/2021/v15i430378
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