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Al-Bahir Journal for Engineering and Pure Sciences

Abstract

Economic performance of a nation depends majorly on the stability of foreign exchange rate; the economic viability hangs on the exchange rate of local currencies against other currencies across the globe. Box – Jenkins Approach was employed to model the Naira exchange rate to other major currencies using Autoregressive Integrated Moving Average (ARIMA) and The autoregressive fractional integral moving average (ARFIMA) models. This studies aimed on measuring forecast ability of Autoregressive Integrated Moving Average (ARIMA) (p,d,q) and autoregressive fractional integral moving average (ARFIMA) (p, fd, q) models for stationary type series that exhibit features of Long memory properties. Results indicate autoregressive fractional integral moving average (ARFIMA) is the best model in terms of fit, serial correlation analysis and accuracy measures. The out-sample forecasts confirmed the competence of the autoregressive fractional integral moving average (ARFIMA) models as shown by forecast validation tools. Consequently, the out-sample forecasts result nearly reveal the current economic situation in Nigeria indicating that the autoregressive fractional integral moving average (ARFIMA) model is appropriate and realistic in modeling and forecasting the strength of Naira to other currencies.

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References

[1] Khashei M, Bijari M. Exchange rate forecasting better with hybrid artificial neural networks models math. Comput Sci 2011;1(1):103-25.

[2] Jameela OY. Exchange rate changes and output performance in Nigeria. Pakistan J Soc Sci 2010;7(5):380-7.

[3] Yang HC, Syarifuddin F, Chang CP, Wang HJ. The impact of exchange rate futures fluctuations on macro-economy: evidence from ten trading market. Emerg Mark Finance Trade 2021:1-14.

[4] Adeosun OT, Gbadamosi II. Forecasting the Nigeria foreign exchange, leveraging on the ARIMA model. Afr J Math Stat Stud 2022;5(3):10925. https://doi.org/10.52589/AJMSSABLH1-XE.

[5] Babali Gaddafi Adamu, Henry Akpensuen Shiaondo, Ahmed Shitu Abdulrazaq, Malle Ahmad Atiku, Adamu Muhammed. Muhammad Goni Bukar best time series in-sample model for forecasting Nigeria exchange. Rate World Scientific News 2021;151:45-63.

[6] Ibrahim A, Sani UM, Olokojo VO. Forecasting consumer price index and exchange rate using ARIMA models: empirical evidence from Nigeria. Fudma J Sci (FJS) 2022;6(6): 114-24.

[7] Olajide AR, Afolabi AA, Titilayo AO. Effects of monetary policy on financial inclusion: empirical evidence from Nigeria. Economics and Applied Informatics, “Dunarea de Jos” University of Galati, Faculty of Economics and Business Administration 2022;3:74-80.

[8] Atoi NV, Nwambeke CG. Money and foreign exchange markets dynamics in Nigeria: a multivariate GARCH approach. CBN J Appl Statist 2021;12(1):109-38.

[9] Gabriel AC. Modeling naira-rupee exchange rate: an ARIMA framework. Asian J Econ Financ Manag 2022;8(1):24-33.

[10] Murshed M, Rashid S. An empirical investigation of real exchange rate responses to foreign currency inflows: revisiting the Dutch disease phenomenon in South Asia. Econ Finance Lett 2020;7(1):23-46.

[11] Shiaondo Henry Akpensuen, Lasisi KE, Akpan EA, Gwani AA. Application of out-of-sample forecasting in model selection on Nigeria exchange rates. World Scientific News 2019;127(3):225-47.

[12] Oyenuga IF, Oyekunle JO, Agbona AA. Modeling the exchange rate of the Nigeria naira to some other major currencies. Int J Stat Appl 2019;9(3):67-73.

[13] Adewole AI, Bodunwa OK, Akinyanju MM. Structural vector autoregressive modeling of some factors that affect the economic growth inNigeria. SciWorld J 2020;15:2. ISSN1597-6343.

[14] Bodunwa OK, Adewole AI, Ayodele S. Probability distribution moeing of Nigeria's economic growth rate. Nig J Pure Appl Sci 2022;35(I):4232-8. ISSN 2756-4045.

[15] Moosa IA. Economic growth and unemployment in Arab countries. Is Okun's law Valid? J Dev Econ Policies 2008; 10(2):5-24.

[16] Musa Y, Gulumbe SU. Analyzing Nigeria inflation and government revenues using ARDL approach. In: Nigerian Statistical Association, Annual Conference Proceedings; 2014. p. 195-209.

[17] Odukoya OU, Adio MA. Times series analysis of exchange rate Nigeria Naira to Us Dollar (1981-2016). Int J Innov Sci Res Technol 2022;7(3):1043-63.

[18] Adewole AI. Econometric modeling of Nigeria economic growth: a VECM and impulse response functions. Sci World J 2023;18(3):429-37.

[19] Adedoyin IL, Eziekel O, Bukola BL, Joseph I,O, Abiola JA, Henry I, et al. Impact of macroeconomic variables on the Nigerian manufacturing sector. Cogent Econ Finance 2022; 10(1):2090664.

[20] Box GEP, Jenkins M. Time Series analysis forecasting and control. Holden eDay Inc; 1976.

[21] Adewole AI, Amurawaye FF, Oladipupo JO. Times series analysis of malaria fever prevalence in Ogun State, Nigeria. TASUED J Pure Appl Sci 2023;2(1):201-11.

[22] Adewole AI. Statistical modelling and forecasting of temperature and rainfall in Ijebu Ode Nigeria using SARIMA. FNAS J Sci Innovat 2023;5(2):55-68.

[23] Liu K, Chen Y, Zhang X. An evaluation of ARFIMA (autoregressive fractional integral moving average) programs axioms 2017;6(16):1-16.

[24] Granger CWJ, Joyeux R. An introduction to the long memory time series models and fractional differencing. J Time Anal 1980;1:15-29.

[25] Hosking JRM. Fractional differencing. Biometrika 1981;68: 165-76.

[26] Sowell. Maximum likelihood estimation of stationary univariate fractionally integrated time series models. J Econom 1992;53:165-88.

[27] Geweke J, Potter-Hudak S. The estimation and application of long memory time series models. J Time Anal 1993;4:221-38.

[28] Dhliwayo L, Matarise F, Chimedza C. Autoregressive fractionally integrated moving average-generalized autoregressive conditional heteroskedasticity model with level shift intervention. Open J Stat 2020;10:341-62.

[29] Aliyu MA, Dikko HG, Danbaba UA. Statistical modeling for forecasting volatility in Naira per Dollar exchange rate using ARFIMA-GARCH and ARFIMA-FIGARCH models. World Scientific News December 2023;176:27-42.

[30] Ajao IO, Obafemi OS, Bolarinwa I. A modeling dollar-naira exchange rate in Nigeria. Nigeria statistical society, Edited Conference Proceedings. 2017;1(1):191-8.

[31] Thabani N. Modeling and forecasting Naira/USD exchange rate in Nigeria: a Box-Jenkins ARIMA approach. Munich Personal RePEc Archive MPRA; 2018. p. 1-36. Paper No: 88622.

[32] David Reuben O, Dikko Hussaini G, Shehu U. Gulumbe modelling volatility of the exchange rate of the naira to major currencies CBN. J Appl Stat 2016;7(2):159-87.

[33] Zakoian JM. Threshold heteroskedastic models. J Econ Dynam Control 1994;18(5):931-55. https://doi.org/10.1016/ 0165-1889(94)90039-6.

[34] Nelson DB. Conditional heteroskedasticity in asset returns: a new approach. Econometrica 1991;59(2):347-70. https://doi. org/10.2307/2938260.

[35] Egarch Ali G. GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH, and APARCH models for pathogens at marine recreational sites. J Stat Econom Methods 2013;2(3): 57-73.

[36] David RO, Dikko HG, Gulumbe SU. Modelling volatility of the exchange rate of the naira to major currencies. CBN J Appl Statist 2016;7(2):159-80.

[37] Jibrin SA, Yakubu M, Olanrewaju IS, Maimuna AA, Samaila M, Kabir L. The impact of floating exchange rate market to Nigeria naira: time-series intervention analysis. J Sci Eng Res 2017;4(4):152-6.

[38] Wiri Leneenadogo, Tuaneh Godwin Lebari. Autoregressive fractional integrated moving average (ARFIMA (p,d, q)) modeling of Nigeria exchange rate. Asian J Pure Appl Math 2022;4(1):28-35.

[39] Bashir Magaji, Garba Jamilu. Forecasting the exchange rate of Nigerian naira to united state’ dollar using ARIMAGARCH model. Dutse J Pure Appl Sci (DUJOPAS) 2022; 8(3b):87-96.

[40] Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. Testing the null hypothesis of stationarity against the alternative of a unit root. J Econom 1992;54:159-78.

[41] Ljung GM, Box GEP. On a measure of lack of fit in time series models. Biometrika 1978;65:297-303.

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