Modelling Naira - Rupee Exchange Rate: An ARIMA Framework


Published: 2022-08-23

Page: 416-425

Amaefula Chibuzo Gabriel *

Department of Mathematics and Statistics, Federal University Otuoke, Bayelsa State, Nigeria.

*Author to whom correspondence should be addressed.


This paper models Naira/1 Rupee exchange rate (NREXR) using ARIMA framework for forecasting NREXR now that bi-lateral relation between Nigeria and India have deepened over the years in areas of crude oil demand, medical facilities, and public and private business relations. The monthly bi-lateral NREXR data used spanned from 2008 to 2020. The auxiliary autoregressive order three (AAR (3)) order of integration test (OIT) adopted showed that NREXR is integrated order one (I(1)) in its original level and integrated order zero I(0) at 1st difference. Four possible ARIMA (p, 1, q) models were identified and compared using an output-based criterion known as sum of square deviation forecast criterion (SSDFC) conditioned on absence of serial correlation on the model residuals. The result showed that the ARIMA (1, 1, 2) model is the best performing model with the smallest SSDFC value. However, the ARIMA (1, 1, 2) can be used in predicting NREXR in terms of investment risk averse or incline.

Keywords: NREXR, ARIMA, order of integration test, SSDFC

How to Cite

Gabriel, A. C. (2022). Modelling Naira - Rupee Exchange Rate: An ARIMA Framework. Asian Journal of Economics, Finance and Management, 4(1), 416–425. Retrieved from


Download data is not yet available.


Medel C, Camilleri G, Hsu H, Kania S, Touloumtzoglou M. Robustness in foreign exchange rate forecasting models: Economics – based modeling after the financial crisis, Munich Personal RePEc Archive (MPRA), Paper No. 65290; 2015.


Ramzan S, Ramzan S, Zahid FM. Modeling and forecasting exchange rate dynamics in Pakistan using ARCH family models, Electronic Journal of Applied Statistical Analysis. 2012;5 (1): 15–19.


Khashei M, Bijari M. Exchange rate forecasting better with hybrid artificial neural networks models math, Computer Science. 2011;1(1):103–125.


Taiwo O, Adesola OA. Exchange rate volatility and bank performance in Nigeria, Asian Economic and Financial Review. 2013;3(2):178–185.


Adeoye BW, Saibu OM. Monetary policy shocks and exchange rate volatility in Nigeria, Asia Economic and Financial Review. 2014;4(4):544–562.


Dilmaghani AK, Tehranchian AM. The impact of monetary policies on the exchange rate: a GMM approach, Iranian Economic Review. 2015;2(19):177–191.


Oleka CD, Okolie PIP. The impact of floating exchange rate regime on economic growth in Nigeria (1986 – 2015), IOSR Journal of Economics and Finance (IOSR – JEF). 2016;7(5):35–42.


Central Bank of Nigeria. Foreign exchange rate, Research Department Education in Economics Series, No. 4, Abuja; 2016.

Arize C, Osang T. Exchange volatility and foreign trade: Evidence from thirteen LDCs, Journal of Business and Economic Statistics. 2000;18(2000):10–17.


Giovanni A. Exchange rates and traded goods prices, Journal of International Economics, 1988;24:45–68.


Islam MR, Sardar NM. Quantitative exchange rate economics in developing countries, Palgrave Macmillan, New York; 2007.


Brown CD. Theory of International Trade, John Wiley Publishers, Canada; 2008.

Okon EJ, Ikpang IN. Modeling and forecasting exchange rate values between naira and Us dollar to assess the effect of COVID-19 Pandemic Period on the Rate. Asian Journal of Probability and Statistics. 2020;8(1):55-65.

Shittu OI. Modeling exchange rate in Nigeria in the presence of financial and political instability: An intervention analysis approach. Middle Eastern Finance and Economics; 2009. ISSN: 1450-2889 Issue 5,


Appiah ST, Adetunde IA. Forecasting exchange rate between the Ghana cedi and the US dollar using Time Series Analysis. Current Research Journal of Economic Theory. 2011;3:76-83.

Huang W, Lai LK, Nakamori Y, Wang S. Forecasting foreign exchange rates with Artificial Neural Networks: a review, International Journal of 27 Information Technology & Decision Making. 2004;3(1):145–165.


Tindaon S. Forecasting the NTD/USD exchange rate using Autoregressive Model, Department of International Business, CYCU, Taiwan; 2015.


Moosa EA. Forecasting the Chinese Yuan – US Dollar exchange rate under the New Chinese Exchange Rate Regime, International Journal of Business and Economics. 2008;7(1):23–35.

Central Bank of Nigeria. Statistical Belletin; 2021.

Amaefula CG. A Simple integration order test: An alternative to unit root testing. EJ-MATH, European Journal of Mathematics and Statistics. 2021;2(3):77-85.


Box DE, Jenkins GM. Time series analysis, forecasting and control, revised edition, holden day; 1974.

Amaefula CG. Optimal identification of subclass of autoregressive integrated moving average model using sum of square deviation forecasts criterion. International Journal of Statistics and System. 2011;6(1):35-40.