Forecasting indian rupee/us dollar: arima, exponential smoothing, naïve, nardl, combination techniques

Muhammad AsadUllah, Hira Mujahid, Mosab I. Tabash, Sharique Ayubi and Rabia Sabri
Authors registered in the RePEc Author Service: Dr.Hira Mujajhid

MPRA Paper from University Library of Munich, Germany

Abstract: The primary purpose of the study is to forecast the exchange rate of Indian Rupees against the US Dollar by combining the three univariate time series models i.e., ARMA/ARIMA, exponential smoothing model, Naïve and one non-linear multivariate model i.e., NARDL. For this purpose, the authors choose the monthly data of exchange rate and macro-economic fundamentals i.e., trade balance, federal reserves, money supply, GDP, inflation rate and interest rate over the period from January 2011 to December 2020. The data from January 2020 to December 2020 are held back for the purpose of in-sample forecasting. By applying all the models individually and combinedly, the NARDL model out performs other individual and combined models with the least MAPE value of 0.6653. It is the evidence that the Indian Rupee may forecast through non-linear analysis of macro-economic fundamentals rather than single univariate models. The findings will be beneficial for the policy makers, FOREX market, traders, tourists and other financial markets.

Keywords: Forecasting; Exchange Rate; Auto-Regressive; Naïve; Exponential Smoothing. (search for similar items in EconPapers)
JEL-codes: E37 E4 E47 F47 (search for similar items in EconPapers)
Date: 2020
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