文章基本信息

标题：Forecasting Indian Rupee/Us Dollar: Arima, Exponential Smoothing, Naive, Nardl, Combination Techniques
本地全文：下载
作者：Muhammad AsadUllah ; Hira Mujahid ; Rabia Sabri 等
期刊名称：Academy of Accounting and Financial Studies Journal
印刷版ISSN：1096-3685
出版年度：2021
卷号：25
期号：3
页码：1-8
语种：English
出版社：The DreamCatchers Group, LLC
摘要：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, Nave 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 macroeconomic fundamentals rather than single univariate models. The findings will be beneficial for the policy makers, FOREX market, traders, tourists and other financial markets.
关键词：Forecasting;Exchange Rate;Auto-Regressive;Naïve;Exponential Smoothing