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

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Listed:
  • AsadUllah, Muhammad
  • Mujahid, Hira
  • I. Tabash, Mosab
  • Ayubi, Sharique
  • Sabri, Rabia

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.

Suggested Citation

  • AsadUllah, Muhammad & Mujahid, Hira & I. Tabash, Mosab & Ayubi, Sharique & Sabri, Rabia, 2020. "Forecasting indian rupee/us dollar: arima, exponential smoothing, naïve, nardl, combination techniques," MPRA Paper 111150, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:111150
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    References listed on IDEAS

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    1. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    2. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    3. John N. Kallianiotis, 2013. "Exchange Rate Forecasting," Palgrave Macmillan Books, in: Exchange Rates and International Financial Economics, chapter 4, pages 143-179, Palgrave Macmillan.
    4. Fat Codruta Maria & Dezsi Eva, 2011. "Exchange-Rates Forecasting: Exponential Smoothing Techniques And Arima Models," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 499-508, July.
    5. Deutsch, Melinda & Granger, Clive W. J. & Terasvirta, Timo, 1994. "The combination of forecasts using changing weights," International Journal of Forecasting, Elsevier, vol. 10(1), pages 47-57, June.
    6. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    7. Lillie Lam & Laurence Fung & Ip-wing Yu, 2008. "Comparing Forecast Performance of Exchange Rate Models," Working Papers 0808, Hong Kong Monetary Authority.
    8. John N. Kallianiotis, 2013. "International Financial Transactions and Exchange Rates," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-35693-2.
    9. John N. Kallianiotis, 2013. "Exchange Rates and International Financial Economics," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-31888-6.
    10. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    11. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    12. Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Modelling and trading the EUR/USD exchange rate at the ECB fixing," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 541-560.
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    More about this item

    Keywords

    Forecasting; Exchange Rate; Auto-Regressive; Naïve; Exponential Smoothing.;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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