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Forecasting USDTRY rate by ARIMA method

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  • Cenk Ufuk Yıldıran
  • Abdurrahman Fettahoğlu

Abstract

This paper conducts a USDTRY rate forecast by ARIMA method using 3,069 daily observations between the dates of 3 January 2005 and 8 March 2017 and generates both long-term and short-term models. Existing works related to USDTRY rate forecast using ARIMA method generate static models, and none of them conduct multi-step prediction or out of sample fit. The work described in this paper, however, applies dynamic model generation and conducts multi-step ahead prediction for out of sample observations. In forecasts performed in this work for USDTRY rate, the short-term ARIMAs outperform the long-term ARIMAs in predicting accuracy. Specifically, for the short-term ARIMAs appropriate specification is raised as ARIMA (2,1,0); on the other hand, for the long-term ARIMAs, the best order is emerged as ARIMA (0,1,1).

Suggested Citation

  • Cenk Ufuk Yıldıran & Abdurrahman Fettahoğlu, 2017. "Forecasting USDTRY rate by ARIMA method," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1335968-133, January.
    • Handle: RePEc:taf:oaefxx:v:5:y:2017:i:1:p:1335968
    DOI: 10.1080/23322039.2017.1335968
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    References listed on IDEAS

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    1. Cem Kadilar & Muammer Simsek & Cagdas Hakan Aladag, 2009. "Forecasting The Exchange Rate Series With Ann: The Case Of Turkey," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 9(1), pages 17-29, May.
    2. 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.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
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