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BENEKI Christina , YARMOHAMMADI Masoud   

  1. 1.School of Business and Economics, Department of Business Administration, Technological Educational Institute of Ionian Islands, 31100 Lefkada, Greece;2.Department of Statistics, Payame Noor University, 19395-4697 Tehran, Islamic Republic of Iran.
  • Received:2013-04-25 Online:2014-02-25 Published:2014-08-20
  • Supported by:

    This research was supported by a grant from Payame Noor University, Tehran-Iran.

BENEKI Christina , YARMOHAMMADI Masoud. FORECASTING EXCHANGE RATES: AN OPTIMAL APPROACH[J]. Journal of Systems Science and Complexity, 2014, 27(1): 21-28.

This paper looks at forecasting daily exchange rates for the United Kingdom, European Union, and China. Here, the authors evaluate the forecasting  erformance of neural networks (NN), vector singular spectrum analysis (VSSA), and recurrent singular spectrum analysis (RSSA) for forecasting exchange rates in these countries. The authors find statistically significant evidence based on the RMSE, that both VSSA and RSSA models outperform NN at  orecasting the highly unpredictable exchange rates for China. However, the authors find no evidence to suggest any difference between the forecasting accuracy of the three models for UK and EU exchange rates.
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