The Comparison between IABC with EGARCH in Foreign Exchange Rate Forecasting

  • Jui-Fang Chang
  • Pei-Wei Tsai
  • Jung-Fang Chen
  • Chun-Tsung Hsiao
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)


Foreign exchange rate forecasting catches many researchers interests in recent years. Problems of the foreign exchange rate forecasting model selection and the improvement on forecasting accuracy are not easy to be solved. In this paper, the forecasting results obtained by conventional time-series models and by the Inter-active Artificial Bee Colony (IABC), which is a young artificial intelligent meth-od, are compared with each other with 4 years historical data. The sliding win-dow strategy is used in the experiment for both the training and the testing phases. In our experiments, we use continuous previous three days data as the training set, and use the training result to forecast the foreign exchange rate on the fourth day. In addition, we evaluate the forecasting accuracy with three criteria, namely, Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The experimental results indicate that feeding macroeco-nomic factors to IABC as the input data is capable to produce higher accurate data in the foreign exchange rate than the conventional time-series models such as EGARCH.


IABC Foreign Exchange Rate Forecasting Time-series EGARCH 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jui-Fang Chang
    • 1
  • Pei-Wei Tsai
    • 2
  • Jung-Fang Chen
    • 3
  • Chun-Tsung Hsiao
    • 1
  1. 1.Department of International BusinessNational Kaohsiung University of Applied SciencesKaohsiung CityTaiwan, R.O.C.
  2. 2.Department of Marine Information and TechnologyNational Kaohsiung Marine UniversityKaohsiung CityTaiwan, R.O.C.
  3. 3.Department of Business AdministrationNational Kaohsiung University of Applied SciencesKaohsiung CityTaiwan, R.O.C.

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