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The Construction and Application of a New Exchange Rate Forecast Model Combining ARIMA with a Chaotic BP Algorithm

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  • Ma Yonghong
  • Wu Zhiyong
  • Ai Mingye
  • Wang Wei

Abstract

This article introduces a new model that combines an ARIMA with a chaotic BP (Backforward Propagation Neural Network) algorithm for exchange rate forecasting purposes, which is based on sample data collected from January 4, 2010, to October 20, 2011. The forecast of the exchange rate trend is then provided for the subsequent twenty-five days. Other models are also constructed, such as the ARIMA, BP, ARIMA, and BP algorithms, in order to evaluate the forecast accuracy. Based on our results, the combination of an ARIMA and a chaotic BP algorithm outperforms all other models in terms of the statistical accuracy of short-term forecasts.

Suggested Citation

  • Ma Yonghong & Wu Zhiyong & Ai Mingye & Wang Wei, 2016. "The Construction and Application of a New Exchange Rate Forecast Model Combining ARIMA with a Chaotic BP Algorithm," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(6), pages 1481-1495, June.
    • Handle: RePEc:mes:emfitr:v:52:y:2016:i:6:p:1481-1495
    DOI: 10.1080/1540496X.2015.1008894
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    Cited by:

    1. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
    2. Xue, Jian & Ding, Jing & Zhao, Laijun & Zhu, Di & Li, Lei, 2022. "An option pricing model based on a renewable energy price index," Energy, Elsevier, vol. 239(PB).

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