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Research on RMB exchange rate forecast based on the neural network model and the Nelson–Siegel model

Author

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  • Rui Hua

    (Hubei University of Science and Technology)

  • Wenzhe Hu

    (Wuhan University)

  • Xiuju Zhao

    (Hubei University of Arts and Science)

Abstract

This paper expands the neural network model to predict exchange rate based on the factors extracted from the Nelson–Siegel model. Based on the theory about exchange rate forecasting, interest could be used to predict the movement of exchange rate. Therefore, this paper analyzes the interest rate term structure factors based on the US and China yield curves data, then uses the Nelson–Siegel model to extract the factors of the interest rate term structure. Finally, the factors of yield curves are used as input data to of the neural network model. And the mean forecasting squared errors, mean absolute errors, mean absolute percentage errors of neural network model, Nelson–Siegel regression model, and ARIMA model are compared. The results show that the neural network model has a superior ability to explain the exchange rate fluctuations of the CNY and USD, and the prediction ability is better than the exchange rate prediction ability of the Nelson–Siegel regression model and ARIMA model.

Suggested Citation

  • Rui Hua & Wenzhe Hu & Xiuju Zhao, 2020. "Research on RMB exchange rate forecast based on the neural network model and the Nelson–Siegel model," Risk Management, Palgrave Macmillan, vol. 22(3), pages 219-237, September.
    • Handle: RePEc:pal:risman:v:22:y:2020:i:3:d:10.1057_s41283-020-00062-3
    DOI: 10.1057/s41283-020-00062-3
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    References listed on IDEAS

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    Cited by:

    1. de Souza Vasconcelos, Camila & Hadad Júnior, Eli, 2023. "Forecasting exchange rate: A bibliometric and content analysis," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 607-628.

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