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

Rui Hua (), Wenzhe Hu () and Xiuju Zhao ()
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Rui Hua: Hubei University of Science and Technology
Wenzhe Hu: Wuhan University
Xiuju Zhao: Hubei University of Arts and Science

Risk Management, 2020, vol. 22, issue 3, No 4, 219-237

Abstract: 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.

Keywords: Neural network; Exchange rate forecast; Interest rate term structure; Nelson–Siegel model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41283-020-00062-3

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