The Analysis of Global RMB Exchange Rate Forecasting and Risk Early Warning Using ARIMA and CNN Model

Author:

Liang Feng1,Liang Feng2,Zhang Hongxia3,Zhang Hongxia4,Fang Yuantao5,Fang Yuantao6

Affiliation:

1. Southwestern University of Finance and Economics, China

2. Institute of Chinese Financial Studies, Southwestern University of Finance and Economics, China

3. University of International Business and Economy, China

4. School of Business, University of International Business and Economy, China

5. Shanghai Lixin University of Accounting and Finance, China

6. Department of Finance, Shanghai Lixin University of Accounting and Finance, China

Abstract

The purposes are to predict exchange rate fluctuations more accurately and enhance Chinese enterprises’ ability to avoid exchange rate risks. Renminbi (RMB) exchange rate fluctuation’s prediction methods are studied based on data mining technology. The Auto-Regressive Integrated Moving Average (ARIMA) model is introduced first using a modeling method that combines linear and nonlinear models. The linear prediction is obtained by the ARIMA model’s application in the RMB exchange rate’s dynamic fluctuation analysis. The nonlinear residual prediction is obtained by integrating the ARIMA model with the convolutional neural network (CNN) algorithm. The RMB exchange rate fluctuations’ influence mechanism on China’s economic growth is explored by theoretical analysis and empirical research. The US dollar’s daily central parity rate (USD) data against the RMB from September 2015 to March 2019 are selected for model verification, obtaining the exchange rate’s logarithmic return sequence (RUSD).

Publisher

IGI Global

Subject

Strategy and Management,Computer Science Applications,Human-Computer Interaction

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