Early Warning of Chinese Yuan’s Exchange Rate Fluctuation and Value at Risk Measure Using Neural Network Joint Optimization Algorithm

The aims are to analyze the fluctuation forecast of exchange rate markets, including the Chinese Yuan (CNY), and discuss applying the neural network model in the Value at Risk (VaR) measure. Therefore, six exchange rate markets are selected as the research objects, with the CNY exchange rate market as the main body, to analyze and explain the exchange rate fluctuation risks. Second, based on the overview of the neural network model, the Deep Belief Network (DBN), Multilayer Perceptron (MLP), and Long Short-Term Memory Network (LSTM) are introduced, and a VaR method based on risk measurement is proposed. Finally, based on the number of excess days (Exc) and the Kupiec test, the VaR measure results under different models are analyzed. Results demonstrate that the CNY exchange rate market’s historical data are relatively concentrated, with minor fluctuations, and the overall change is a sharp right shift. Compared with the benchmark model Generalized AutoRegressive Conditional Heteroskedasticity, the three neural network models show excellent risk measurement performance for different exchange rate markets. Based on Exc, the DBN model has the optimal risk forecast performance. In the CNY exchange rate market, the Exc values corresponding to the DBN and LSTM models are small, and the forecast performance is fair. Based on the Kupiec test, in addition to the Great Britain Pound exchange rate market, the three neural network models perform well in measuring the risks of the other five exchange rate markets. Besides, the MLP model has the optimal performance in measuring the CNY risks. Hence, the neural network models have excellent applicability in measuring the risks of exchange rate markets.