文献类型：期刊文献

英文题名：Analysis of Early Warning of RMB Exchange Rate Fluctuation and Value at Risk Measurement Based on Deep Learning

作者：Lu, Chunyi[1];Teng, Zhuoqi[2];Gao, Yu[3];Wu, Renhong[4];Hossain, Md Alamgir[5];Fang, Yuantao[1]

机构：[1]Shanghai Lixin Univ Accounting & Finance, Sch Finance, Shanghai 201209, Peoples R China;[2]Henan Finance Univ, Sch Business, Zhengzhou 450046, Peoples R China;[3]Qingdao Univ, Sch Accounting, Qingdao 266071, Peoples R China;[4]Guangdong Ocean Univ, Sch Econ, Zhanjiang, Peoples R China;[5]Hajee Mohammad Danesh Sci & Technol Univ, Sch Management, Dinajpur 5200, Bangladesh

年份：2022

卷号：59

期号：4

起止页码：1501

外文期刊名：COMPUTATIONAL ECONOMICS

收录：SSCI(收录号：WOS:000681524300001)、SCI-EXPANDED(收录号：WOS:000681524300001)、、Scopus(收录号：2-s2.0-85111945976)、WOS

基金：This research was funded by Chinese National Funding of Social Sciences, grant number 17BJY185.

语种：英文

外文关键词：Deep learning; RMB exchange rate fluctuation forecast; VaR risk measurement; Deep belief network (DBN); Long short-term memory (LSTM) model

外文摘要：To improve the RMB exchange rate prediction and risk measurement, the RMB exchange rate prediction model is constructed based on deep learning approaches. Value at risk (VaR) risk measurement related data are used, and this model is combined with the autoregressive moving average model-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model to fabricate an integrated VaR risk measurement model. The effectiveness of the proposed model is verified on specific example data. The results show that the proposed deep learning RMB exchange rate prediction model has better performance than traditional exchange rate prediction models in predicting exchange rates in different international foreign exchange markets, with accuracy of 74.92%. ARMA-GARCH risk prediction model has good measurement performance for the market, and its accuracy is significantly higher than that of the traditional measurement model. The deep confidence network model has stable performance and ideal forecasting effects both in the forecast of exchange rate fluctuations and in risk measurement. In short, this research can improve China's research on exchange rate fluctuations and effectively strengthens the ability of forecasting and risk assessment of the foreign exchange market.