The effect of volatility of RMB exchange rate about China's export—an empirical study based on Sino–US time series during 2005–2012

2014 ◽  
Author(s):  
Yuan Yao ◽  
Pu Lu
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Empirical Study ◽  
Rmb Exchange Rate

scholarly journals Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chi Xie ◽  
Zhou Mao ◽  
Gang-Jin Wang
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Financial Time Series ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Rmb Exchange Rate ◽  
Combination Model ◽  
Arfima Model

There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate.

scholarly journals FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE USING VECTOR INTEGRATED MOVING AVERAGE

2020 ◽  
Vol 13 (2) ◽  
pp. 182-193
Author(s):  
Trimono Trimono ◽  
Abdulah Sonhaji ◽  
Utriweni Mukhaiyar
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Empirical Study ◽  
Moving Average ◽  
Agriculture Sector ◽  
Central Java ◽  
Several Variables ◽  
The Future ◽  
Vector Time Series ◽  
Central Java Province

Farmer Exchange Rate (FER) is an indicator that can be used to measure the level of farmers welfare. For every agriculture sector, FER is affected by the historical price of harvest from the corresponding sector and historical prices of other agriculture sectors. In Central Java Province, rice & palawija, horticulture, and fisheries are the largest agriculture sectors which is the main livelihood for most of the population. FER forecasting is a crucial thing to determine the level of farmers welfare in the future. One method that can be used to predict the value of a variable that is influenced by the historical value of several variables is Vector Time Series. An empirical study was conducted using FER data from the rice & palawija, horticulture and fisheries sectors for January 2011-June 2017 in Central Java Province. The results obtained show that by using the VIMA(2.1) model, the FER prediction was very accurate, with MAPE values were 1.91% (rice & palawija sector), 2.44% (horticulture sector), and 2.18% (fisheries sector).

Sign in / Sign up

Export Citation Format

Share Document