大中華貨幣單一化與總體經濟指標之關聯性—以模糊類神經和ARIMAX-GARCH模型分析

Abstract

Refer to the structure of the EURO currency basket, the central rate of Chinese Currency（CCU） Unit was simulated from 1992/3 to the 2007/6 by the weights based on the GDP per capital, the exports, and the net foreign reserve of the Taiwan, Hong Kong, and China. By using each of the Special Drawing Rights（SDR） EURO, and modified-SDR method, the study utilized the analysis of grey relation, fuzzy neural network, and ARIMAX-GARCH model to find out the key factors from import growth rate, export growth rate, trade balance, industrial productive index, foreign reserve, interest rate,, inflation rate, monet supply growth rate, stock index and gross domestic product affecting CCU. According the grey relational analysis by dividing the better five and the worse five factors, the CCU was affected by the industrial productive index, GDP, stock price index, and net foreign reserve. And the fuzzy neutral was used to test forecasting performance for each currency. The research found that the better five variables performed well comparing with the worse five. And the modified-SDR is better than SDR and EURO, except the Hong Kong for EURO method. As analyzing the CCU by fuzzy neutral network, the forecasting performance of Taiwan’s better five variables (gross domestic product, stock index, and industrial productive index, foreign reserve and money supply growth rate) is superior to other groups. If the CCU is simulated by modified-SDR method utilizing the ARIMAX-GARCH model, the forecasting performance of China’s better five variables (industrial productive index, interest rate, export growth rate, stock index, and inflation rate) is the best. If CCU is built by EURO, and the forecasting performance of Hong Kong’s better five variables (industrial productive index, gross domestic product, foreign reserve, trade balance and stock index) has best performance. According to the ARIMAX-GARCH model, the industry productive index, money supply growth rate, and trade factors significantly affect the CCU and its dynamic effect. Generally, the forecasting performance of ARIMAX-GARCH model is better than the neutral network, and the macroeconomic factors effect will be different if the CCU is built by the different way. Finally, this study will provide some valueable suggestions to policy makes and researchers for Taiwan, Hong Kong, and China if the CCU is created in the future.