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  • 學位論文

大中華貨幣單一化與短期資本流動之關聯研究 -以灰關聯和類神經模型分析

The Study of the Relationship between Chinese Currency Unit and the Short-term Capital Flow : Using Grey Relational Analysis and Neural Network

指導教授 : 陳若暉

摘要


歐洲國家歷經了關稅同盟時期、經濟同盟時期及貨幣同盟時期,終在1999年1月1日建立統一的歐洲貨幣,歐元。藉此探討大中華區(台灣、香港、中國大陸)貨幣單一化之主題,再加以探討其與資本流動之關聯性。本研究主要參考歐元整合方式及其中心匯率,並以台灣、香港及中國大陸之淨外匯存底、每人生產淨額及出口貿易值進行加權,建構出大中華單一貨幣化(CCU)的中心匯率。再以短期資本流動相關變數,包含經濟成長率、資本流動金額占GDP比率、金融效率、利率差、貨幣升值率、利率、消費者物價指數與M2占外匯存底之比例等八項,加上三種貨幣的歷史兌美元匯率,建構灰關聯及類神經匯率預測模型。 經灰關聯分析結果顯示,CCU分別受實質利率、利率差、M2占外匯存底比率及消費者物價指數等變數影響最深。結果顯示,前四變數之預測績效較後四變數為佳,且修正後SDR及EURO之匯率模式績效較佳。在遞迴式類神經及倒傳遞類神經之預測績效上,台灣之修正EURO方式前四變數(利率差、實質利率、M2占外匯存底比率和消費者物價指數)之績效最佳; 香港之修正EURO方式前四變數(利率差、實質利率、M2占外匯存底比率和經濟成長率)之績效最佳;中國大陸之修正SDR前四變數(消費者物價指數、經濟成長率、利率差和實質利率)之績效最佳。本研究未來可作為政府與及投資者對短期資本流動之參考。

並列摘要


The European countries had gone through the period of Customs Union , Economic Union and Monetary Union and eventually established a single European currency, Euro, in 1999, January 1st. This study analized Chinese Currency Unification with Greater China region, including Taiwan, Hong Kong, and Mainland China, associated with the capital flow. This study refers to form of Euro and its central rate by taking Taiwan, Hong Kong, and Mainland China’s net reserves, GNP per capita, and export to weight as samples. It can establish Chinese Currency Unification’s central rate. Considering short-term capital flow are the explanatory variables, including economic growth rate, the ratio of the capital flow to GDP, the ratio of domestic credit to GDP, the difference between the deposit rate and London Inter-Bank Offered Rate (LIBOR), currency appreciation ratio, real interest rate, consumer price index, the ratio of M2 to reserves. This study use Back-propagation Network and Recurrent Neural Network. According the grey relational analysis, the CCU was affected by real interest rate, interest-rate spread, the ratio of M2 to reserves and consumer price index. The results show that the first four performance prediction are batter than the east four explanatory variables, and modified-SDR and EURO currency mode perform evenbetter. By using Back-propagation Network and Neural Network to analyze performance prediction, the forecasting performance of Taiwan’s the first four performances prediction are real interest rate, the difference between the deposit rate and London Inter-Bank Offered Rate (LIBOR), the ratio of M2 to reserves and consumer price index. The forecasting performance of Hong Kong ’s the first four performances prediction are real interest rate, the difference between the deposit rate and London Inter-Bank Offered Rate (LIBOR), the ratio of M2 to reserves and economic growth rate. The forecasting performance of China’s the first four performances prediction are consumer price index, economic growth rate, real interest rate and the difference between the deposit rate and London Inter-Bank Offered Rate (LIBOR). This study can refer to short-term capital flow as a valuable informantion for the government and investors.

參考文獻


楊仁吉(2008),亞洲單一貨幣化與短期資本流動之關聯性研究,中原大學企業管理學系研究所碩士論文。
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江明宏(2009),台灣房地產景氣循環週期之研究-應用灰色關聯分析及類神經網路之預測,中原大學企業管理學系碩士論文。
徐玉瑛(2006),亞洲單一貨幣與海外投資政策關連性分析,中原大學企業管理系研究所碩士論文。
呂欣怡(2005),大中華貨幣單一化與貨幣和貿易政策關聯性之研析,中原大學企業管理學系研究所碩士論文。

被引用紀錄


高秀貞(2016)。亞洲匯率指數與指數型基金之預測分析-以ARFIMA-FIAPARCH模型為例〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840%2fcycu201600445

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