臺灣博碩士論文加值系統

匯率影響許多經濟決策與外匯市場參與者的行為，若能掌握匯率波動的走勢，有利於企業及投資人決定經營投資策略。本文以1984年1月至2014年6月新加坡幣兌美元、韓幣兌美元、新台幣兌美元之匯率資料，並以新加坡、南韓及台灣每月出口量之資料做為外生變數，以自我迴歸整合移動平均模型、廣義線性自我迴歸模型、類神經網路模型、廣義線性自我迴歸模型及類神經網路模型之混合模型、組合預測模型及非線性組合預測模型等六種模型進行預測，最後以正規均方誤差、方向變化統計量及年報酬率等指標進行預測能力比較。實證發現，新加坡幣、韓幣及台幣在類神經網路模型的預測結果最差，其次是組合預測模型，顯示出新加坡、南韓及台灣的匯率走勢受到了各國每月的出口量影響。

Foreign exchange rate will influence lots of economic policy and the behaviors of market players. If we can have a sense of the trend of exchange rate fluctuations, it will be instrumental for us to formulate strategies in dealing with the deliberations resulting from the decision concerns over both corporate finance and individual investment.
In this study, we use the historical statistics of SGD/USD, KRW/USD, USD/NTD exchange rates which were dated from Jan 1984 through June 2014 as base data, coupled with the exogenous variable derived from monthly export volume of Singapore, South Korea and Taiwan respectively, then run the forecast by utilizing Autoregressive Integrated Moving Average Model, Generalized Linear Auto-Regression Model, Artificial Neural Network Model, The Hybrid Methodology Integrated GLAR with ANN, The Combined Forecasting Model, The Nonlinear Ensemble Forecasting Model separately. In the end, we further utilize normalized mean squared error, directional change statistics and annualized return as indicators to make a comparison analysis on the forecast capability.
Empirical results illustrate that SGD/USD, KRW/USD, USD/NTD exchange rate utilized Artificial Neural Network Model and The Combined Forecasting Model reveals a worse forecast performance. The conclusion demonstrates that the trend of foreign exchange rate of Singapore, South Korea and Taiwan were influenced by their own monthly export volume. Judging from three indicators, we can conclude that the forecast result derived from directional change statistics and annualized return demonstrate a similar outcome.

目錄
第1章 緒論 1
1.1 研究動機與目的 1
1.2 研究方法及步驟 2
第2章 文獻回顧 4
2.1 匯率預測因素 4
2.2 匯率預測方法 6
第3章 研究架構 9
3.1 研究方法 9
3.1.1 自我迴歸整合移動平均模型 9
3.1.2 廣義線性自我迴歸模型 14
3.1.3 類神經網路模型 15
3.1.4 GLAR與ANN混合模型 22
3.1.5 組合預測模型 22
3.1.6 非線性組合預測模型 23
3.2 統計模型預測表現的評估 24
3.3 研究結果 25
3.3.1 研究資料說明 25
3.3.2 時間序列模型建構 26
第四章 結論 33
參考文獻 35


圖目錄
圖1.2.1 研究步驟流程圖 3
圖2.1.1 匯率影響因素 4
圖3.1.1 生物神經元模型 16
圖3.1.2 McCulloch-Pitts神經元 17
圖3.1.3 網路處理與輸出 17
圖3.1.4 倒傳遞類神經網路模型架構 21
圖3.4.1 三種幣別每月兌美元匯率資料之自我相關圖 26
圖3.4.2 三種幣別每月兌美元匯率資料一階差分後之自我相關圖 27
圖3.4.3 新加坡幣兌美元匯率預測情形 30
圖3.4.4 韓幣兌美元匯率預測情形 30
圖3.4.5 新台幣兌美元匯率預測情形 30


表目錄
表3.1.1 ARMA模型下ACF/PACF判別落後期數準則 13
表3.4.1 ADF檢定定態資料結果 27
表3.4.2 新加坡幣兌美元匯率各ARIMA模型組合之AIC值 28
表3.4.3 韓幣兌美元匯率各ARIMA模型組合之AIC值 28
表3.4.4 新台幣兌美元匯率各ARIMA模型組合之AIC值 28
表3.4.5 新加坡幣兌美元匯率各GLAR模型組合之AIC值 29
表3.4.6 韓幣兌美元匯率各GLAR模型組合之AIC值 29
表3.4.7 新台幣兌美元匯率各GLAR模型組合之AIC值 29
表3.4.8 各匯率幣別在各種模型下正規均方誤差的比較 31
表3.4.9 各匯率幣別在各種模型下方向變化統計量的比較 31
表3.4.10 各匯率幣別在各種模型下年報酬率的比較 32

參考文獻
Akaike, Hirotugu. (1974). A new look at the statistical model identification. Automatic Control, IEEE Transactions on, 19(6), 716-723.
Bates, John M., and Granger, Clive W.J. (1969). The combination of forecasts. Or, 451-468.
Box, George E. (1976). P, and Jenkins, GM,“Time Series Analysis: Forecasting and Control,”. Time Series and Digital Processing.
Della Corte, Pasquale, and Tsiakas, Ilias. (2012). Statistical and economic methods for evaluating exchange rate predictability. Handbook of exchange rates, 221-263.
Denton, James W. (1995). How good are neural networks for causal forecasting? The Journal of Business Forecasting, 14(2), 17.
Dickey, David A, and Fuller, Wayne A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
El Shazly, Mona R., and El Shazly, Hassan E. (1999). Forecasting currency prices using a genetically evolved neural network architecture. International review of financial analysis, 8(1), 67-82.
Fang, Hsing, and Kwong, K. Kern (1991). Forecasting foreign exchange rates. The Journal of Business Forecasting, 10(4), 16.
Granger, Clive WJ, and Newbold, Paul. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111-120.
Hagan, Martin T, and Menhaj, Mohammad B. (1994). Training feedforward networks with the Marquardt algorithm. Neural Networks, IEEE Transactions on, 5(6), 989-993.
Levenberg, Kenneth. (1944). A method for the solution of certain non–linear problems in least squares.
Ljung, Greta M., and Box, George E.P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.
Marquardt, Donald W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431-441.
McCulloch, Warren S., and Pitts, Walter. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
Meese, Richard A., and Rogoff, Kenneth. (1983-a). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of international economics, 14(1), 3-24.
Meese, Richard, and Rogoff, Kenneth. (1983-b). The out-of-sample failure of empirical exchange rate models: sampling error or misspecification? Exchange rates and international macroeconomics (pp. 67-112): University of Chicago Press.
Molodtsova, Tanya, and Papell, David H. (2009). Out-of-sample exchange rate predictability with Taylor rule fundamentals. Journal of international economics, 77(2), 167-180.
Nelson, Charles R., and Plosser, Charles R. (1982). Trends and random walks in macroeconmic time series: Some evidence and implications. Journal of Monetary Economics, 10(2), 139-162.
Parker, DB. (1985). Learning-logic (TR-47). Center for Computational Research in Economics and Management Science. MIT-Press, Cambridge, Mass, 8.
Reid, David J. (1968). Combining three estimates of gross domestic product. Economica, 431-444.
Rumelhart, DE. (1986). David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Nature, 323, 533-536.
Schwarz, Gideon. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461-464.
Shephard, Neil. (1995). Generalized linear autoregressions.
Sims, Christopher A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
Tseng, Fang-Mei, Yu, Hsiao-Cheng, and Tzeng, Gwo-Hsiung. (2002). Combining neural network model with seasonal time series ARIMA model. Technological Forecasting and Social Change, 69(1), 71-87.
Werbos, Paul. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences.
Widrow, Bernard, and Hoff, Marcian E. (1960). Adaptive switching circuits.
Yao, Jingtao, and Tan, Chew Lim. (2000). A case study on using neural networks to perform technical forecasting of forex. Neurocomputing, 34(1), 79-98.
Yu, Lean, Wang, Shouyang, and Lai, K. K. (2005). A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Computers and Operations Research, 32(10), 2523-2541.
Zhang, G. Peter. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.