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研究生:梁煜傑
研究生(外文):YU-CHIEN LIANG
論文名稱:利用機器學習預測臺幣匯率
指導教授:姚睿姚睿引用關係
學位類別:碩士
校院名稱:國立中央大學
系所名稱:經濟學系
學門:社會及行為科學學門
學類:經濟學類
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:60
中文關鍵詞:機器學習馬可夫轉換模型向量誤差修正模型預測匯率
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在經濟領域有學者研究匯率的經濟預測模型;在電腦科學領域學者利用了機器學習模型來預測匯率,但是跨領域學者常僅與電腦科學的模型比較,很少比較經濟預測模型與機器學習模型的預測績效,本文利用馬可夫轉換模型(Markov Switching Model)及向量誤差修正模型(Vector Error Correction Model)來與機器學習(Machine Learning)比較預測能力的優劣,結果發現在短期經濟預測模型與機器學習模型並無明顯的差異,而在長期機器學習模型有比較好的預測能力。
In the field of economics, scholars studied how to forecast exchange rates by economic models. In the field of computer science, scholars applied machine learning approach to forecast exchange rates. Although cross-disciplinary scholars often compare their empirical model with computer science models, they hardly compare the performance of economic forecasting models with the performance of machine learning approach. In this thesis, we applied Markov Switching Model, Vector Error Correction Model and Machine Learning approach to forecast the exchange rate of new Taiwan dollar. Besides, we compared the outcome of economic model with the outcome of machine learning models. The results show that, in the short run forecast horizon, there are insignificant difference between the economic models and the machine learning models. In the long run forecast horizon, there are significant differences between economic models and the machine learning models.
List of Figures VIII
List of Tables IX
1 緒論1
2 文獻回顧2
3 預測模型4
3.1 隨機漫步模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2 馬可夫轉換模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.3 向量誤差修正模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.4 機器學習模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4 資料處理與檢定13
4.1 敘述性統計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 單根檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3 共整合檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5 實證結果27
5.1 領先1 期的匯率預測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.2 領先4 期的匯率預測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.3 領先12 期的匯率預測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.4 延伸討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.4.1 不同的資料縮放方法. . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.4.2 套索迴歸及脊迴歸套用向量誤差修正模型之變數. . . . . . . . . . . 38
5.4.3 機器學習模型替換不同的被解釋變數. . . . . . . . . . . . . . . . . . 39
5.4.4 套索迴歸及脊迴歸不同正規化程度的變數係數. . . . . . . . . . . . . 40
6 結論47
References 48
Alvarez-Diaz, M., & Alvarez, A. (2003). Forecasting exchange rates using genetic algorithms.
Applied Economics Letters, 10(6), 319-322.
Benaroch, M. (1996). Artificial intelligence in economics Truth and dare. Journal of Economic
Dynamics and Control, 20(4), 601-605.
Bajari, P., Nekipelov, D., Ryan, S. P., & Yang, M. (2015). Machine learning methods for
demand estimation. American Economic Review, 105(5), 481-85.
Balassa, B. (1964). The purchasing-power parity doctrine: a reappraisal. Journal of Political
Economy, 72(6), 584-596.
Cheung, Y. W., Chinn, M. D., & Pascual, A. G. (2005). Empirical exchange rate models of
the nineties: Are any fit to survive?. Journal of International Money and Finance, 24(7),
1150-1175.
Chinn, M. D. (1997). Paper pushers or paper money? Empirical assessment of fiscal and
monetary models of exchange rate determination. Journal of Policy Modeling, 19(1), 51-78.
Chen, W., Xu, H., Jia, L., & Gao, Y. (2020). Machine learning model for Bitcoin exchange
rate prediction using economic and technology determinants. International Journal of Forecasting.
Diebold, F. X., & Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business
& Economic Statistics, 20(1), 134-144.
Dueker, M., & Neely, C. J. (2007). Can Markov switching models predict excess foreign
exchange returns?. Journal of Banking & Finance, 31(2), 279-296.
Dornbusch, R. (1976). Expectations and exchange rate dynamics. Journal of Political Economy,
84(6), 1161-1176.
Elliott, G., Rothenberg, T. J., & Stock, J. H. (1992). Efficient tests for an autoregressive
unit root (No. t0130). National Bureau of Economic Research.
Engel, C. (1994). Can the Markov switching model forecast exchange rates?. Journal of
International Economics, 36(1-2), 151-165.
Engel, C., & Hamilton, J. D. (1990). Long swings in the dollar: Are they in the data and do
markets know it?. The American Economic Review, 689-713.
Frankel, J. A. (1979). On the mark: A theory of floating exchange rates based on real interest
differentials. The American Economic Review, 69(4), 610-622.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time
series and the business cycle. Econometrica,357-384.
Nikolsko-Rzhevskyy, A., & Prodan, R. (2012). Markov switching and exchange rate predictability.
International Journal of Forecasting, 28(2), 353-365.
Sarno, L., Valente, G., & Wohar, M. E. (2004). Monetary fundamentals and exchange rate
dynamics under different nominal regimes. Economic Inquiry, 42(2), 179-193.
Wolff, C. C. (1987). Time-varying parameters and the out-of-sample forecasting performance
of structural exchange rate models. Journal of Business & Economic Statistics, 5(1), 87-97.
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