政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/67098

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/67098

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/67098

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/67098

Title:	小波分析方法對時間序列模型預測能力之影響 -以新台幣對美元匯率為例 The Influence of Time Series model Forecasting Accuracy On Wavelet Analysis -Evidence from NTD/USD exchange rate
Authors:	吳修宏 Wu, Hsiu Hung
Contributors:	廖四郎 Liao, Szu Lang 吳修宏 Wu, Hsiu Hung
Keywords:	小波分析預測匯率時間序列 wavelet transform forecasting exchange rate time series
Date:	2012
Issue Date:	2014-07-01 12:06:42 (UTC+8)
Abstract:	過去對匯率資料進行預測研究，多數利用時間序列模型、迴歸模型等方法，為了提升預測能力，學者們著重於迴歸因子的選取、模型參數的配適、假設條件的放寬或者模型的改進等等，已經成為一個龐大的結構。近年來新的預測方法興起，開始有類神經模擬、基因演算法等人工智慧方法可對匯率、股價等金融商品時間序列資料進行預測。當有如此多的預測模型可供選擇，為了提升模型的預測效果，開始有研究在使用預測模型對資料進行預測之前先對資料進行分析及處理。本研究所使用之小波轉換方法為近年來常被搭配使用之訊號分析方法，藉由小波分解將資料分解成較為低頻的近似子序列及高頻的細部子序列，如同時間序列資料的長期趨勢項及短期波動項，而後再將兩項不同特性的序列分別用時間序列模型進行預測，時間序列模型選擇較常被使用的ARMA模型及ARMA-GARCH模型，經由配適後的模型得到預測值後再將兩者進行合成，還原成匯率的預測值，並且跟實際值進行比較。研究結果發現加入小波轉換對資料先進行處理後再使用時間序列模型預測，透過MAE及RMSE預測力指標的判斷下能夠有效提升模型對於日資料、周資料及月資料的預測能力，也就是不論資料周期長短期小波轉換都有其功用存在。 This paper illustrates an application of wavelets transform method with “singal analysis methods”. The entire procedure can be roughly divided into three steps: wavelet decomposition, signal extension and wavelet reconstruction. In the step of wavelet decomposition, we divide the data into low-signal and high-signal time-series sub data just like the long term trend and the short term volatility in time-series. Second, we apply the ARMA and ARMA-GARCH model to forecast the exchange rate separately and finally reconstruct the two predicting value from the best fitting model to form the forecasting exchange rate which could be compared to the real value. It could be concluded in this study that if we apply the MAE and RMSE index to evaluate the predicting result which is generated from the time-series model with the wavelets transformation of the data beforehand, the forecasting accuracy could be enhanced no matter the data are in daily, weekly or monthly type. In other words, no matter what type of time series data is, the wavelets transform method does enhance the forecasting accuracy.
Reference:	中文呂佳芹(2009)，應用時間序列、演化式類神經網路與灰預測方法在匯率預測績效之比較，朝陽科技大學財務金融所碩士論文。呂宜珊(2010)，金融商品資料的小波分析預測，臺北大學統計研究所碩士論文。周靖秦、陳秀淋(2011)，利用小波轉換分析美國總體指標與道瓊工業指數之關係，經濟論文 39.3 : 61-89。林家卉(2007)，匯率預測研究-ARIMA模式之應用，高雄第一科技大學金融營運所碩士論文。林萍珍，投資分析-含Matlab應用、類神經網路與遺傳演算法模型。張小彤(2003)，匯率預測研究－時間數列分析法之應用，大葉大學事業經營研究所碩士論文。陳心一(1997)，短期匯率預測：ARIMA 與 GARCH 模型之比較研究，中山大學財務管理研究所碩士論文。楊奕農，時間序列分析：經濟與財務上之應用二版。劉薇、常振海(2003)，基于小波的 GARCH 模型及其在汇率中的应用，Journal of Yanbian University ( Natural Science)Vol. 35 No. 3Sep. 2009 簡苺蓉(2007)，小波轉換結合類神經網路匯率預測能力之研究，高雄應用科技大學商務經營研究所碩士論文。英文 Aguiar-Conraria, Luís, Nuno Azevedo, and Maria Joana Soares(2008). "Using wavelets to decompose the time–frequency effects of monetary policy." Physica A: Statistical mechanics and its Applications 387.12 : 2863-2878. Aussem, Alex, and Fionn Murtagh(1997). "Combining neural network forecasts on wavelet-transformed time series." Connection Science 9.1 : 113-122. Bollerslev, Tim(1986). "Generalized autoregressive conditional heteroskedasticity."Journal of econometrics 31.3 : 307-327. Bollerslev, Tim, Ray Y. Chou, and Kenneth F. Kroner(1992). "ARCH modeling in finance: a review of the theory and empirical evidence." Journal of econometrics52.1: 5-59. Connor, Jeff, and Rosemary Rossiter(2005). "Wavelet transforms and commodity prices." Studies in Nonlinear Dynamics & Econometrics 9.1. Daubechies, Ingrid(1992). Ten lectures on wavelets. Vol. 61. Philadelphia: Society for industrial and applied mathematics. Davidson, Russell, Walter C. Labys, and Jean-Baptiste Lesourd(1997). "Walvelet analysis of commodity price behavior." Computational Economics 11.1-2: 103-128. Engle, Robert F(1982). "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation." Econometrica: Journal of the Econometric Society : 987-1007. Fang, Hsing, and K. Kern Kwong(1991). "Forecasting foreign exchange rate." Journal of Business Forecasting : 16-19. Granger, Clive WJ, and Paul Newbold(1974). "Spurious regressions in econometrics."Journal of econometrics 2.2: 111-120. Grossmann, Alexander, and Jean Morlet(1984). "Decomposition of Hardy functions into square integrable wavelets of constant shape." SIAM journal on mathematical analysis 15.4: 723-736. Karim, Samsul Ariffin Abdul, et al(2011). "Applications of Wavelet Method in Stock Exchange Problem." Journal of Applied Sciences 11.8 : 1331-1335. Kim, Sangbae, and Francis In(2007). "On the relationship between changes in stock prices and bond yields in the G7 countries: Wavelet analysis." Journal of International Financial Markets, Institutions and Money 17.2: 167-179. Mehran, Jamshid, and Manuchehr Shahrokhi(1997). "An application of four foreign currency forecasting models to the US dollar and Mexican peso." Global Finance Journal 8.2: 211-220. Nelson, Daniel B(1991). "Conditional heteroskedasticity in asset returns: A new approach." Econometrica: Journal of the Econometric Society : 347-370. Railean, Ion, Sorin Moga, and Monica Borda. "Forecasting by neural networks in the wavelet domain." Acta technica napocensis-electronica-telecomunicatii50.4 (2009): 15-27. Ramsey, James B., and Zhifeng Zhang(1997). "The analysis of foreign exchange data using waveform dictionaries." Journal of Empirical Finance 4.4: 341-372. Rua, António, and Luís C. Nunes(2009). "International comovement of stock market returns: A wavelet analysis." Journal of Empirical Finance 16.4: 632-639.
Description:	碩士國立政治大學金融研究所 100352027 101
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0100352027
Data Type:	thesis
Appears in Collections:	[Department of Money and Banking] Theses

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