| dc.contributor.advisor | 廖四郎 | zh_TW |
| dc.contributor.advisor | Liao, Szu Lang | en_US |
| dc.contributor.author (Authors) | 吳修宏 | zh_TW |
| dc.contributor.author (Authors) | Wu, Hsiu Hung | en_US |
| dc.creator (作者) | 吳修宏 | zh_TW |
| dc.creator (作者) | Wu, Hsiu Hung | en_US |
| dc.date (日期) | 2012 | en_US |
| dc.date.accessioned | 1-Jul-2014 12:06:42 (UTC+8) | - |
| dc.date.available | 1-Jul-2014 12:06:42 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Jul-2014 12:06:42 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0100352027 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/67098 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融研究所 | zh_TW |
| dc.description (描述) | 100352027 | zh_TW |
| dc.description (描述) | 101 | zh_TW |
| dc.description.abstract (摘要) | 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。 | zh_TW |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.description.tableofcontents | 第一章 緒論 - 1 - 第一節 研究背景及動機 - 1 - 第二節 研究目的 - 1 - 第三節 本文內容 - 2 - 第二章 文獻回顧 - 3 - 第一節 時間序列模型 - 3 - 第二節 小波轉換 - 4 - 第三章 研究方法 - 6 - 第一節 白噪音、定態序列與安定條件 - 6 - 第二節 ARMA(p,q)/ARIMA(p,d,q)模型 - 7 - 第三節 ARCH(q)/GRACH(p,q)模型 - 8 - 第四節 Random walk模型 - 9 - 第五節 假性迴歸問題與單根檢定 - 10 - 第六節 小波轉換 - 12 - 第四章 實證分析 - 16 - 第一節 實證模型 - 16 - 第二節 資料來源及分析 - 18 - 第三節 實證結果及比較 - 20 - 第五章 結論與建議 - 31 - 參考文獻 - 32 - | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0100352027 | en_US |
| dc.subject (關鍵詞) | 小波分析 | zh_TW |
| dc.subject (關鍵詞) | 預測 | zh_TW |
| dc.subject (關鍵詞) | 匯率 | zh_TW |
| dc.subject (關鍵詞) | 時間序列 | zh_TW |
| dc.subject (關鍵詞) | wavelet transform | en_US |
| dc.subject (關鍵詞) | forecasting | en_US |
| dc.subject (關鍵詞) | exchange rate | en_US |
| dc.subject (關鍵詞) | time series | en_US |
| dc.title (題名) | 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 | zh_TW |
| dc.title (題名) | The Influence of Time Series model Forecasting Accuracy On Wavelet Analysis -Evidence from NTD/USD exchange rate | en_US |
| dc.type (資料類型) | thesis | en |
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