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系統識別號 U0002-1406201115105600
中文論文名稱 應用經驗模態分解及頻譜分析重建時間序列
英文論文名稱 An Application of Empirical Mode Decomposition and Spectrum Analysis to Reconstruct Time Series
校院名稱 淡江大學
系所名稱(中) 管理科學研究所博士班
系所名稱(英) Graduate Institute of Management Science
學年度 99
學期 2
出版年 100
研究生中文姓名 白明珠
研究生英文姓名 Ming-Chu Pai
學號 892560094
學位類別 博士
語文別 中文
口試日期 2011-06-10
論文頁數 83頁
口試委員 指導教授-張紘炬
委員-林進財
委員-陳耀竹
委員-黃建森
委員-李培齊
委員-歐陽良裕
委員-莊忠柱
中文關鍵字 經驗模態分解法  累積本質模態函數  頻譜分析  價格發現 
英文關鍵字 Empirical mode decomposition  Cumulative intrinsic mode functions  Spectrum analysis  Price discovery 
學科別分類
中文摘要 過去對於財務經濟的數據分析,都要求該數據必須為定態資料,甚至必須為線性資料。實際上,資料往往是非定態,導致研究者經常需要進行資料的定態轉換,而此舉往往使得資料喪失原有的特徵。本研究選取2006年至2009年度,S&P500股價指數、全球鋼鐵價格指數及布蘭特原油現貨價格等,利用Huang et al.(1998)所提出的經驗模態分解程序(empirical mode decomposition, EMD),分析非定態與非線性之時間序列資料。首先運用EMD技術,將時間序列資料,依本身的震盪特徵分解為數個分量序列與一個單調函數,分量序列即為所謂的本質模態函數(intrinsic mode functions, IMFs)。接著使用頻譜分析的技術,由頻域面界定各個IMFs之週期,再依照實務上的週期分類標準,將各IMFs合成為短期、中期與長期三個主要的分量序列,本研究稱之為累積本質模態函數(cumulative intrinsic mode functions, CIMFs)。研究結果發現三個樣本各自的CIMFs皆為定態序列,不但解決傳統時間序列分析資料必須為定態(stationary)的限制,且CIMFs皆可使用傳統的時間序列分析進行後續研究,開創了資料差分轉換的另外一條道路。本研究亦發現,若將所有的CIMFs與單調函數加總之後,可以還原為原始的時間序列資料,避免了差分轉換造成與原始資料之間的失真問題。另應用此方法,以西德州原油現貨與期貨價格時間序列資料為實證標的,再透過交叉相關係數與VAR探討其交互關係。研究結果發現,期貨短週期的變動對現貨短週期的變動具有價格發現的功能,與過去使用報酬率探討價格發現的研究結果相似。而期貨中週期的變動,同樣對於現貨中週期具有價格發現的功能,此為過去研究所沒有的發現。
英文摘要 Financial data of past economic analyses are either stationary or even linear. In fact, data are always non-stationary; hence, researchers often need to carry out data conversion, which may lose original features of the data. This study applies empirical mode decomposition (EMD) proposed by Huang et al. (1998) for analysis of non-stationary and nonlinear financial and economic time-series data, including S&P 500 stock index, global iron and steel price index, and Brent crude oil price from 2006 to 2009. According to fluctuation characteristics, EMD first decomposes the time series into several component series and a monotonic function. The component series are called intrinsic mode functions (IMFs). Spectral analysis utilizes the frequency domain region to define the IMF period, and aggregates the IMFs into short-, medium-, and long-term component series, which are referred to in this study as cumulative intrinsic mode functions (CIMFs). The findings show that the CIMFs of the three samples are stationary series, thus resolving the restriction on stationary data. Moreover, CIMFs use traditional time-series analysis for further studies, and provide another way for data differential conversion. The proposed approach can restore all CIMFs and one monotonic function to the raw time-series data after aggregation to avoid data distortion caused by differential conversion. This study also selects the West Texas Crude oil spot and futures prices as empirical objects. This result is consistent with previous studies on price discovery using the rate of return. Unlike previous research, this study shows that a medium-term change of futures also leads to price discovery for a medium-term change in spot prices.
論文目次 目 錄
頁次
目 錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
第一節 研究背景及動機 1
第二節 研究問題與目的 2
第三節 研究流程與步驟 5
第二章 文獻探討 8
第一節 HHT和EMD相關文獻 8
第二節 價格發現相關文獻 11
第三章 研究方法 16
第一節 資料來源 16
第二節 研究設計 18
第三節 EMD演算法 22
第四節 傅立葉轉換 24
第五節 交叉相關分析 24
第六節 自我向量迴歸 26
第四章 實證結果 30
第一節 S&P500股價、全球鋼鐵價格及布蘭特原油價格之實證 30
第二節 WTI期貨與現貨價格發現之應用 38
第五章 結論與建議 58
第一節 結論 58
第二節 研究限制與建議 59
參考文獻 61
附錄圖 65
圖A1 研究樣本之原始資料序列走勢 65
圖A2 研究樣本之正規化資料序列走勢 66
圖A3 S&P500之IMFS與走勢 67
圖A4 S&P500之IMFS之能量週期 68
圖A5 S&P500之CIMFS與走勢 69
圖A6 全球鋼鐵價格指數之IMFS與走勢 70
圖A7 全球鋼鐵價格指數之IMFS之能量週期 71
圖A8 全球鋼鐵價格指數之CIMFS與走勢 72
圖A9 布蘭特原油價格之IMFS與走勢 73
圖A10 布蘭特原油價格之IMFS能量週期 74
圖A11 布蘭特原油價格之CIMFS與走勢 75
圖A12 WTI期貨與現貨價格走勢 76
圖A13 WTI期貨與現貨標準化價格走勢 76
圖A14 WTI期貨之IMFS與長期趨勢項 77
圖A15 WTI期貨各IMFS之能量週期 78
圖A16 WTI期貨之CIMFS與長期趨勢項 79
圖A17 WTI現貨之IMFS與長期趨勢項 80
圖A18 WTI現貨各IMFS之能量週期 80
圖A19 WTI現貨之CIMFS與長期趨勢項 81
附錄表 82
表A1 WTI期貨現貨交叉相關分析結果表 82


表 目 錄
頁次
表4.1 S&P500股價、全球鋼鐵價格及布蘭特原油價格-頻譜分結果……….……….31
表4.2 S&P500股價指數之ADF和PP單根檢定結果…………………………...……37
表4.3 全球鋼鐵價格指數之ADF和PP單根檢定結果……………………….………37
表4.4 布蘭特原油價格之ADF和PP單根檢定結果…………….……………………38
表4.5 WTI期貨與現貨-頻譜分析結果……………………..…………………………39
表4.6 WTI期貨與現貨之ADF和PP單根檢定結果………….………………………43
表4.7 WTI期貨與現貨之交叉相關分析結果………………….………..……………45
表4.8 VAR落差期候選表…………………………………….………………46
表4.9 Granger因果檢定結果表…………………………………..……………………47

圖 目 錄
頁次
圖1.1 研究流程………………………………………………………….……………6
圖3.1 應用頻譜分析重建時間序列實證之研究流程.………………………..……20
圖3.2 價格探討之研究流程.…………………………………………………..…23
圖4.1 S&P500股價指數之正規化資料、CIMFs以及長期趨勢走勢………..….…33
圖4.2 全球鋼鐵價格指數之正規化資料、CIMFs以及長期趨勢走勢........……….34
圖4.3 布蘭特原油價格之正規化資料、CIMFs以及長期趨勢走勢……...……..…35
圖4.4 WTI期貨之正規化資料、CIMFs以及長期趨勢走勢………………………41
圖4.5 WTI現貨之正規化資料、CIMFs以及長期趨勢走勢……………42
圖4.6 殘差自我相關分析.……………………………………………..…………45
圖4.7 各變數對期貨短週期的衝擊反應……………...……………………………49
圖4.8 各變數對期貨中週期的衝擊反應………………...…………………………50
圖4.9 各變數對現貨短週期的衝擊反應………………………...…………………51
圖4.10 各變數對現貨中週期的衝擊反應………………………...…………………52
圖4.11 期貨短週期的預測誤差變異分解………………………...…………………53
圖4.12 期貨中週期的預測誤差變異分解……………………...……………………54
圖4.13 現貨短週期的預測誤差變異分解…………………………...………………55
圖4.14 現貨中週期的預測誤差變異分解……………………...……………………56





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