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研究生: 莊淑棻
Chuang, Shu-Fen
論文名稱: 亞洲金融風暴前後台灣股票市場元月效應與春節效應之研究
The Research on January Effect and Chinese Lunar New Year Effect in Taiwan Stock Market Before and After Asian Financial Crisis
指導教授: 蔡長鈞
Tsai, Chang-chun
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年: 2008
畢業學年度: 96
語文別: 中文
論文頁數: 91
中文關鍵詞: 亞洲金融風暴元月效應春節效應ARIMAEGARCH
外文關鍵詞: Asian Financial Crisis, EGARCH, ARIMA, January Effect, Chinese Lunar New Year Effect
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  • 本文使用Box和Jenkins(1970)提出的Autoregressive Integrated Moving Average模型與Nelson(1991)的Exponential Generalized Autoregressive Conditional Heteroscedasticity模型進行台灣股價指數之實證研究。對一些學者已檢視出來在股票市場中,既存的規則與現象是否因為亞洲金融風暴之發生而產生改變。檢定台灣股市大盤與八大產業於亞洲金融風暴前後,在元月效應及春節效應上的顯著性與差異。
    本研究發現金融、營建與造紙等產業,不管是在亞洲金融風暴發生前或發生後,皆存在元月效應,代表這三個產業不受風暴發生之影響。而台灣股市在亞洲金融風暴發生之前後,至少都有一半(4個)以上的產業,存在元月效應,且整體之大盤在亞洲金融風暴發生後,反而出現元月效應。顯然元月效應沒有受到亞洲金融風暴發生之影響。而在春節效應上,於亞洲金融風暴發生前之大盤是顯著存在的,八大產業中也有金融、機電、食品與水泥窯等四個產業存在春節效應。但在風暴發生後卻只剩金融產業存在春節效應。因此春節效應明顯地受到亞洲金融風暴發生之影響,且在亞洲金融風暴發生後十年內消失了。

    The research is to discuss the stock price in Taiwan in terms of ARIMA announced by Box and Jenkins as well as Nelson’s EGARCH, that is, to survey the feathers and differences shown on January Effect and Chinese Lunar New Year Effect among Taiwan stock market and first eight industries after Asian Financial Crisis. Some scholars have been curious about whether certain used rulers and appearances in stock market were changed or not after Asian Financial Crisis.
    This study indicate that industries such as banking, construction, and paper-making all present January Effects no matter whether Asian Financial Crisis is happened or not. Thus, Asian Financial Crisis made no impact on these three industries mentioned above. Besides, although general stock market presented January Effect after Asian Financial Crisis, there were over half industries (4 exactly) have had the feature of January Effect before Asian Financial Crisis’s breaking out. Therefore, it can be concluded that Asian Financial Crisis has no effect on January Effect. On the other hand, Chinese Lunar New Year Effect was an obvious feature among stock market and industries of banking, food, electrical engineering, and cement before Asian Financial Crisis. However, there is only banking industry remained the feature of Chinese Lunar New Year Effect after Asian Financial Crisis and that feature disappeared 10 years later of the economic storm. So we can conclude that Asian Financial Effect made an impact on Chinese Lunar New Year Effect.

    摘要 I Abstract II 誌謝 III 目錄 IV 表目錄 VIII 圖目錄 X 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 3 1.4 論文架構 4 第二章 文獻探討 6 2.1 亞洲金融風暴(Asian Financial Crisis) 6 2.1.1 亞洲金融風暴簡述 6 2.1.2 亞洲金融風暴之起因 8 2.1.3 亞洲金融風暴之影響 11 2.2 元月效應(January Effect) 14 2.2.1 美洲國家元月效應相關研究 14 2.2.2 亞洲國家元月效應相關研究 15 2.2.3 歐洲國家元月效應相關研究 16 2.2.4 多個國家元月效應比較相關研究 16 2.3 春節效應(Chinese Lunar New Year Effect) 22 2.4 時間序列之定態與單根檢定 25 2.4.1 變數定態性 25 2.4.2 單根檢定(Unit Root test) 26 2.5 自我迴歸整合移動平均模型(ARIMA Model)之探討 27 2.5.1 自我迴歸模式(Autoregressive, AR) 28 2.5.2 移動平均模式(Moving Average, MA) 29 2.5.3 自我迴歸移動平均模型(Autoregressive Moving Average, ARMA) 29 2.6 自我迴歸條件異質變異數模型(ARCH Model)之概述 30 2.7 指數型一般化自我迴歸條件異質變異數模型(EGARCH Model)之探討 31 2.7.1 EGARCH(p, q)模型之介紹 31 2.7.2 EGARCH(p, q)模型設定之限制 32 第三章 研究方法 33 3.1 研究架構 33 3.2 研究假說 33 3.3 資料內容 35 3.4 資料分析方法 35 3.4.1 常態性檢定 35 3.4.2 ADF單根檢定法(Augmented Dickey-Fuller test) 36 3.4.3 差分運算(Differencing Operator) 37 3.5 自我迴歸整合移動平均模型(ARIMA Model)之估計步驟 38 3.5.1 自我相關函數(ACF)與偏自我相關函數(PACF) 39 3.5.2 序列自我相關性檢定 40 3.6 EGARCH模型之估計步驟 41 3.6.1 殘差平方之自我相關性檢定 41 3.6.2 模型選取準則 43 3.7 元月效應與春節效應之實證模型 43 3.7.1 元月效應之實證模型 43 3.7.2 春節效應之實證模型 44 第四章 實證結果與分析 46 4.1 資料之描述與分析 46 4.1.1 台灣股市大盤月收盤股價指數與月報酬之走勢圖 46 4.1.2 台灣股市八大產業月收盤股價指數與月報酬之走勢圖 48 4.1.3 樣本資料之基本特性 53 4.2 時間序列之定態性 61 4.2.1 股價指數之自然對數走勢圖與單根檢定 61 4.2.2 月報酬之走勢圖與單根檢定 61 4.3 ARIMA模型之估計過程 69 4.3.1 AR項與MA項之判斷 69 4.3.2 殘差之自我相關性檢定 70 4.3.3 ARIMA模型之估計結果 70 4.4 EGARCH模型之估計過程 72 4.4.1 EGARCH模型落後期數(p, q)之判斷 72 4.4.2 殘差平方之自我相關性檢定 73 4.4.3 模式之選取依據 74 4.4.4 EGARCH模型之估計結果 75 4.5 元月效應與春節效應之實證結果 76 4.5.1 元月效應之模型驗證 76 4.5.2 春節效應之模型驗證 78 4.5.3 研究假說之驗證 79 4.6 實證研究與分析之結論 81 第五章 結論與建議 83 5.1 結論 83 5.2 建議 84 參考文獻 85 附錄 90

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