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研究生:楊琪倫
研究生(外文):Yang Chi Lun
論文名稱:台灣指數期貨開盤價預測之研究
論文名稱(外文):Forecasting the Opening Index Futures Prices Using Networks: Evidence from the TAIFEX TAIEX Index Futures Contracts
指導教授:李天行李天行引用關係
指導教授(外文):Lee Tian-Shyug
學位類別:碩士
校院名稱:輔仁大學
系所名稱:管理學研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:55
中文關鍵詞:類神經網路GARCH指數期貨台股指數期貨
外文關鍵詞:neural networksGARCHfuturesforecasting
相關次數:
  • 被引用被引用:9
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:6
由於金融市場的開放,衍生性金融商品使用增加,但對於台灣近幾年才推出的台灣指數期貨商品(TAIEX)研究甚少,因此希望本研究能提供一個對價格良好之預測模式,提供投資者作為參考之用。本研究在針對TAIEX之開盤價格作預測,利用類神經網路以及GARCH模型作為預測的工具,除了探討TAIEX開盤前MSCI價格以及國際股市與期貨市場價格是否隱含對TAIEX開盤價預測之資訊,也可了解到對於TAIEX開盤價之預測,國際股市與期貨市場價格之漲跌是否已充分反映於開盤前MSCI指數上。
結論顯示利用類神經網路可精準預測TAIEX開盤價格,其最適模型之預測績效優於傳統的統計模型(GARCH模型)及隨機漫步模型。此外,TAIEX開盤前之MSCI價格以及國際股市與期貨市場價格對開盤價之預測有所幫助。最後對於TAIEX開盤價之預測,國際股市之漲跌尚未充分反映於開盤前MSCI期貨指數上,也代表對於TAIEX開盤價之預測上,增加國際股市與期貨市場指數的資訊可提高準確度以及其必要性。
This study investigates the influence of SGX-DT MSCI Taiwan index futures prices during the non- trading period (NPT) of TAIFEX TAIEX Taiwan index futures, previous day’s Dow Jones and Nasdaq’s closing index, and the same day’s Nikkei 225 opening index futures to the opening index futures of TAIFEX TAIEX Taiwan index. Three NPT MSCI futures prices, previous day’s closing index futures at 12:15, previous day’s Dow Jones and Nasdaq’s closing index, and the opening Nikkei 225 index futures are used to forecast the 09:00 opening index futures of TAIFEX TAIEX by the neural networks model. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate setup of the topology of the networks. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. To demonstrate the effectiveness of our proposed method, the five-minute intraday data of futures index, Dow Jones and Nasdaq’s closing index and the opening index futures of Nikkei 225 from October 1, 1998 to December 31, 20000 was evaluated using the designed neural network model. Empirical results demonstrate that the proposed neural networks model outperforms the neural network model with previous day’s closing index as the input variable, the random walk and GARCH model forecasts. It therefore indicates that there is valuable information involved in the futures prices during NPT of TAIFEX TAIEX, Dow Jones and Nasdaq’s closing index, and the opening Nikkei 225 index futures and hence contribute to the success in forecasting the opening index futures of TAIFEX TAIEX.
目錄
目錄 I
圖目錄 II
表目錄 III
第壹章、緒論 1
一、研究動機與背景 1
二、研究目的 3
三、研究架構 4
四、研究流程 4
第貳章、文獻探討 6
一、台股指數期貨相關之文獻 6
二、國際股市間之訊息傳遞 9
三、類神經網路在期貨市場上的運用 15
四、小結 19
第參章、研究方法 21
一、類神經網路 21
二、GARCH模型 25
第肆章、實證研究 29
一、類神經網路模型之預測分析 31
二、GARCH模型之預測分析 34
三、預測能力綜合比較 40
第伍章、結論與建議 42
一、結論與研究貢獻 42
二、研究建議 43
參考文獻 44
附錄 51
圖目錄
圖1-1 研究流程圖 5
圖3-1 神經元之構造 22
圖3-2 倒傳遞類神經網路結構圖 24
圖4-1 TAIEX開盤價趨勢圖 30
圖4-2類神經網路最佳模式訓練樣本之RMSE趨勢圖 33
表目錄
表4-1 預測變數(應變數)與輸入變數之整理 30
表4-2 類神經網路最佳模式之輸入變數在不同隱藏層與學習率下
之訓練及測試結果……………………………………………32
表4-3 ADF與P. P.檢定結果(差分前) 34
表4-4 ADF與P. P.檢定結果(差分後) 35
表4-5 L-B-Q(K)檢定表 36
表4-6 ARCH LM檢定表 37
表4-7各階次模型之AIC與SIC值 38
表4-8 GARCH(2, 1)預測模型之估算結果(I) 39
表4-9 GARCH(2, 1)預測模型之估算結果(II) 39
表4-10 本研究各模式之RMSE與RMSPE值 41
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