臺灣博碩士論文加值系統

本研究以道瓊工業指數與史坦普500指數為資料，探討技術分析用於波動率預測之成效。首先我們以實現波動率為資料、由技術分析產出交易訊號，再將交易訊號加入以日報酬率為資料的GARCH模型之條件變異數內進行參數估計，並使用逐月滾動的方法進行預測。評估預測的成效我們以逐步預測力優劣檢定挑出可擊敗GARCH基準模型的本研究模型，其中以MAE與MSE作為評價基準建立損失函數。實驗結果發現本研究利用技術分析調整後的GARCH模型有少部分可顯著擊敗GARCH基準模型。

We use Dow Jones Industrial Average and S&P 500 Index as the data to discuss the effectiveness of technical analysis predicting volatility. We first use realized volatility to produce technical trading signals. We then use these trading signals adding to the conditional variance of GARCH model which is obtained from daily return data, and we estimate the parameters of GARCH model by using rolling-window forecast method. Finally we use the SSPA test to evaluate the effectiveness of forecasting, by using the MAE and MSE as two evaluation criterions to build up the loss functions. Our results show a few adjusted GARCH model can outperform the benchmark model.

摘要 i
Abstract ii
目錄 iii
表目次 iv
第一章 緒論 1
第一節 研究背景 1
第二節 研究目的與架構 2
第二章 文獻探討 3
第一節 技術分析 3
第二節 GARCH模型 3
第三節 最佳化方法 4
第四節 資料探勘問題 5
一、 定態拔靴法 6
二、 逐步預測力優劣檢定法 6
第三章 資料與研究方法 8
第一節 資料介紹 8
一、 報酬率 9
二、 實現波動率 9
第二節 技術交易規則 11
一、 濾嘴法則(FR) 11
二、 移動平均(MA) 12
三、 支撐與壓力(SR) 12
四、 通道突破(CB) 13
第三節 模型設定 15
一、 基準模型 16
二、 競爭模型 16
第四節 模型配適 17
一、 參數最佳化 17
二、 預測方法 17
第五節 評價預測結果 18
第四章 實證結果與分析 20
第一節 預先分析 20
第二節 預測結果 21
第五章 結論與建議 24
第一節 結論 24
第二節 未來研究及建議 24
參考文獻 25

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