運用日內資料提升選擇權價格預測準確性之研究

淡江大學機構典藏 > 商管學院 > 財務金融學系暨研究所 > 學位論文 > Item 987654321/87198

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题名:	運用日內資料提升選擇權價格預測準確性之研究
其它题名:	Improving forecast accuracy of stock index option prices by using intraday data
作者:	周益賢;Chou, Yi-Hsien
贡献者:	淡江大學財務金融學系碩士班邱建良;Chiu, Chien-Liang
关键词:	日內資料;日變幅;已實現波動;選擇權;SPA檢定;Intraday data;Daily range;realized volatility;option;SPA test
日期:	2012
上传时间:	2013-04-13 10:50:45 (UTC+8)
摘要:	本研究擬在善於捕捉條件異質變異特性的GARCH架構下，考慮三類波動模型：(i) GARCH(1,1)模型、(ii) 在GARCH的條件變異數方程式中分別加入日變幅(PK range)及已實現波動(Realized volatility, RV)之GARCH-X模型、(iii) 將GARCH模型條件變異數方程式的殘差帄方項改以RV取代之MGARCH模型(Modified GARCH)，進行台灣加權股價指數之日波動性預測，並將各類模型所預測的波動性代入BS模型計算台指選擇權的理論價格後，再與市場價格進行比較，探討日內交易資訊能否提升GARCH模型對台指選擇權價格的預測準確性。同時，本研究擬以不同頻率之日內資料估計RV，檢視價格資訊頻率對於選擇權價格預測的影響效果，進一步尋求最適的日內資訊頻率。實證結果發現，GARCH-X模型及MGARCH模型的預測績效均優於傳統GARCH模型。因此，RV確實能提升GARCH模型對選擇權標的資產的波動預測準確性，進一步得到較佳的選擇權價格預測。其次，在不同的資訊頻率下，GARCH-X模型預測之選擇權價格較MGARCH模型更為準確。再者，SPA檢定的結果指出GARCH-RV10模型顯著優於其它模型。最後，10分鐘頻率的日內資料最具資訊價值。 Based on the GARCH (generalized autoregressive conditional heteroskedasticity, GARCH) framework, this thesis considers three volatility model categories: (i) the GARCH(1,1) model, (ii) the GARCH-X model which augments the traditional GARCH model by respectively incorporating daily PK range and RV (realized volatility, RV) as explanatory variable into the GARCH variance equation, (iii) the MGARCH model (modified GARCH) that modifies the GARCH by replacing squared residuals of its variance equation with RV. These models are used to investigate the information value of the high frequency data that is embodied in the PK/RV for improving forecasts of TAIEX option (TXO) prices at daily horizon. Empirical results indicate that both of the GARCH-X and MGARCH models perform better than the traditional GARCH(1,1) model, suggesting that the GARCH-based option price forecasts can be moderately improved with the additional information contained in volatility estimators considered in this study. Secondly, the GARCH-X model always generates more accurate option price forecasts than the MGARCH model, irrespective of data frequency. Thirdly, the SPA test results show that the GARCH-RV10 significantly outperforms other models. Finally, the intraday data with ten-minute frequency is the most informative.
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