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系統識別號 U0002-2705201122295600
中文論文名稱 應用高頻率資料提升波動模型預測能力之研究
英文論文名稱 Improving Predictive Ability of Volatility Models with High-frequency Data
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士班
系所名稱(英) Department of Banking and Finance
學年度 99
學期 2
出版年 100
研究生中文姓名 張黃威
研究生英文姓名 Huang-Wei Zhang
學號 698530796
學位類別 碩士
語文別 中文
口試日期 2010-05-21
論文頁數 75頁
口試委員 指導教授-邱建良
共同指導教授-劉洪鈞
委員-邱建良
委員-李命志
委員-劉洪鈞
委員-涂登才
委員-俞海琴
中文關鍵字 波動估計式  GARCH  已實現波動率  已實現變幅  已實現雙冪次變異 
英文關鍵字 Volatility estimator  GARCH  Realized volatility  Realized range volatility  Realized bipower variation 
學科別分類 學科別社會科學商學
中文摘要 本研究以美國個股(微軟、亞馬遜)、股價指數(S&P 500指數、那斯達克綜合指數)與指數型股票基金(道瓊工業平均指數基金)自2001年1月至2010年5月之日資料為實證標的,探討加入日變幅(PK)、已實現波動率(RV)、已實現變幅(RRV)與已實現雙冪次變異(RBP)等波動估計式對於GARCH模型樣本外預測能力的提升效果。分別以PK及RV作為市場真實波動的代理變數,並採用各種損失函數評估各波動模型的預測績效。
本文以五分鐘頻率之日內資料來估計RV、RRV與RBP,進一步探討高頻率資料的效果。實證發現,各種波動估計式對GARCH模型波動預測存在不同程度的提升效果,其中又以RV、RRV及RBP等高頻日內資料為基礎之波動估計式表現較佳。而以RV及PK作為真實波動性之代理變數下的預測績效比較結果趨於一致,證明預測結果的穩健性,也另外說明除了RV適合作為真實波動性之代理變數外,PK也是個不錯選擇。
另外,波動估計式對於提升波動模型預測績效的程度會因標的不同而
有所差異,本文研究發現在個股方面的提升效果最佳,此結果隱含在波動性較大的標的下更能顯現波動估計式的效果,因此投資者在投資高波動性之金融商品時,可應用波動估計式提升模型的波動性預測績效。
英文摘要 This paper augments the GARCH models with the PK range, realized volatility (RV), realized range volatility (RRV) and realized bipower variation (RBP). We investigate the impact of these volatility estimators by examining their out-of-sample forecast-improved. The data for our empirical study consists of individual stocks (Amazon and Microsoft), stock indices (S&P 500 and Nasdaq) and exchange traded fund (Dow Jones Industrial Average ETF) price quotes covering the period from 16 January 2001 to 28 May 2010. The forecast performance evaluation is relied on several loss functions and utilizing PK and RV as a proxy for true volatility. RV, RRV and RBP are intraday-based
Volatilities which are obtained from intraday prices at 5-min frequency. So we can study the effect of high frequency data. Empirical results indicate that all volatility estimators can improve predictive ability. Especially, the inclusion of
intraday-based volatility measure in GARCH models notably improves forecasts. In most cases, the forecasting performances of models are almost consistent which are robust to alternative proxy measures, indicating that the PK is a useful alternative to the RV since daily high-low price data are readily available for most financial assets.
Additionally, the degree of incremental predictive content of these volatility estimators varies from the data used. The volatility estimator provides the most incremental predictive content on individual stock. It implies that the volatility estimator can be more advantageous with higher volatility commodity. Thus, market practitioners can exploit the information content implied by these volatility estimators to improve forecast accuracy of models when they invest in financial instruments with higher volatility.
論文目次 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究架構 5
第四節 研究流程圖 6
第二章 文獻回顧 7
第一節 波動性預測模型 7
第二節 GARCH族預測能力比較之相關文獻 11
第三節 加入外生變數的增廣GARCH模型及日內資料相關文獻 17
第三章 研究方法 27
第一節 單根檢定 27
第二節 ARCH效果檢定 29
第三節 SGT分配 31
第四節 波動估計式 33
第五節 波動性預測模型架構 36
第六節 模型預測能力比較 38
第四章 實證結果 40
第一節 資料之基本分析 40
一、 資料來源與處理 40
二、 基本統計量分析 41
第二節 單根檢定與ARCH效果檢定之結果 46
一、 單根檢定 46
二、 ARCH效果檢定 49
第三節 波動模型之參數估計結果 50
第四節 模型預測能力比較 54
五、 結論 66
參考文獻 68
一、 國內文獻 68
二、 國外文獻 70

表目錄
【表2.3.1】加入隱含波動率對波動性預測能力的提升效果 18
【表2.3.2】加入成交量對波動性預測能力的提升效果 20
【表2.3.3】不同樣本外預測期間下績效改善之比較 22
【表2.3.4】加入RV之模型預測能力提升效果比較 22
【表2.3.5】加入PK之模型預測能力提升效果比較 23
【表2.3.6】加入各解釋變數之文獻整理 25
【表4.1.1】基本統計量之分析 42
【表4.1.1】基本統計量之分析(續) 43
【表4.2.1】時間序列資料之ADF單根檢定 47
【表4.2.2】時間序列資料之PP單根檢定 48
【表4.2.3】ARCH效果之檢定結果 49
【表4.3.1】個股波動模型參數估計結果 51
【表4.3.2】股價指數波動模型參數估計結果 52
【表4.3.3】指數型基金波動模型參數估計結果 53
【表4.4.1】波動性預測能力比較-以RV為代理變數 58
【表4.4.1】波動性預測能力比較-以RV為代理變數(續) 59
【表4.4.1】波動性預測能力比較-以RV為代理變數(續) 60
【表4.4.2】波動性預測能力比較-以PK為代理變數 61
【表4.4.2】波動性預測能力比較-以PK為代理變數(續) 62
【表4.4.2】波動性預測能力比較-以PK為代理變數(續) 63
【表4.3.3】加入波動估計式績效提升之比較 65
【表4.3.4】加入RV之績效提升比較 65

圖目錄
【圖1.4】研究流程圖 6
【圖3.3.1】SGT分配和標準常態分配比較圖 32
【圖4.1.1】亞馬遜之時間序列資料趨勢圖 44
【圖4.1.2】微軟之時間序列資料趨勢圖 44
【圖4.1.3】S&P 500股價指數之時間序列資料趨勢圖 44
【圖4.1.4】那斯達克股價指數之時間序列資料趨勢圖 45
【圖4.1.5】道瓊工業指數型基金之時間序列資料趨勢圖 45
【圖4.4.1】真實波動性之代理變數走勢圖-亞馬遜 55
【圖4.4.2】真實波動性之代理變數走勢圖-微軟 55
【圖4.4.3】真實波動性之代理變數走勢圖-S&P500股價指數 55
【圖4.4.4】真實波動性之代理變數走勢圖-那斯達克股價指數 56
【圖4.4.5】真實波動性之代理變數走勢圖-道瓊工業指數型基金 56

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