考量一般投資人或股票型基金所持有的投資組合大多非完全風險分散的組合，常有集中於某些權值股或是藍籌股的機會，因此本研究以風險分散不足之投資組合：美國熱門三支個股，分別為亞馬遜 (Amazon；AMZN)、蘋果電腦 (APPLE)以及高盛銀行 (Goldman Sachs ; GS)為研究對象，樣本資料期間為2001年1月2日至2010年5月28日，其中2001年1月2日至2005年12月30日為樣本內估計，而2006年至2010年5月28日之資料為波動度預測期間。分別採用以報酬 (return) 概念為主之CCC-GARCH (constant conditional correlation GARCH)、DCC-GARCH (dynamic conditional correlation GARCH)模型方法和已實現變異數法 (realized variance) 以及變幅 (range) 概念之已實現變幅變異數法 (realized range-based variance) 去分別求算出波動度，再透過波動擇時策略衡量這四種方法下的經濟價值來做比較探討。過往研究中，常將預測出的波動以統計損失函數，例如：MSE等來驗證其績效；但在此研究中特別將預測出的波動度實際應用於財務上，以波動擇時策略也就是現在時常被討論的資產配置觀念來驗證波動估計方法的經濟價值。
    實證結果發現，透過平均數、標準差、夏普值、投資組合變動程度與損益兩平之成本等衡量指標來觀察四種方法下的波動擇時策略，與Bannouh, van Dijk and Martens (2009)文獻中之論點相符；以變幅概念之已實現變幅變異數法所估計出的波動其帶來的經濟價值會優於以報酬概念為主之CCC-GARCH、DCC-GARCH模型方法和已實現變異數法，且即使標的物為相關性高之風險不分散投資組合，也能利用已實現變幅變異數法來估計波動度以提供投資人較佳之經濟價值。

Consider the general investors or equity funds are mostly held by the diversified portfolio of risk concentration .This study focus on the portfolio composed by the stocks, were the Amazon, Apple Computer and Goldman Sachs, the period of sample data were January 2, 2001 to May 28, 2010. We used the return based method of the CCC-GARCH, DCC-GARCH model and the realized variance method and the range based method of realized rang-based variance method to forecast the volatility, and measured the volatility forecast method by volatility timing strategies respectively in order to compare the economic value of the fore volatility forecast methods. Previous studies, often used the statistical loss function, such as: MSE, etc. to verify the performance of volatility forecast method; but specifically in this study we verify the economic value of the fore volatility forecast methods by the volatility timing strategy , the hot topics about asset allocation. 
    Empirical results match with the point mentioned by Bannouh, van Dijk and Martens (2009). Through the mean, standard deviation, Sharpe ratio, turnover and the degree of break-even cost of four methods to observe the volatility timing strategies, proved that the realized range-based variance is better than the CCC-GARCH, DCC-GARCH model and the realized variance method on volatility forecast. Even if the diversified portfolio were risk concentration, but also can use the realized range-based variance method to forecast the volatility to provide investors with better economic value.

第一章	緒  論	1
第一節 研究動機與背景	1
第二節 研究目的	3
第三節 研究架構	6
第二章	理論基礎與文獻回顧	8
第一節 波動性探討	8
第二節 估計波動性之模型發展	9
第三節 變幅波動 (Range-based Volatility)	14
第四節 投資組合理論介紹	16
第三章	研究方法與理論模型	18
第一節 單根檢定	18
第二節　ARCH效果檢定	23
第三節　GARCH模型	25
第四節　波動率的估計方式	33
第五節 波動擇時策略(Volatility timing)	37
第六節 績效評估	38
第四章	實證結果分析	39
第一節　研究對象與資料處理	39
第二節　基本統計量分析	48
第三節　單根檢定	49
第五章	結論	56
參　考　文　獻	58
一、國外文獻	58
二、國內文獻	66
表　　　目　　　錄
【表一】各股價日報酬率之基本敘述統計量	48
【表二】各股價指數報酬率之單根檢定	49
【表三】各股價報酬率之ARCH效果檢定	50
【表四】波動擇時策略	55
圖　　　目　　　錄
【圖一】：研究流程圖	7
【圖二】：AMAZON/APPLE/GOLDMAN SACHS2001年至2010年間的股價走勢	40
【圖三】: AMZN 歷年股價走勢 / 於已實現變幅法下之波動圖	42
【圖四】: AMZN歷年股價走勢 / 於已實現變異數法下之波動圖	42
【圖五】: AMZN歷年股價走勢 / 於GARCH模型下之波動圖	43
【圖六】: APPLE歷年股價走勢 / 於已實現變幅法下之波動圖	44
【圖七】: APPLE 歷年股價走勢 / 於已實現變異數法下之波動圖	44
【圖八】: APPLE 歷年股價走勢 / 於GARCH模型下之波動圖	45
【圖九】: GS歷年股價走勢 / 於已實現變幅法下之波動圖	46
【圖十】: GS歷年股價走勢 / 於已實現變異數法下之波動圖	46
【圖十一】: GS歷年股價走勢 / 於GARCH模型下之波動圖	47
【圖十二】：已實現變幅法下投資權重之動態調整圖 (目標報酬=5%)	52
【圖十三】：已實現變異數法下投資權重之動態調整圖 (目標報酬=5%)	52
【圖十四】： CCC-GARCH 下投資權重之動態調整圖 (目標報酬=5%)	52
【圖十五】：DCC-GARCH 下投資權重之動態調整圖(目標報酬=5%)	53

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