論文提要內容：
　　本研究依據Jondeau, Wang, Yan and Zhang (2020)的研究方法，將上市櫃公司透過Amihud (2002) 非流動性指標篩選流動性前99%公司以及每股高於10元的股票，而後計算出市值加權SKEW與平均加權SKEW。採用台灣上市櫃的股票、台灣VIX指數和台灣加權指數期貨作為研究樣本，研究期間為2016年2月15日至2020年11月17日之日資料，目的為研究SKEW指數能否預測台指期報酬。
　　實證結果發現市值加權SKEW在預測台指期報酬上表現最為出色，當市值加權SKEW增加一個標準差，台指期報酬便會0.0047%，但平均加權SKEW在預測台指期報酬上是不成功的，因為平均加權法對所有公司一視同仁，報酬極端的小公司出現高波動性的現象，平均加權SKEW在預測台指期失敗是由於它對異常值的敏感性，而市值加權法能利用市值比重將小公司的高波動性影響降低，而加入落差期後，落差期平均加權SKEW表現比落差期市值加權SKEW更為出色，但樣本外預測發現當期市值加權SKEW對實際值最為貼近，為所有變數中表現最出色。

Based on the research method of jondeau, Wang, Yan and Zhang (2020), this paper selects the top 99% of the stocks with liquidity and the stocks with more than 10 yuan per share through Amihud (2002) illiquidity index, and then calculates the market value weighted skew and average weighted skew. This paper uses Taiwan's stocks, Taiwan's VIX index and Taiwan index futures as research samples. The research period is from February 15, 2016 to November 17, 2020. The purpose is to study whether the skew index can predict the return of Taiwan's index period.
The empirical results show that the market value weighted skew is the best in predicting the return of the Taiwan index. When the market value weighted skew increases by a standard deviation, the return of the Taiwan index will be 0.0047%.The average weighted skew is not successful in predicting the return of the Taiwan index, because the average weighted method treats all companies equally, and small companies with extreme returns have high volatility. The failure of average weighted skew in forecasting Taiwan index period is due to its sensitivity to outliers. The market value weighted method can reduce the impact of high volatility of small companies by using the market value proportion. After adding the lagged period, the lagged average weighted skew performs better than the lagged market value weighted skew. Market value weighted skew is the closest to the actual value in out of sample prediction, and it is the best of all variables.

目錄
目錄		i
表目錄		iii
圖目錄		iv
第一章　緒論	1
第一節、研究背景與動機	1
第二節、研究目的	2
第三節、研究架構與流程	4
第二章　文獻探討	5
第一節、波動率指數 (VIX)	5
第二節、黑天鵝指數(skew index)	10
第三節、領先與落後、偏斜及VIX相關文獻	13
第三章 研究方法	21
第一節、VIX指數	21
第二節、偏斜係數	24
第三節、預測能力模型	27
第四節、預測能力指標	28
第四章 實證結果與分析	30
第一節、樣本資料處理	30
第二節、基本敘述統計	32
第三節、迴歸分析	34
第四節、樣本外預測	42
第五章 結論	46
第一節、結論	46
第二節、建議	47
參考文獻		48


 
表目錄
表2.2.1 SKEW與S&P500相關係數		13
表4.1.1年度股票檔數		31
表4.2.1基本敘述統計		33
表4.2.2相關係數		34
表4.3.1 市值加權SKEW和標準差預測能力模型迴歸分析		36
表4.3.2 平均加權SKEW和標準差預測能力模型迴歸分析		38
表4.3.3 落差期市值加權SKEW和標準差預測能力模型迴歸分析		40
表4.3.4 落差期平均加權SKEW和標準差預測能力模型迴歸分析		42
表4.4.1 市值加權SKEW和標準差預測能力模型預測誤差分析		43
表4.4.2 平均加權SKEW和標準差預測能力模型預測誤差分析		43
表4.4.3 落差期市值加權SKEW和標準差預測能力模型預測誤差分析		44
表4.4.4 落差期平均加權SKEW和標準差預測能力模型預測誤差分析		44

 
圖目錄
圖1.4.1 研究流程圖		4
圖2.1.1 CBOE VIX走勢圖		9
圖2.2.1 1987年S&P500隱含波動率曲線		11
圖2.2.2 S&P500與SKEW走勢圖		12
圖4.4.1市值加權SKEW和報酬標準差公式Ⅲ預測誤差圖		45
圖4.4.2平均加權SKEW和報酬標準差公式Ⅲ預測誤差圖		45
圖4.4.3落差期市值加權SKEW和報酬標準差公式Ⅲ預測誤差圖		45
圖4.4.4落差期平均加權SKEW和報酬標準差公式Ⅲ預測誤差圖		45

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