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研究生:沈俞瑄
研究生(外文):Yu-Hsuan Shen
論文名稱:台灣股票市場中個別公司報酬率風險和報酬的關係
論文名稱(外文):Idiosyncratic Risk and Returns in the Taiwanese Stock Market
指導教授:姚睿姚睿引用關係
指導教授(外文):Ruey Yau
學位類別:碩士
校院名稱:國立中央大學
系所名稱:經濟學研究所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:32
中文關鍵詞:個別公司報酬率偏態別公司報酬率變異數風險與低報酬迷思
外文關鍵詞:Idiosyncratic skewnessIdiosyncratic volatilityiv puzzle.
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本文主要目的是在利用人們喜好正偏態報酬的特性,來探討Ang et al. (2006, 2008)提出的高風險卻有低報酬的迷思,是否存在於台灣的上市股票市場。但是經由本文解析,正偏態特性卻不存在於台灣股票市場,論證台灣無法利用正偏態特性來解釋高風險卻有低報酬的迷思。

方法上,首先使用個別公司股票報酬率偏態、變異數和其他公司特性來預測偏態。再以資本資產訂價模型,來檢視報酬率與個別公司股票之報酬率偏態、變異數和其他公司特性關連性,最後以這些實證結果來歸納風險及報酬關鏈性。

本文實證結果顯示,台股報酬與個別公司股票變異數有負向相關,故有所謂的高風險低報酬迷思,但是台股報酬與偏態呈現不顯著結果,故正偏態特性在台灣的股價市場並不存在。
Ang et al. (2006, 2008) proposed that low expected average return with high idiosyncratic volatility appears in U.S. data and G7 countries. Hence, this pattern of the cross-sectional expected average return found by Ang et al. (2006, 2008) is what we call iv puzzle.

This paper aims to investigate whether Taiwanese Stock Market has iv puzzle or not and tries to use idiosyncratic skewness to explain iv puzzle phenomena. But, the results show that idiosyncratic skewness can''t help explain iv puzzle in Taiwanese Stock Market.

This paper uses lagged idiosyncratic skewness, lagged idiosyncratic volatility, and other firm characteristics to predict expected idiosyncratic skewness. Next, expected idiosyncratic skewness and other firm characteristics are used to understand the asset-pricing implications and to investigate whether the historical idiosyncratic volatility and expected idiosyncratic skewness play important roles in asset pricing.

The empirical result indicates that idiosyncratic volatility is negative and strongly statistically significant. The expected idiosyncratic skewness has no significant relationship with expected return.
中文摘要...................i
Abstract...................ii
Acknowledgement............iii
Contents...................iv
List of Tables.............vi
List of Figures............vii
1 Introduction ....................................1
1.1 Motivation . . . . . . . . . . . . . . . . . . 1
1.2 Literature Reviews . . . . . . . . . . . . . . 2
1.2.1 Idiosyncratic Skewness . . . . . . . . . . . 2
1.2.2 Predicting Skewness . . . . . . . . .. . . . 4
1.2.3 Overview of the Paper . . . . . . . . . . . 5
2 Model............................................6
2.1 Obtaining the Idiosyncratic Component .........6
2.2 Predicting Expected Skewness...................7
3 Empirical results................................10
3.1 Data...........................................10
3.1.1 Sample Selection.............................10
3.1.2 Source of Data and Sample Period.............11
3.1.3 The Definition of Variables..................11
3.2 Summary Statistics.............................13
3.3 Expected Idiosyncratic Skewness Estimation ....18
3.4 Relationship between Expected Idiosyncratic Skewness and Expected Return . . . . . . . . . . . . . . . .21
3.4.1 Portfolios sorted on predicted skewness . . 21
3.4.2 Fama-MacBeth Regression . . . . . . . . . . .23
4 Conclusion.......................................26
References.........................................28
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