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研究生:阮氏橫兒
研究生(外文):Nguyen Thi Hang Nga
論文名稱:變異數異質性之不對稱資本資產定價模式研究
論文名稱(外文):A Study on an Asymmetric Capital Asset Pricing Model with Heteroskedasticity
指導教授:陳婉淑
口試委員:李木易陳麗君
口試日期:2014-05-31
學位類別:碩士
校院名稱:逢甲大學
系所名稱:國際經營管理碩士學位學程
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:54
中文關鍵詞:分量迴歸分析廣義自我相關條件異質變異數模型資本資產定價模型時間變動型市場風險係數非對稱現象
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在探討股票投資組合報酬跟市場報酬之間的關係時,資本資產定價模型是個很重要的基準。然而,在資本資產定價模型中,固定的市場風險係數過於簡化實際狀況,因此,本篇論文的目的在於提出更一般化且會隨時間而改變的市場風險係數,得以合理得估計資產或投資組合的價值。為達此目的,本論文嘗試採用非對稱異質變異數資本資產定價模型。此外,為研究在不同分量下,市場風險的不同表現,分量迴歸分析也被一併考慮。16檔道瓊工業指數股票被選作實證分析。強烈證據顯示,市場消息對股票報酬有非對稱性的影響。此結果證實壞消息會使股票報酬下降;反之,好消息會使股票報酬上升。而且,壞消息對股價的影響遠超過好消息。分量迴歸分析顯示市場風險係數會隨不同股票超額報酬分量而有所變動。
Capital Asset Pricing Model (CAPM) is an important benchmark for studying the relationship between market returns and portfolio returns. The constant beta coefficient found in the CAPM is over-simplified, and therefore proposing a more general time-varying model that focuses on valuing risky securities and market portfolios is the motivation of this thesis. This thesis intends to model a time-varying market beta coefficient in CAPM by employing an asymmetric CAPM with heteroskedasticity. The quantile regression is also considered to explore the different behaviour in the market beta coefficients under a range of quantile levels. The proposed model is illustrated by the daily excess returns of sixteen stocks from the Dow Jones Industrial Average. There is strong evidence that suggests that news may have an asymmetric effect on the stock excess returns. This confirms that negative news result downward revison on stock returns and positive news result upward revision on stock returns, however, the magnitude of negative news is generally more severe. Quantile regression indicates that the market beta coefficients have distinct performance over different quantile levels of the stock excess returns.
Acknowledgements i
Abstract ii
Table of Contents iii
List of Figures iv
List of Tables v
CHAPTER I. INTRODUCTION 1
CHAPTER II. MOTIVATION AND THE ASYMMETRIC CAPM 6
2.1. ARCH and GARCH Models 6
2.2. Quantile regression 8
2.3. The asymmetric CAPM under different quantile levels 9
CHAPTER III. STATISTICAL METHODS 11
3.1. The Kolmogorov-Smirnov Test 11
3.2. Assumption for error terms 11
3.3. Diagnostic checking 12
CHAPTER IV. DATA ANALYSIS 14
CHAPTER V. CONCLUSION 43
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