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研究生:張台偉
研究生(外文):Tai-Wei Zhang
論文名稱:ThreeEssaysontheRelationshipbetweenStructuralBreaksandAssetBehavior
論文名稱(外文):Three Essays on the Relationship between Structural Breaks and Asset Behavior
指導教授:陳安行陳安行引用關係
學位類別:博士
校院名稱:國立中正大學
系所名稱:財務金融所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:62
中文關鍵詞:structural breaksstock returnsasset pricing
外文關鍵詞:asset pricingstructural breaksstock returns
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This thesis includes three independent essays. The first is to investigate whether Beta regime change risk is a systematic risk priced in subsequent security returns. The second essay in this thesis is to investigate whether dividend yield (DY) can predict aggregate stock returns while controlling for the effects of structural breaks and bias induced by autocorrelation in the predictor variable. The third essay in this thesis is to investigate whether DY can predict aggregate stock returns while controlling for the effects of time-varying regression coefficients and bias induced by autocorrelation in the predictor variable.

The first essay called “The Beta Regime Change Risk Premium”. This paper proposes a simple idea that portfolio’s beta estimation uncertainty is also a “risk” for risk averse investors. The number of statistically detectable Beta regime changes a portfolio experienced in the past is a natural proxy for ex ante “Beta regime change risk” of the portfolio. This study applies newly developed statistical tests of multiple structural breaks to investigate whether Beta regime change risk is a systematic risk priced in subsequent security returns. Our results show Beta regime change risk is rewarded by higher returns. In addition, we find evidence showing that the explanatory power of both size and BE/ME are subsumed by our proxy for Beta regime change. Portfolios with high Beta regime change risk earn substantially higher than average returns. Additional tests confirm that a risk-based explanation can account for the results.

The second essay called “Autocorrelation, Structural Breaks, and the Predictive Ability of Dividend Yield”. In this study, we investigate whether dividend yield (DY) can predict aggregate stock returns while controlling for the effects of structural breaks and bias induced by autocorrelation in the predictor variable. To do so we apply the Bai and Perron (1998, 2003 (BP)) methodology to test for structural breaks and the bias-adjusted predictability test of Lewellen (2004). We show that although DY predicts market returns during the period 1946-2000, there exist “natural” sub-samples bounded by statistically detectable structural breaks that can last for long periods of time (up to 10 years in duration) when DY does not show significant forecasting power. This has important implications in that even if in the long run DY actually provides strong predictive ability, investors should be mentally prepared for long dry spells of unpredictability with respect to DY.

The last part of this thesis called “Autocorrelation, Smooth-Time-Varying, and the Predictive Ability of Dividend Yield”. The main difference from the previous chapter is that we do not have to assume that parameters change abruptly. This allows for a time-varying relationship between stock return and dividend yield, and is therefore more robust to possible model misspecification. Hence, this methodology should be more in line with the general equilibrium framework of Menzly, Santos, and Veronesi (2004). We find strong evidence that DY do not predict VWNY and Excess VWNY. Our results have important implications for investors. The predictive ability of DY turns out to be an illusion when we control for the effect of time-varying coefficients and bias induced by autocorrelation in the predictor variable.
This thesis includes three independent essays. The first is to investigate whether Beta regime change risk is a systematic risk priced in subsequent security returns. The second essay in this thesis is to investigate whether dividend yield (DY) can predict aggregate stock returns while controlling for the effects of structural breaks and bias induced by autocorrelation in the predictor variable. The third essay in this thesis is to investigate whether DY can predict aggregate stock returns while controlling for the effects of time-varying regression coefficients and bias induced by autocorrelation in the predictor variable.

The first essay called “The Beta Regime Change Risk Premium”. This paper proposes a simple idea that portfolio’s beta estimation uncertainty is also a “risk” for risk averse investors. The number of statistically detectable Beta regime changes a portfolio experienced in the past is a natural proxy for ex ante “Beta regime change risk” of the portfolio. This study applies newly developed statistical tests of multiple structural breaks to investigate whether Beta regime change risk is a systematic risk priced in subsequent security returns. Our results show Beta regime change risk is rewarded by higher returns. In addition, we find evidence showing that the explanatory power of both size and BE/ME are subsumed by our proxy for Beta regime change. Portfolios with high Beta regime change risk earn substantially higher than average returns. Additional tests confirm that a risk-based explanation can account for the results.

The second essay called “Autocorrelation, Structural Breaks, and the Predictive Ability of Dividend Yield”. In this study, we investigate whether dividend yield (DY) can predict aggregate stock returns while controlling for the effects of structural breaks and bias induced by autocorrelation in the predictor variable. To do so we apply the Bai and Perron (1998, 2003 (BP)) methodology to test for structural breaks and the bias-adjusted predictability test of Lewellen (2004). We show that although DY predicts market returns during the period 1946-2000, there exist “natural” sub-samples bounded by statistically detectable structural breaks that can last for long periods of time (up to 10 years in duration) when DY does not show significant forecasting power. This has important implications in that even if in the long run DY actually provides strong predictive ability, investors should be mentally prepared for long dry spells of unpredictability with respect to DY.

The last part of this thesis called “Autocorrelation, Smooth-Time-Varying, and the Predictive Ability of Dividend Yield”. The main difference from the previous chapter is that we do not have to assume that parameters change abruptly. This allows for a time-varying relationship between stock return and dividend yield, and is therefore more robust to possible model misspecification. Hence, this methodology should be more in line with the general equilibrium framework of Menzly, Santos, and Veronesi (2004). We find strong evidence that DY do not predict VWNY and Excess VWNY. Our results have important implications for investors. The predictive ability of DY turns out to be an illusion when we control for the effect of time-varying coefficients and bias induced by autocorrelation in the predictor variable.
Essay1: The Beta Regime Change Risk Premium
Abstract………………………………………………………………………….. 1
I. Introduction……………………………………………………………………. 2
II. Related Literature……………………………………………………………. 4
III. Research Design, Sample Selection, Empirical Tests………………… 6
IV. Results……………………………………………………………………… 7
V. Conclusion…………………………………………………………………….. 11

References……………………………………………………………………….. 12
Technical Appendix……………………………………………………………….. 14

Essay2: Autocorrelation, Structural Breaks, and the Predictive Ability of Dividend Yield
Abstract…………………………………………………………………………. 24
I. Introduction…………………………………………………………………… 25
II. Methodology…………………………………………………………………. 26
III. Data, Break Test Results and Descriptive Statistics…………… 29
IV. Empirical Results…………………………………………………………. 30
V. Summary and Conclusion……………………………………………….…….. 31

References……………………………………………………………………….. 33
Technical Appendix……………………………………………………………….. 34

Essay3: Autocorrelation, Smooth–Time-Varying, and the Predictive Ability of Dividend Yield
Abstract……………………………………………………………………….... 41
I. Introduction………………………………………………………………..... 42
II. Methodology……………………………………………………………….. 44
III. Data and Descriptive Statistics……………..……………………………50
III. Empirical Results……………………………………………………………. 50
IV. Summary and Conclusion.……………………………………………………. 53

References……………………………………………………………………….... 54
Essay1: The Beta Regime Change Risk Premium
Abstract………………………………………………………………………….. 1
I. Introduction……………………………………………………………………. 2
II. Related Literature……………………………………………………………. 4
III. Research Design, Sample Selection, Empirical Tests………………… 6
IV. Results……………………………………………………………………… 7
V. Conclusion…………………………………………………………………….. 11

References……………………………………………………………………….. 12
Technical Appendix……………………………………………………………….. 14

Essay2: Autocorrelation, Structural Breaks, and the Predictive Ability of Dividend Yield
Abstract…………………………………………………………………………. 24
I. Introduction…………………………………………………………………… 25
II. Methodology…………………………………………………………………. 26
III. Data, Break Test Results and Descriptive Statistics…………… 29
IV. Empirical Results…………………………………………………………. 30
V. Summary and Conclusion……………………………………………….…….. 31

References……………………………………………………………………….. 33
Technical Appendix……………………………………………………………….. 34

Essay3: Autocorrelation, Smooth–Time-Varying, and the Predictive Ability of Dividend Yield
Abstract……………………………………………………………………….... 41
I. Introduction………………………………………………………………..... 42
II. Methodology……………………………………………………………….. 44
III. Data and Descriptive Statistics……………..……………………………50
III. Empirical Results……………………………………………………………. 50
IV. Summary and Conclusion.……………………………………………………. 53

References……………………………………………………………………….... 54
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