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1 
Correlation of returns and volatility among US, Japan, and Asian equity markets.January 2001 (has links)
by Cheung ChanWah. / Thesis (M.B.A.)Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 8086). / ABSTRACT  p.ii / TABLF OF CONTENTS  p.iii / LIST OF TABLES  p.iv / ACKNOWLEDGMENTS  p.v / Chapter / Chapter I  INTRODUCTION l  p.1 / Chapter II.  REVIEW OF LITERATURE  p.7 / Chapter III.  METHODOLOGY。  p.16 / Summary Statistics  p.16 / Correlation  p.21 / GARCH Estimation  p.22 / Chapter IV.  NATIONAL MARKET INDEX AND DATA  p.31 / National Stock Indices and Trading Mechanisms  p.31 / Stock Return Data and Data Transformation  p.34 / Chapter V.  EMPIRICAL RESULTS  p.37 / Summary Statistics  p.37 / CrossCorrelation  p.45 / GARCH Estimation  p.51 / Chapter VI.  SUMMARY AND CONCLUSION  p.75 / BIBLIOGRAPHY  p.80

2 
Asset price determination in the presence of noise traders: a reaction approach.January 2000 (has links)
Lau Yuk Hoi. / Thesis (M.Phil.)Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 109110). / Abstracts in English and Chinese. / Abstract  p.i / Acknowledgement  p.iii / Table of Contents  p.iv / List of Notations  p.vi / List of Propositions  p.vii / List of Figures  p.viii / List of Appendices  p.x / Chapter Chapter 1.  Introduction  The Reaction Approach  p.1 / Chapter Chapter 2.  Assumption for OLG Model  p.7 / Chapter 2.1  Assumption A  p.7 / Chapter Chapter 3.  Equilibrium Conditions Without Fundamental Risk  p.9 / Chapter 3.1  Price as a Weighted Average  p.9 / Chapter 3.2  Determination of A and B  p.11 / Chapter 3.2.1  Assumption B  p.12 / Chapter 3.2.2  RE Line and NE Line  p.13 / Chapter 3.2.3  Equilibrium values of A and B  p.14 / Chapter 3.3  Rational Expectation on Price Variance (RV Line)  p.16 / Chapter 3.4  Noisy Expectation on Price Variance (NV Line)  p.18 / Chapter 3.4.1  DeLong's Model  p.19 / Chapter 3.4.2  Bhushan's Model  p.21 / Chapter 3.5  Change in Relative Perceived Variance  p.23 / Chapter 3.5.1  General Problem of OLG Model in Noisy Trading  p.23 / Chapter 3.5.2  Changes in Noise Traders' Beliefs  p.24 / Chapter 3.5.3  "Relative Perceived Price Variance of n, θ"  p.25 / Chapter 3.5.3.1  "Effect of Increasing θ on Price Variance, dC/dθ"  p.26 / Chapter 3.5.3.2  "Effect of Increasing θ on Expected Price Level, dp/dθ"  p.27 / Chapter Chapter 4.  Equilibrium Conditions With Fundamental Risk  p.31 / Chapter 4.1  Price as a Weighted Average  p.32 / Chapter 4.2  Determination of A and B  p.34 / Chapter 4.2.1  Assumption C  p.34 / Chapter 4.2.2  RE Line and NE Line  p.35 / Chapter 4.2.3  Equilibrium values of A and B  p.36 / Chapter 4.3  Rational Expectation on return Variance (RV Line)  p.37 / Chapter 4.4  Noisy Expectation on Return Variance (NV Line)  p.40 / Chapter 4.4.1  De Long's Model  p.41 / Chapter 4.4.2  Bhushan's Model  p.42 / Chapter 4.5  Change in Relative Perceived Return Variance  p.45 / Chapter 4.5.1  Specification of Noisy Expectation  p.46 / Chapter 4.5.2  Relative Perceived Return Variance of n，Θ  p.46 / Chapter 4.5.2.1  "Effect of Increasing Θ on Price Variance, dC/dΘ"  p.47 / Chapter 4.5.2.2  "Effect of Increasing Θ on Expected Price Level, dp/dΘ"  p.48 / Chapter 4.6  Relative Perceived Price Risk versus Relative Perceived Dividend Risk  p.52 / Chapter Chapter 5.  Conclusion and Discussion  p.55 / Figures  p.58 / Appendices  p.86 / References  p.109

3 
The booktomarket effect and the behaviour of stock returns in the Australian equity marketEmeny, Matthew. January 1998 (has links) (PDF)
"August 1998" Bibliography: leaves 7478. The relationship between the returns to a stock, and ratio of book equity to market equity of the firm, are tested for the Australian stock market, and statistically significant evidence is found in support if the :book to market effect". Several tests are performed to determine whether this return premium is the result of additional risk or market inefficiency. No evidence is found to suggest that high booktomarket stocks are associated with additional risk, and only weak evidence is found to suggest that return premium is a result of investor overreaction. An alternative explanation IS offered, relying on the dynamic behavior of firms and the process by which investors value the stocks of these firms.

4 
Fisher hypothesis, international stock return differentials and inflation differentials.January 2000 (has links)
Wu Haijun. / Thesis (M.Phil.)Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 4548). / Abstracts in English and Chinese. / Abstract  p.ii / Acknowledgement  p.iv / Chapter Chapter 1.  Introduction  p.1 / Chapter Chapter 2.  Literature Review  p.4 / Chapter 2.1.  The Fisher Hypothesis  p.4 / Chapter 2.2.  International Fisher Equation  p.11 / Chapter Chapter 3.  Theoretical Basis on The Link Between Stock Return Differential and Inflation Rate Differential  p.15 / Chapter Chapter 4.  Data Description  p.19 / Chapter Chapter 5.  Results  p.23 / Chapter 5.1.  Does The Generalized Fisher Hypothesis Hold In The Long Horizons  p.24 / Chapter 5.2.  Does International Fisher Equation Hold  p.29 / Chapter 5.3.  Can International Elements Account For The Failure of Fisher Hypothesis  p.36 / Chapter Chapter 6.  Conclusion  p.43 / Bibliography  p.45 / Appendix A  p.49 / Chapter A.1.  The link between interest rate differential and inflation rate differential  p.49 / Chapter A.2.  Instrumental Variable Estimation  p.53 / Appendix B  p.59 / Chapter B.1.  Hong Kong CPI(A) Source  p.59 / Chapter B.2.  Taiwan CPI Source  p.61 / LIST OF TABLES / Table 4.1: Data Description  p.21 / Table 4.2: Means and Standard Deviations of Inflation and Stock Returns  p.22 / Table 5.1: Shortterm (One Year) Test on Fisher Hypothesis on Stock Returns  p.26 / Table 5.2: Longterm (Five Years) Test on Fisher Hypothesis on Stock Returns  p.27 / Table 5.3: Longterm (Ten Years) Test on Fisher Hypothesis on Stock Returns  p.30 / Table 5.4: Shortterm (One Year) Test For International Fisher Equation on Stock Returns  p.33 / Table 5.5: Longterm (Five Years) Test For International Fisher Equation on Stock Returns  p.34 / Table 5.6: Longterm (Ten Years) Test For International Fisher Equation on Stock Returns  p.35 / Table 5.7: Testing Effects of International Elements on The Fisher Hypothesis  p.39 / Table 5.8: Regression Results For The Coefficients of Domestic Inflation With and Without International Elements  p.40

5 
Market size, booktomarket equity and the crosssection of stock returns: an application of the multiplevariable threshold model. / Market size, booktomarket equity & the crosssection of stock returnsJanuary 2006 (has links)
Mak Wing Hei. / Thesis (M.Phil.)Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 5052). / Abstracts in English and Chinese. / ABSTRACT  p.1 / 摘要  p.2 / ACKNOWLEDGEMENTS  p.3 / TABLE OF CONTENTS  p.4 / Chapter CHAPTER 1  INTRODUCTION & LITERATURE REVIEW  p.6 / Chapter CHAPTER 2  DATA DESCRIPTION  p.12 / Chapter 2.1   Coverage and Sources  p.12 / Chapter 2.2   Match Accounting Data with Stock Returns  p.12 / Chapter 2.3   Selection Rule  p.13 / Chapter 2.4   Choice of the Threshold Variables Z  p.14 / Chapter CHAPTER 3  THE MODEL  p.15 / Chapter 3.1   Estimating excess returns & Betas  p.15 / Chapter 3.2  Estimating Threshold Effects  p.17 / Chapter 3.3   Testing the Number of Threshold Variables  p.19 / Chapter 3.4   Estimating Threshold values  p.21 / Chapter CHAPTER 4  PRELIMINARY OBSERVATIONS  p.21 / Chapter 4.1   Excess Returns  p.21 / Chapter 4.2   "Relationship between Beta, Market Size and BooktoMarket Equity"  p.24 / Chapter CHAPTER 5  ESTIMATION RESULTS OF THE THRESHOLD MODEL  p.35 / Chapter 5.1   Number of Threshold Variables  p.35 / Chapter 5.2  Threshold Value Estimates  p.39 / Chapter 5.3  The “and´ح case and “or´حcase  p.40 / Chapter 5.4   Comparison with OLS  p.45 / Chapter CHAPTER 6  CONCLUSION  p.48 / REFERENCES  p.50

6 
Valueatrisk analysis of portfolio return model using independent component analysis and Gaussian mixture model.January 2004 (has links)
Sen Sui. / Thesis submitted in: August 2003. / Thesis (M.Phil.)Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 8892). / Abstracts in English and Chinese. / Abstract  p.ii / Acknowledgement  p.iv / Dedication  p.v / Chapter 1  Introduction  p.1 / Chapter 1.1  Motivation and Objective  p.1 / Chapter 1.2  Contributions  p.4 / Chapter 1.3  Thesis Organization  p.5 / Chapter 2  Background of Risk Management  p.7 / Chapter 2.1  Measuring Return  p.8 / Chapter 2.2  Objectives of Risk Measurement  p.11 / Chapter 2.3  Simple Statistics for Measurement of Risk  p.15 / Chapter 2.4  Methods for ValueatRisk Measurement  p.16 / Chapter 2.5  Conditional VaR  p.18 / Chapter 2.6  Portfolio VaR Methods  p.18 / Chapter 2.7  Coherent Risk Measure  p.20 / Chapter 2.8  Summary  p.22 / Chapter 3  Selection of Independent Factors for VaR Computation  p.23 / Chapter 3.1  Mixture Convolution Approach Restated  p.24 / Chapter 3.2  Procedure for Selection and Evaluation  p.26 / Chapter 3.2.1  Data Preparation  p.26 / Chapter 3.2.2  ICA Using JADE  p.27 / Chapter 3.2.3  Factor Statistics  p.28 / Chapter 3.2.4  Factor Selection  p.29 / Chapter 3.2.5  Reconstruction and VaR Computation  p.30 / Chapter 3.3  Result and Comparison  p.30 / Chapter 3.4  Problem of Using Kurtosis and Skewness  p.40 / Chapter 3.5  Summary  p.43 / Chapter 4  Mixture of Gaussians and ValueatRisk Computation  p.45 / Chapter 4.1  Complexity of VaR Computation  p.45 / Chapter 4.1.1  Factor Selection Criteria and Convolution Complexity  p.46 / Chapter 4.1.2  Sensitivity of VaR Estimation to Gaussian Components  p.47 / Chapter 4.2  Gaussian Mixture Model  p.52 / Chapter 4.2.1  Concept and Justification  p.52 / Chapter 4.2.2  Formulation and Method  p.53 / Chapter 4.2.3  Result and Evaluation of Fitness  p.55 / Chapter 4.2.4  Evaluation of Fitness using ZTransform  p.56 / Chapter 4.2.5  Evaluation of Fitness using VaR  p.58 / Chapter 4.3  VaR Estimation using Convoluted Mixtures  p.60 / Chapter 4.3.1  Portfolio Returns by Convolution  p.61 / Chapter 4.3.2  VaR Estimation of Portfolio Returns  p.64 / Chapter 4.3.3  Result and Analysis  p.64 / Chapter 4.4  Summary  p.68 / Chapter 5  VaR for Portfolio Optimization and Management  p.69 / Chapter 5.1  Review of Concepts and Methods  p.69 / Chapter 5.2  Portfolio Optimization Using VaR  p.72 / Chapter 5.3  Contribution of the VaR by ICA/GMM  p.76 / Chapter 5.4  Summary  p.79 / Chapter 6  Conclusion  p.80 / Chapter 6.1  Future Work  p.82 / Chapter A  Independent Component Analysis  p.83 / Chapter B  Gaussian Mixture Model  p.85 / Bibliography  p.88

7 
Improved estimation of Markowitz efficient portfolios.January 2008 (has links)
Ng, Hon Yip. / Thesis (M.Phil.)Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 7983). / Abstracts in English and Chinese. / Chapter 1  Introduction  p.1 / Chapter 2  Basic Concepts in Portfolio Theory  p.8 / Chapter 2.1  Statistical Model  p.8 / Chapter 2.2  MeanVariance Optimization  p.9 / Chapter 2.3  The Efficient Frontier  p.11 / Chapter 2.4  The Tangency Portfolio and The Capital Market Line  p.13 / Chapter 2.5  Mathematical Formulation of Portfolio Optimization  p.17 / Chapter 3  Derivation of The Improved Estimator  p.29 / Chapter 4  Simulation Study  p.40 / Chapter 4.1  Procedure of Simulation  p.40 / Chapter 4.2  Simulation Results  p.46 / Chapter 4.2.1  Zero Correlation  p.47 / Chapter 4.2.2  Positive Correlations  p.50 / Chapter 4.2.3  Negative Correlations  p.52 / Chapter 5  Conclusion and Future Direction  p.56 / Chapter A  Simulation results for p = 200  p.58 / Chapter B  Simulation results for p = 400  p.61 / Chapter C  Simulation results for p = 500  p.64 / Chapter D  Simulation results for p = 200 with negative correlations  p.67 / Chapter E  Simulation results for p = 400 with negative correlations  p.71 / Chapter F  Simulation results for p = 500 with negative correlations  p.75 / Bibliography  p.79

8 
Three essays on noise and institutional tradingLuo, Yan, 罗妍 January 2010 (has links)
published_or_final_version / Business / Doctoral / Doctor of Philosophy

9 
Stock return, trading volume, and volatility: an empirical study of Hong Kong.January 1998 (has links)
by Sze Kin Wan. / Thesis (M.Phil.)Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 6975). / Abstract also in Chinese. / ACKNOWLEDGMENTS  p.iii / LIST OF TABLES  p.iv / LIST OF ILLUSTRATIONS  p.v / CHAPTER / Chapter ONE  INTRODUCTION  p.1 / Chapter TWO  REVIEW OF THE LITERATURE  p.7 / Stock Returns and Trading Volume / Volatility / Chapter THREE  ECONOMETRIC ANALYSIS  p.16 / Unit Root Tests / Lag Length Tests / Causality Detection between Two Series / ARCH Modelling / Chapter FOUR  DATA AND ESTIMATION RESULTS  p.34 / Data / Unit Root Test / Optimal Lag Length / Causality Detection / GARCH Modelling / Chapter FIVE  CONCLUSION  p.62 / APPENDIX  p.67 / BIBLIOGRAPHY  p.69 / ILLUSTRATIONS  p.76

10 
Investigation of an errorcorrection model for trade and quote prices. / 一個買入和賣出價的誤差修正模型之調查 / Yi ge mai ru he mai chu jia de wu cha xiu zheng mo xing zhi diao chaJanuary 2010 (has links)
Wong, Kin Lung Keith. / Thesis (M.Phil.)Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 127131). / Abstracts in English and Chinese. / Abstract  p.i / Thesis/Assessment Committee  p.iii / Acknowledgement  p.iv / Chapter 1  Introduction  p.1 / Chapter 2  Background Studies  p.5 / Chapter 2.1  Ultrahigh Frequency Data Handling with Database Server  p.5 / Chapter 2.1.1  Use of Database Server  p.5 / Chapter 2.2  Ultrahigh Frequency Data Treatments  p.7 / Chapter 2.2.1  Cleaning of Data  p.7 / Chapter 2.2.2  Matching of a Trade and Its Standing Quote  p.13 / Chapter 2.3  Tickbytick Price Modeling  p.15 / Chapter 2.3.1  Multivariate Linear Models  p.15 / Chapter 2.3.2  Duration and Volume Handling  p.16 / Chapter 2.3.3  VAR Model Selection Techniques  p.20 / Chapter 2.3.4  Seasonality Handling  p.24 / Chapter 3  Problem Definition and Framework  p.27 / Chapter 3.1  Engle and Patton's Model  p.27 / Chapter 3.2  Preparation of data  p.31 / Chapter 3.3  Methods to Estimate Diurnal Adjustment Param eters  p.38 / Chapter 3.4  Transformation of the Model to Fit in VARX soft wares  p.40 / Chapter 3.5  Modification of the Model  p.47 / Chapter 3.6  Estimating and Forecasting the Exogenous Vari ables  p.52 / Chapter 3.6.1  Modelling BUYt and SELLt  p.52 / Chapter 3.6.2  Modelling DURt and VOLt  p.53 / Chapter 3.6.3  Modelling k(t)  p.56 / Chapter 3.6.4  Forecasting the Cross Terms and the Sum of Buys and Sells  p.62 / Chapter 3.7  Forecasting with the Main Model  p.64 / Chapter 4  Experimental Evaluation  p.67 / Chapter 5  Conclusion  p.73 / Chapter A  Source and Data Information  p.76 / Chapter B  Model Estimation Results for (3.13)  p.80 / Chapter C  Model Forecasting Results for (3.13) and (3.2)  p.102 / Bibliography  p.127

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