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Long run diversification potential in Asian stock markets: a test of cointegration.January 1997 (has links)
by Lam Cham. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 75-79). / ACKNOWLEDGMENTS --- p.i / ABSTRACT --- p.ii / LIST OF TABLES --- p.iii / LIST OF FIGURES --- p.iv / Chapter CHAPTER 1: --- INTRODUCTION --- p.1 / Chapter CHAPTER 2: --- HISTORICAL BACKGROUND --- p.8 / Chapter 2.1 --- Financial Liberalization in Nine Asian Countries --- p.8 / Chapter 2.1.1 --- Hong Kong --- p.8 / Chapter 2.1.2 --- Korea --- p.12 / Chapter 2.1.3 --- "Indonesia, Malaysia, Singapore and Thailand - the ASEAN-4" --- p.15 / Chapter 2.1.4 --- Taiwan --- p.18 / Chapter 2.1.5 --- Japan --- p.19 / Chapter 2.1.6 --- The Philippines --- p.20 / Chapter 2.2 --- Stock Market Trend --- p.21 / Chapter CHAPTER 3: --- LITERATURE REVIEW --- p.28 / Chapter 3.1 --- Gain from International Diversification --- p.28 / Chapter 3.2 --- International Transmission Effects --- p.30 / Chapter 3.3 --- Integration of World Stock Markets --- p.31 / Chapter CHAPTER 4: --- METHODOLOGY --- p.38 / Chapter 4.1 --- Cointegration and Diversification --- p.38 / Chapter 4.2 --- Testing for Cointegration --- p.45 / Chapter CHAPTER 5: --- DATA --- p.50 / Chapter 5.1 --- MSCI Index --- p.50 / Chapter 5.2 --- Asian Funds --- p.51 / Chapter CHAPTER 6: --- EMPIRICAL RESULTS --- p.52 / Chapter 6.1 --- Unit Root Test --- p.52 / Chapter 6.1.1 --- ADF and Phillips-Perron Unit Root Test --- p.52 / Chapter 6.1.2 --- Unit Root Test with Structural Break --- p.55 / Chapter 6.2 --- Cointegration Test on Stock Markets --- p.57 / Chapter 6.2.1 --- Regional Factor Vs World Factor --- p.57 / Chapter 6.2.2 --- Integration of the Asian Markets --- p.61 / Chapter 6.3 --- Cointegration Test on the Asian Funds --- p.63 / Chapter 6.3.1 --- Weekly Results --- p.65 / Chapter 6.3.2 --- Monthly Results --- p.66 / Chapter CHAPTER 7: --- CONCLUSIONS --- p.72 / REFERENCES --- p.75
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Hybrid VAR, neural network, and evolutionary computation for predicting Asian Pacific market lead-lag dynamics.January 2003 (has links)
by Ao, Sio Iong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.2 / Chapter 1.2 --- Topics of this Study --- p.3 / Chapter 1.3 --- Econometric Analysis --- p.3 / Chapter 1.4 --- Computational Intelligence --- p.4 / Chapter 1.4.1 --- Overview --- p.4 / Chapter 1.4.2 --- Successful Cases of Applying CI in Time Series Analysis --- p.4 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Market Descriptions --- p.6 / Chapter 2.1.1 --- Overview of the Markets --- p.6 / Chapter 2.2 --- VAR method --- p.10 / Chapter 2.2.1 --- Introduction --- p.11 / Chapter 2.2.2 --- Implementation of VAR by RATS --- p.12 / Chapter 2.2.3 --- Impulse Response Functions --- p.12 / Chapter 2.3 --- Neural Network --- p.14 / Chapter 2.3.1 --- Introduction --- p.14 / Chapter 2.3.2 --- Supervised vs Unsupervised learning --- p.15 / Chapter 2.3.3 --- Back-Propagation network --- p.15 / Chapter 2.4 --- Evolutionary Computation --- p.19 / Chapter 2.4.1 --- Motivation of Employing Evolutionary Computation --- p.19 / Chapter 2.4.2 --- Brief Description --- p.21 / Chapter 2.4.3 --- Genetic Algorithm --- p.21 / Chapter 3 --- Analysis of their Interdependence and SD --- p.23 / Chapter 3.1 --- Interdependence of the Asian Indices --- p.23 / Chapter 3.2 --- Forecasting Index Price with the Help of Neural Network --- p.26 / Chapter 3.3 --- Interdependence of the Standard Deviations of the Stock Indices --- p.28 / Chapter 3.4 --- Using the Neural Network to Make Forecasting of the Stan- dard Deviations --- p.29 / Chapter 3.5 --- Summary --- p.33 / Chapter 4 --- Forecasting Opening Prices --- p.34 / Chapter 4.1 --- Step 1: Identificating of the Interdependence of the Opening Price on Different Stock Indices by VAR --- p.36 / Chapter 4.2 --- Step 2: Using the Neural Network to Make Forecasting of the Opening Prices --- p.38 / Chapter 4.3 --- Summary --- p.39 / Chapter 5 --- Incorporating Correlated Markets --- p.41 / Chapter 5.1 --- Overview of the Markets from the Prespectives of VAR --- p.43 / Chapter 5.2 --- Investigation of the Correlations by VAR Method --- p.43 / Chapter 5.3 --- Prediction of the Market by Neural Network --- p.46 / Chapter 5.4 --- Hypothesis: the Correlations of the Markets Are Time-Dependent --- p.46 / Chapter 5.5 --- Testing this Hypothesis with Predictions by Neural Network . --- p.48 / Chapter 5.6 --- Summary --- p.51 / Chapter 5.7 --- F-tests Results on Different Periods of HK Markets --- p.51 / Chapter 6 --- Hybrid VAR-NN-EC System --- p.53 / Chapter 6.1 --- Introduction --- p.53 / Chapter 6.1.1 --- Overview of the Econometric Analysis of the Lead-Lag Relationship of Stock Markets --- p.54 / Chapter 6.1.2 --- Previous Results of Employing the Stand-alone Neural Network --- p.55 / Chapter 6.2 --- Working Mechanism of the Hybrid VAR-NN-EC --- p.56 / Chapter 6.3 --- Comparing Results from the VAR-NN-EC System --- p.58 / Chapter 6.4 --- Summary --- p.60 / Chapter 7 --- Hybrid System for Dual-Listing Indices --- p.61 / Chapter 7.1 --- Introduction --- p.61 / Chapter 7.2 --- HSI vs HSLRI --- p.62 / Chapter 7.2.1 --- HSI's Selection Criteria --- p.62 / Chapter 7.2.2 --- Hang Seng London Reference Index --- p.63 / Chapter 7.2.3 --- Motivation for the Study --- p.63 / Chapter 7.3 --- Data Descriptions --- p.64 / Chapter 7.4 --- Overviews of this Analysis System --- p.64 / Chapter 7.5 --- Results from the Simplified AR-NN System --- p.65 / Chapter 7.5.1 --- Regression Results --- p.66 / Chapter 7.5.2 --- NN Results --- p.67 / Chapter 7.6 --- Summary --- p.68 / Chapter 8 --- Using EC for Selecting Stock Experts --- p.70 / Chapter 8.1 --- Example of Evolutionary Computation --- p.71 / Chapter 8.2 --- Comparison of Results from the VAR-NN-EC System --- p.72 / Chapter 8.3 --- Summary --- p.73 / Chapter 9 --- Conclusion --- p.74 / Bibliography --- p.i
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