ÖйúÓë½ðשËĹú¼°ÃÀ¹úºÍ°Ä´óÀûÑǹÉƱÊг¡Ö®¼äÏà¹ØÐÔ·ÖÎö(Ó¢ÎÄ°æ)

Chapter 1 Introduction 1.1 Motivation and Background 1.2 Research Question and Data 1.3 Research Contribution and Implication 1.4 Research Findings and Weakness Chapter 2 Literature Review 2.1 Diversification Benefit 2.2 Correlation 2.3 Dynamic Correlation-DCC GARCH ModeI 2.3.1 The Development of the DCC GARCH Model 2.4 Dynamic Correlation-Copula Model 2.4.1 The Development of the Copula Model 2.4.2 Upper and Lower Tail Dependence 2.5 Conclusion Chapter 3 Stock Market Background 3.1 US Subprime Crisis 3.2 Market Background-China and the US 3.3 Market Background-China and Australia 3.4 Market Background-China and Brazil 3.5 Market Background-China and Russia 3.6 Market Background-China and South Africa 3.7 Market Background-China and India 3.8 Conclllsion Chapter 4 Methodology 4.1 Static Correlation 4.2 Dynamic Correlation o f the DCC GARCH Model 4.3 Dynamic Correlation o f the Copula Model 4.3.1 Time Varying Gaussian Copula 4.3.2 Time Varying Student's t Copula 4.4 Tail Dependence 4.5 Significance of the Diffcrcnce bctwccn Two Corrclations Chapter 5 Data and Preliminary Data Analysis 5.1 Data 5.2 A Preliminary Statistical Analysis of the Data Chapter 6 Empirical Study 6.1 Autocorrelation Result 6.2 Unit Root Test 6.3 Cointegration Test 6.4 Granger Causality Test and VAR Model 6.4.1 China and the US-Granger Causality Test and VAR Model 6.4.2 China and Australia-Granger Causality Test and VAR Model 6.4.3 China and Brazil-Granger Causality Tcst and VAR Model 6.4.4 China and Russia-Granger Causality Test and VAR Model 6.4.5 China and South Africa-Granger Causality Test and VAR Model 6.4.6 China and India-Granger Causality Test and VAR Model 6.4.7 Summary of Results 6.5 Static Correlation 6.6 Dynamic Correlation-DCC GARCH Model 6.6.1 China and the US-DCC GARCH Model 6.6.2 China and Australia-DCC GARCH Model 6.6.3 Chjna and Brazjl-DCC GARCH Model 6.6.4 China and Russian-IX2C GARCH Model 6.6.5 China and South Africa-DCC GARCH Model 6.6.6 China and India-DCC GARCH Model 6.7 Tail Dependence 6.8 Dynamic Correlation-Copula Model 6.8.1 Dynamic Correlation Results of Gaussian Copula and Student's t Copula 6.8.2 Significance of the Difference-Gaussian and Student's t Copula Model 6.9 Correlation Comparison Chapter 7 Conclusion 7.1 Empirical Findings 7.2 Limitation of the Study. 7.3 Future Research Appendix Appendix 1 Quantile¡ª¡ªQuantile Distribution Curves Appendix 2 Probability of Lj ung-Box Autocorrelation Tcst Appendix 3 Autocorrelation Regression Equations of AR(1)and GARCH Equation Appendix 4 DCC Parameters and Equations Appendix 5 Gaussian and Student's t Copula Parameters of 2-Step MLE Appendix 6 Dynamic Correlation under the Gaussian and Student's t Copula Appendix 7 Dynamic Spearman Rho and Kendall Tall Appendix 8 Copula Dynamic Correlation References Acknowledgements