阎可佳著的《中国与金砖四国及美国和澳大利亚股票市场之间相关性分析(英文版)》探索中国的股票市场与巴西、印度、俄罗斯、南非等其它金砖四国、以及与澳大利亚和美国股票市场之间的相关关系。回答两个主要问题：第一，美国次贷危机对于中国的股票市场与其它六国股票市场之间的Granger因果关系、动态相关关系、以及尾部依赖关系到底有什么影响？第二，使用DCC-GARCH模型估计的动态相关系数与使用copula模型估计的动态相关系数之间是否具有显著的差异？

Mr．Yan Kejia is a Doctor ofPhilosophy in Business ResearchCandidate of Griffith University．Heholds a Master of Applied Finance fromQueensland University of Technology，aGraduate Diploma of Research Studiesin BUSiness and a BachelOr ofCommerce in Accounting from GriffithUniversity．He has been awardedGriffith University Award for AcademicExcellence 2014-2015 and ResearchSchOlarships of GUIPRS and GUPRS2016-2018．His research focuses on therelationship between the internationalstock markets，D0rtfoli0 retUrnforecasting，and green finance．

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——Quantile 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