An Analysis of the Relationship between the Stock Markets of China and other BRICS Countries, the United States and Australia



Author: Yan Kejia

ISBN: 978-1-84464-534-3

Ebook ISBN: 978-1-84464-535-0

Hardback-164 Pages

December 2018


£95 €118 $152





This book presents an integrating theoretical and empirical study of the co-movement and time-varying correlations between the stock markets of China and other BRICS countries including Brazil, India, Russia and South Africa, the United States and Australia. It is motivated by the global integration of capital markets and the resultant decline in the potential benefits of diversification. This book fills the gap in the research regarding this issue, as prior research has investigated the benefits of diversification for investors from developed markets. Topics include Capital markets, Globalization, China, BRICS countries.



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 model
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 50
3.7 Market background - China and India
3.8 Conclusion
Chapter 4 Methodology
4.1 Static correlation
4.2 Dynamic correlation of the DCC GARCH model
4.3 Dynamic correlation of 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 difference between two correlations
Chapter 5 Data and preliminary data analysis
5.1 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 the VAR model
6.4.2 China and Australia - Granger causality test and VAR model
6.4.3 China and Brazil - Granger causality test 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 China and Brazil - DCC GARCH model
6.6.4 China and Russian - DCC 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 1. Quantile - Quantile distribution curves
Appendix 2. Probability of Ljung - Box autocorrelation test
Appendix 3. The 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
Appendix 8. Copula Dynamic Correlation


About Author:

Yan Kejia, Doctor of Philosophy in Business Research Candidate of Griffith University. He holds a Master of Applied Finance from Queensland University of Technology, a Graduate Diploma of Research Studies in Business and a Bachelor of Commerce in Accounting from Griffith University. He has been awarded Griffith University Award for Academic Excellence 2014-2015 and Research Scholarships of GUIPRS and GUPRS 2016-2018. His research focuses on the relationship between the international stock markets, portfolio return forecasting, and the relationship between economic growth and environmental pollution.

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