Description

Book Synopsis
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.

Table of Contents
  • 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
  • References
  • Appendix
  • 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

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

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        View other formats and editions of An Analysis of the Relationship between the Stock Markets of China and other BRICS Countries, the United States and Australia by Yan Kejia

        Publisher: Paths International Ltd
        Publication Date: 30/03/2019
        ISBN13: 9781844645343, 978-1844645343
        ISBN10:
        Also in:
        Business, Finance & Law International business Investment and securities

        Description

        Book Synopsis
        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.

        Table of Contents
        • 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
        • References
        • Appendix
        • 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

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