The Impact of Nasdaq Index on SSEC: A Highlight on Potential Transmission of Shocks Across International Markets
DOI:
https://doi.org/10.54097/hbem.v20i.12343Keywords:
Nasdaq index; Shanghai Stock Exchange Composite (SSEC) index; Econometric analysis; Financial linkages; International stock markets; Volatility.Abstract
The Nasdaq Index, which represents the performance of major comprehensive stocks in the United States, is a leading indicator for global trends. The SSEC, on the other hand, represents the growing importance of the Chinese economy in the global financial landscape. The study recognizes the increasingly interconnected nature of global financial markets and the significant role played by major stock indices in transmitting financial disturbances across borders. With various econometric techniques to analyze the relationship between these two indices, the results demonstrate a positive correlation between the Nasdaq index and SSEC. Additionally, the findings suggest that the Nasdaq index has a significant influence on the volatility of SSEC. These results indicate the importance of global financial linkages and the potential transmission of shocks across international stock markets. The study contributes to the existing literature by providing empirical evidence on the impact of the Nasdaq index on the SSEC and highlights the implications for investors and policymakers.
Downloads
References
Chen, S., Chiang, T. C., & So, M. K. (2007). Asymmetric Return and Volatility Responses to Composite News from Stock Markets. Multinational Finance Journal, 11, 179-210. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2623464.
Wang, H., Xu, N., Yin, H., & Ji, H. (2022). The dynamic impact of monetary policy on financial stability in China after crises. Pacific-Basin Finance Journal, 75. https://doi.org/10.1016/j.pacfin.2022.101855.
Jiang, F., Kim, K. A. (2020). Corporate Governance in China: A Survey*. Review of Finance, 24(4), 733-772.
Wen, F., Xu, L., Ouyang, G., & Kou, G. (2019). Retail investor attention and stock price crash risk: Evidence from China. International Review of Financial Analysis, 65.
Akbas, F., Armstrong, W. J., Sorescu, S., & Subrahmanyam, A. (2015). Smart money, dumb money, and capital market anomalies. Journal of Financial Economics, 118(2), 355-382.
Jiang, F., Ma, Y., & Shi, B. (2017). Stock liquidity and dividend pasyouts. Journal of Corporate Finance. 42, 285-307.
L, H. and S, T. (2018). Choosing Between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis. Oxf Bull Econ Stat, 80, 715-735.
B, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Lin, Y., Cai, F., & Wu, Y. (2017). Difference-in-differences with multiple time periods. Economics Letters, 171, 295-314.s
Chen, J., Tang, G., Yao, J., & Zhou, G. (2022). Investor Attention and Stock Returns. Journal of Financial and Quantitative Analysis, 57(2), 455-484.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






