EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 403 (2023) E3S Web Conf., 403 (2023) 08013 References
Open Access
Issue
E3S Web Conf.
Volume 403, 2023
XII International Scientific and Practical Forum “Environmentally Sustainable Cities and Settlements: Problems and Solutions” (ESCP-2023)
Article Number 08013
Number of page(s) 6
Section Development of Sustainable Cities: Economic, Social and Humanitarian Aspects
DOI https://doi.org/10.1051/e3sconf/202340308013
Published online 25 July 2023
  1. Shu, W., Baicheng, Z., & Tianshu, G. (2023). Speculation or actual demand? The return spillover effect between stock and commodity markets Journal of Commodity Markets 29. https://doi.org/10.1016jjcomm.2022.100308 [Google Scholar]
  2. Bouri, E., Lei, X., Jalkh, N., Xu, Y., Zhang, H., (2021). Spillovers in higher moments and jumps across US stock and strategic commodity markets. Resources Policy, 72, https://doi.org/10.10164.resourpol.2021.102060. [CrossRef] [Google Scholar]
  3. Wen, X.Q., Bouri, E., Cheng, H., (2019). The crude oil-stock market dependence and its determinants: evidence from emerging economies. Emerging. Markets. Finance Trade 25, 225–245. https://doi.org/10.1080/1540496X.2018.1522247. [Google Scholar]
  4. Dutta, A., Bouri, E., Noor, M.H., (2021). Climate bond, stock, gold, and oil markets: dynamic correlations and hedging analyses during the COVID-19 outbreak. Resources Policy, 74, https://doi.org/10.1016/j.resourpol.2021.102265. [CrossRef] [PubMed] [Google Scholar]
  5. Peng, L., (2014). An Empirical Study on the International Linkage of China’s Stock Market under the Open Background. Journal of Finance, vol. 3, pp.131–134. [Google Scholar]
  6. Cui, J., Zou, H., (2020). Research on Risk Spillover Effects of Higher Moments Between International Stock Markets from a Time-Frequency Perspective. Financial Markets, vol. 6, pp.5–85. [Google Scholar]
  7. Grobys, K., (2015). Are Volatility Spillovers between Currency and Equity Market Driven by Economic States? Evidence from the US Economy. Economic Letters, vol. 12, pp. 72–75. [CrossRef] [Google Scholar]
  8. Bubák, V., Kocenda, E., Zikes, F. Volatility transmission in emerging European foreign exchange markets. Journal of Banking & Finance, 2011, vol. 35, iss. 11, pp. 2829–2841. [CrossRef] [Google Scholar]
  9. Shi, Z., Xu, Z., Chen, R., (2016). An Empirical Analysis of the Transmission Relationship between China’s Stock Market and Commodity Futures Market——A Study Based on Risk Granger Causality Test. Securities Market, vol. 2, pp. 82–89. [Google Scholar]
  10. Liu, Y., Ju, Z., Liu, Y., (2017). Research on the Financialization of Commodities in China Based on DCC-GARCH. International Business Research, vol. 5, pp. 75–83. [Google Scholar]
  11. Hu, C., Liu, X., (2017). Research on the linkage between commodity and stock market: Based on the perspective of financing liquidity. Financial Research, 2017, vol. 7, pp. 123–139. [Google Scholar]
  12. Hu, J., (2017). Empirical Research on Information Spillover Effects between Domestic and Foreign Commodity Markets-Based on DAG Method and Spillover Index Model. Industrial Technology Economics, vol. 12, pp. 76–82. [Google Scholar]
  13. Li, Z., Su, Y., (2020). Dynamic Spillovers Between International Crude Oil Market and China’s Commodity Sectors: Evidence From Time-Frequency Perspective of Stochastic Volatility. Frontiers in Energy Research, vol. 8(45), pp. 1–17. DOI: 10.3389/fenrg.2020.00045 [CrossRef] [Google Scholar]
  14. Tan, X., Zhang, J., Zheng, X., (2018). The Two-way Spillover Effect of International Commodity Market and Financial Market—An Empirical Study Based on BEKK-GARCH Model and Spillover Index Method. Science and Technology, vol. 8 pp. 31–48. [Google Scholar]
  15. Xinquan, Z., Xiaohua, M., (2018). Research on the Extreme Risk Spillover Effect of International Commodities and China’s Stock Market. Caijing Zongheng, V. 4 pp 164–167. [Google Scholar]
  16. Diebold, F.X., Yilmaz, K., (2012). Better to give than to receive: predictive directional measurement of volatility spillovers. International Journal of Forecasting, vol. 28(1) pp. 57–66. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.