Intraday dynamic relationships between CSI 300 index futures and spot markets: a high-frequency analysis. Issue 4 (May 2016)
- Record Type:
- Journal Article
- Title:
- Intraday dynamic relationships between CSI 300 index futures and spot markets: a high-frequency analysis. Issue 4 (May 2016)
- Main Title:
- Intraday dynamic relationships between CSI 300 index futures and spot markets: a high-frequency analysis
- Authors:
- Zhou, Bei
Wu, Chong - Abstract:
- Abstract Based on intraday 5-min high-frequency dataset, this paper empirically analyzes the intraday dynamic relationships between China's CSI 300 index futures and spot markets with vector autoregression (VAR) and multivariate GARCH (MGARCH) models. By comparing four VAR–MGARCH models (dynamic conditional correlation, constant conditional correlation, diagonal and BEKK), the VAR–DCC–MGARCH model is found to fit the data the best and be preferred over the other models. The results of this model show that although there are bidirectional price causal relationships between the CSI 300 index futures and spot markets, the index futures return shock affects the spot market more severely than the spot return shock affects the futures market, indicating that the index futures market dominates the price discovery process between the two markets. There are bidirectional volatility spillovers effects between the CSI 300 index futures and spot markets, and the spillovers effects from index futures to spot almost equal to that from index spot to futures. The time-varying conditional correlations between the CSI 300 index futures and spot markets change from 0.4787 to 0.9594 across time, showing there is a strong positive correlation and linkage effect between the two markets. These results indicate that after a period of time of development, the price discovery performance of the CSI 300 index futures market has begun to function well, and the impact of the CSI 300 index futures marketAbstract Based on intraday 5-min high-frequency dataset, this paper empirically analyzes the intraday dynamic relationships between China's CSI 300 index futures and spot markets with vector autoregression (VAR) and multivariate GARCH (MGARCH) models. By comparing four VAR–MGARCH models (dynamic conditional correlation, constant conditional correlation, diagonal and BEKK), the VAR–DCC–MGARCH model is found to fit the data the best and be preferred over the other models. The results of this model show that although there are bidirectional price causal relationships between the CSI 300 index futures and spot markets, the index futures return shock affects the spot market more severely than the spot return shock affects the futures market, indicating that the index futures market dominates the price discovery process between the two markets. There are bidirectional volatility spillovers effects between the CSI 300 index futures and spot markets, and the spillovers effects from index futures to spot almost equal to that from index spot to futures. The time-varying conditional correlations between the CSI 300 index futures and spot markets change from 0.4787 to 0.9594 across time, showing there is a strong positive correlation and linkage effect between the two markets. These results indicate that after a period of time of development, the price discovery performance of the CSI 300 index futures market has begun to function well, and the impact of the CSI 300 index futures market on its underlying spot market has strengthened. … (more)
- Is Part Of:
- Neural computing & applications. Volume 27:Issue 4(2016)
- Journal:
- Neural computing & applications
- Issue:
- Volume 27:Issue 4(2016)
- Issue Display:
- Volume 27, Issue 4 (2016)
- Year:
- 2016
- Volume:
- 27
- Issue:
- 4
- Issue Sort Value:
- 2016-0027-0004-0000
- Page Start:
- 1007
- Page End:
- 1017
- Publication Date:
- 2016-05
- Subjects:
- Index futures -- Dynamic relationships -- Volatility spillovers -- Multivariate GARCH -- High-frequency data
Neural networks (Computer science) -- Periodicals
Neural circuitry -- Periodicals
Artificial intelligence -- Periodicals
Neural Networks (Computer) -- Periodicals
Réseaux neuronaux (Informatique) -- Périodiques
Réseaux nerveux -- Périodiques
Intelligence artificielle -- Périodiques
006.32 - Journal URLs:
- http://www.springerlink.com/content/0941-0643/20/6/ ↗
http://www.springerlink.com/content/102827/ ↗
http://www.springer.com/gb/ ↗ - DOI:
- 10.1007/s00521-015-1915-y ↗
- Languages:
- English
- ISSNs:
- 0941-0643
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6081.280250
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British Library HMNTS - ELD Digital store - Ingest File:
- 10041.xml