Volatility behaviour of stock index futures in China: A bivariate GARCH approach

Purpose: This paper aims to investigate the volatility transmission and dynamics in China Securities Index (CSI) 300 index futures market. Design/methodology/approach: This paper applies the bivariate Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC) Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models using high frequency data. Estimates for the bivariate GARCH models are obtained by maximising the log-likelihood of the probability density function of a conditional Student's t distribution. Findings: This empirical analysis yields a few interesting results: there is a one-way feedback of volatility transmission from the CSI 300 index futures to spot returns, suggesting index futures market leads the spot market; volatility response to past bad news is asymmetric for both markets; volatility can be intensified by the disequilibrium between spot and futures prices; and trading volume has significant impact on volatility for both markets. These results reveal new evidence on the informational efficiency of the CSI 300 index futures market compared to earlier studies. Originality/value: This paper shows that the CSI 300 index futures market has improved in terms of price discovery one year after its existence compared to its early days. This is an important finding for market participants and regulators. Further, this study considers the volatility response to news, market disequilibrium and trading volume. The findings are thus useful for financial risk management.