Information transmission between U.S. and China index futures markets: An asymmetric DCC GARCH approach ☆ Yang Hou a, ⁎, Steven Li b a Department of Finance, Waikato Management School, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand b Graduate School of Business and Law, RMIT University, 379-405 Russell St., Melbourne, VIC 3000, Australia abstract article info Article history: Accepted 23 October 2015 Available online 10 November 2015 Keywords: Information transmission Asymmetric DCC GARCH Stock index futures market Chinese and U.S. stock markets The Chinese stock market and its impacts on other stock markets have attracted a lot of attention and have been of a great concern to many countries. This paper aims to shed light on the issue by examining the information transmission between the S&P 500 and the CSI 300 index futures markets. The empirical results reveal that news from one market significantly affects the volatilities of open prices of the other and the impact from U.S. to China is stronger than the other way round. Further, past news of the U.S. has a significant impact on the vol- atilities of daily trading in China, but not vice versa. These findings indicate that the U.S. stock index futures mar- ket is more efficient in impounding information from other markets. © 2015 Elsevier B.V. All rights reserved. 1. Introduction The informational linkages between stock markets in developed economies are widely observed in the literature (Antoniou, Pescetto, and Violaris, 2003; Eun and Shim, 1989; Hamao, Masulis, and Ng, 1990; King and Wadhwani, 1990; Koutmos and Booth, 1995). It is gen- erally agreed that pricing behavior of domestic securities is determined not only by domestic information but also by news generated interna- tionally (Koutmos and Booth, 1995). News generated in one market may transmit into another one via return and volatility spillovers, resulting in the prices of the latter being partially determined by inter- national risk factors. However, the patterns of informational linkages observed in the developed economies may not hold for the Chinese stock market due to its short history and some unique features. These features per- tain to the ownership types of instrument suppliers, the proportion of different types of investors, and the trading mechanisms. First, se- curities of the Chinese stock market are mostly supplied by the state- owned enterprises (SOEs). Unfortunately, the tradable shares only account for a small proportion of total shares of SOEs, resulting in the scarcity of security supplies due to the limited number of SOEs in China. This makes the stock market vulnerable to speculation (Chen, Han, Li, and Wu, 2013). Second, the individual and retail in- vestors are the major force driving stock market movements (Chen et al., 2013; Ng and Wu, 2007; Yang, Yang, and Zhou, 2012). Third, a T + 1 trading rule applies in the Chinese stock market. Those who trade stocks in one trading day cannot do another trade until the next trading day. Thus, the possibility of intraday trading is preclud- ed. In addition, short-sale transactions are difficult to implement due to high transaction costs and a lack of lenders (Chang, Luo, and Ren, 2013; Chen et al., 2013; Xie and Mo, 2014). Finally, the Chinese stock market contains two types of stocks that are available to trade. One type is called ‘A-shares’ and the other is called ‘B-shares’. When do- mestic investors in China can trade both A-shares and B-shares, for- eign qualified investors are only allowed to trade B-share stocks. To clarify how the Chinese stock market interacts with developed markets given the characteristics above, this study examines the informa- tion transmission in terms of daily return and volatility spillovers between the renowned stock index futures market, the Standard & Poor's (S&P) 500 stock index futures market in the U.S., and the recently established China Securities Index (CSI) 300 stock index futures market in China. In this study, we utilize a bivariate Vector Autoregression (VAR) model with a bivariate asymmetric Dynamic Conditional Correlation (A-DCC) Generalized Autoregressive Conditional Heteroskedasticity (GARCH) error structure to estimate the interdependencies of first and second mo- ments of futures returns distributions. The bivariate A-DCC GARCH model can simultaneously capture the time-varying covariance matrix of error structure of the VAR model and the asymmetry of the correlation matrix of error terms. It should be noted that examining the interaction between the S&P 500 and the CSI 300 stock index futures markets is nearly equivalent to examining the interaction between U.S. and Chinese stock markets. The underlying spot asset of the CSI 300 stock index futures is the CSI 300 stock index, which accounts for approximately 70% of the market capitalization of the Chinese stock market. It is well acknowledged that Economic Modelling 52 (2016) 884–897 ☆ The authors would like to thank Sushanta Mallick (The Editor) and three anonymous referees for their comments and suggestions which have been helpful in improving the quality of this paper. ⁎ Corresponding author. Tel.: +64 7 8379402. E-mail addresses: greghou@waikato.ac.nz (Y. Hou), steven.li@rmit.edu.au (S. Li). http://dx.doi.org/10.1016/j.econmod.2015.10.025 0264-9993/© 2015 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Economic Modelling journal homepage: www.elsevier.com/locate/ecmod