Owing to the liberalisation of financial markets, the impact of international capital flows on the Chinese stock market has become substantial. This study investigates the effects of economic policy uncertainty (EPU), geopolitical risk (GPR), consumer sentiment (CCI), macroeconomic fundamentals (MECI), and money supply (M2) on the correlations between the stock and exchange rate markets. The negative correlation between these two markets has become more pronounced in recent years. Moreover, EPU, GPR, CCI, and MECI negatively impact long-term stock-exchange rate correlations, while M2 has a positive impact. Portfolios of stock-exchange rates effectively reduce risk, especially when considering structural breaks.

This study examines the price discovery patterns in the three BRICS countries’ stock index futures markets that were launched after 2000 – China, India, and Russia. We detect two structural breaks in these three futures price series and their underlying spot price series, and use them to form subsamples. Employing a Vector Error Correction Model (VECM) and the Hasbrouck (1995) test, we find the price discovery function of stock index futures markets generally improves over time in China and India, but declines in Russia. A closer examination not only confirms the findings of Yang et al. (2012) and Hou and Li (2013) regarding price discovery in China’s stock index markets, but also reveals the inconsistency of futures’ leading role in the price discovery process. Further, we find some evidence of day-of-the-week effects in earlier part of the sample in China, but not in India or Russia. And our GARCH model results show bidirectional volatility spillover between futures and spot in China and India, but only unidirectional in Russia.

The short history and market segmentation characteristic of China stock markets not surprisingly make the market indicators behave in certain way. In this paper, we tabulate the belief that the regulatory and instrumental policy changes in China structurally break the market indices. This is proven and break points are detected with a focus on Shanghai Stock Exchange in the first part of this paper. Whereas, the stochastic trend nature of the market remains even when the structural breakpoints are detected and after it is tested against various kinds of deterministic trends. It, to some extent, implies the efficiency of Shanghai market with regards to unpredictability. The second part of this paper dedicates to analyzing the interaction between A and B share markets. As a contrast to the past literature, the change in trading volume of B share market is found to be a much more sensitive leading indicator to the change in A share market, in the sense of Granger causality with a VAR fashion. This finding may further reveal the unbalanced investor structure in A and B share markets.