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.

From an industry perspective, this paper applies the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market for theperiod2003/02-2021/10, we find that the impact of geopolitical risk on stock market volatility is highly heterogeneous, and its significance mostly appears in the upper and lower tails. At the market level, China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the current and lagging effects of China’s and U.S. GPR on industry stock market volatility are heterogeneous. Second, for most industries, China’s and U.S. GPR/IS can exacerbate industry stock market volatility both in bullish and bearish markets. In addition, China’s and U.S.GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.

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.

We investigate, for China’s investable companies, the relation between stock returns and firm characteristics, and the impacts on the relation of the 2001-2003 financial reforms to further liberalize stock markets. For the first time in the literature, we document coexistence of a positive size effect and a growth effect, and the importance of liquidity and positive earnings for returns; and we also show that they underwent a structural break upon the reforms. These results are robust across 12 alternative panel model specifications with different ways of estimating and controlling for the market beta, different proxies for market portfolios, the problem of outliers considered, and the January effect allowed for.

This letter applies the endogenous structural break Minimum Lagrange Multiplier unit root test to re-examine the stationarity of Chinese stock market indices. The main result is consistent with Yan and Felminghan (Applied Economics Letters, 13, 605-608, 2006) who use the ADF-type structural break unit test, and the break we found is more in line with the reality.

The paper examines the short-run spillover effect of daily stock returns and volatilities between the S&P 500 in the U.S. and Shanghai SSE composite in China. First, we find that a structural break happened in the SSE stock return mean in December 2005. Second, analyzing modified GARCH (1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the U.S. to the China stock market in the post-break period. Third, the symmetric volatility spillover effect from China to the U.S. is also observed in the post-break period.

According to the theory of unit root test, Lee-Carter model and generalized linear model, which are widely used in mortality projection, impose key implicit assumptions respectively which are inconsistent with each other. Log mortality rate (the force of mortality or the central mortality rate) is described as a unit root process in Lee-Carter model, while it is modeled as a deterministic trend process in generalized linear model. We use panel LM unit-root tests with level shifts to test the assumptions in above models, based on mortality data of the 7 most developed countries(G7) and Nordic countries(Denmark, Finland, Norway, Sweden). The test results show that a mortality projection model, whatever it is Lee-Carter model or generalized linear model, is not always appropriate to predict dynamic mortality rates of different countries. Further, we explain period effect and cohort effect of dynamic mortality according to the results of structural break test. Based on the empirical results, we extend Lee-Carter model, which includes a special case of generalized linear model. To check the performance of the extended model, we use it to forecast USA and Sweden mortality and we find that the extended Lee-Carter model works better than the original Lee-Carter model.

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.