Chinese Stock Market Indices Research Articles

Multivariate causality tests with simulation and application

This paper extends the test established by Hiemstra and Jones (1994) to develop a nonlinear causality test in a multivariate setting. A Monte Carlo simulation is conducted to demonstrate the superiority of our proposed multivariate test over its bivariate counterpart. In addition, we illustrate the applicability of our proposed test for analyzing the relationships among different Chinese stock market indices.

Crash occurrence probability and stock market efficiency: The Chinese stock exchange via Shannon entropy

The main purpose of this paper is to study the ability of the financial model to predict a crash, thus the evolution of the daily informational efficiency is measured for the Chinese stock market index. The efficiency, which is the issue addressed by the weak-form efficient market hypothesis, is calculated using a new method the Shannon entropy and the symbolic time series analysis. A logit model is applied in order to study the relationship between efficiency and probability of the financial crash. The obtained results reveal that the Chinese financial market tends towards efficiency without attaining totally and we note that the market goes through periods of inefficiency. The results of the logit models suggest always the probability of having a crash increases as the informational efficiency.

Multivariate Causality Tests with Simulation and Application

The traditional linear Granger causality test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones (1994) develop a nonlinear Granger causality test in a bivariate setting to investigate the nonlinear causality between stock prices and trading volume. In this paper, we first discuss linear causality tests in multivariate settings and thereafter develop a nonlinear causality test in multivariate settings. A Monte Carlo simulation is conducted to demonstrate the superiority of our proposed multivariate test over its bivariate counterpart. In addition, we illustrate the applicability of our proposed test to analyze the relationships among different Chinese stock market indices.

Analysis of the long‐term relationship between macro‐economic variables and the Chinese stock market using heteroscedastic cointegration

Purpose – The purpose of this paper is to investigate the relationship between the Chinese stock market indices and a set of macro‐economic variables, i.e. money supply, industrial production, inflation, exchange rate and interest rates.Design/methodology/approach – The aims of this paper are addressed using heteroscedastic cointegration analysis.Findings – Results show that the cointegrating relationship does exist between stock prices and the macro‐economic variables in the highly speculative Chinese stock market. Detailed analysis shows stock market performance is positively related to that of macro‐economy in the long term.Research limitations/implications – The results imply that in the long run, investors can benefit in terms of better returns and portfolio diversification as the Chinese economy is expected to continue to perform strongly.Originality/value – The main contributions of this paper are two‐fold: first, this is the first paper to examine the long‐term relationship between the stock marke...

Relationships between oil price shocks and stock market: An empirical analysis from China

This paper investigates the interactive relationships between oil price shocks and Chinese stock market using multivariate vector auto-regression. Oil price shocks do not show statistically significant impact on the real stock returns of most Chinese stock market indices, except for manufacturing index and some oil companies. Some “important” oil price shocks depress oil company stock prices. Increase in oil volatility may increase the speculations in mining index and petrochemicals index, which raise their stock returns. Both the world oil price shocks and China oil price shocks can explain much more than interest rates for manufacturing index.