Chinese Stock Market

В середине 2019 года КНР открыла новую биржу, а именно "Биржа научных и технологичных инновацией под Шанхайской фондовой биржей" с символичным англоязычным названием STAR Market. В процессе создания данной биржи были обнародованы множество различных документов, данная работа рассматривает нововедение (или их отсутствие) и проводит сопоставление того, что было в Китае и его целями который ставил Китай с тем, что в итоге получилось в рамках временного отрезка в 6 месяцев после ее официального открытия.

We provide a novel lens on to the presence and impact of qualified foreign institutional investors (QFII) in the top shareholdings of the non-financial domestically listed Chinese ‘A’ share firms. Unlike prior cross sectional studies which use only annual data, this research runs a robust panel fixed effects model employing quarterly data, thereby capturing the presence of a foreign investor in these firms for the first time and with greater precision. The results suggest that the presence of a QFII as a top shareholder in these companies is associated with their better performance, using both Tobin’s Q and ROA as the performance measures and controlling for internal/external corporate governance mechanisms. Economically, the impact on the former is the more significant. This suggests that the effect could be a result of herding after QFII behaviour in the market, but that there may also have been a managerial impact. We follow up with an instrumental variables model to mitigate the reverse causality question and find the empirical relationship holds. Contrary to the only work on QFIIs and governance post-implementation, the findings of this paper suggest that in spite of their very low percentage holdings, the presence of a QFII top shareholder could have acted to augment corporate governance mechanisms in the Chinese listed companies, contributing towards the mitigation of agency problems in Chinese firms. This is therefore the first paper to suggest quantitative evidence of a positive governance effect of foreign investment in the exclusively A share companies in China.

This paper studies the profitability of applying technical analysis that signals the entry and exit from the stock market in three Chinese stock markets - the Shanghai, Hong Kong and Taiwan Stock Exchanges. The Simple Moving Average (MA) and its extensions, Exponential MA, Dual MA, Triple MA, MACD and TRIX for both long and short strategies are examined. Applying the

China’s stock markets are barely 25 years old however, they already boast more members than China’s communist party, “90 million individual investors, compared with 87.8 million members of the Communist Party” (Faux 2015).


First, this paper will explore investor behaviors which caused volatility in the Chinese stock market. Next, it will explore government intervention to support the stock market. Finally, it will explore the implications a Chinese stock market crash could have on Chinese domestic and foreign economies.

Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highly persistent in both the markets. The T-GARCH model has been applied in order to assess the presence of information asymmetry that bad news impacts volatility more than good news. Our results reveal that both Indian and Chinese stock markets' volatility shows time varying behaviour. The theoretical reasoning of the asymmetric impact of news that bad news affects volatility more than good news has been confirmed in both markets. Furthermore, the spillover effect of volatility across the two markets has been tested using the T-GARCH-X model. The results show unidirectional spillover effect of volatility from Chinese stock market to the Indian stock market. This implies that shocks from Chinese stock market impact conditional volatility in the Indian stock markets only but not vice-versa. Article can be accessed online at http://www.publishingindia.com

The aim of the paper is to evaluate the effects of the intervention of the Chinese government undertaken during the 2015–2016 crisis on the Shanghai Stock Exchange (SSI). The following research hypothesis was set up: in the long run, both the initial efforts of the Chinese authorities to drive individual investors to invest in stock exchanges along with the interventions launched by the government to stop the market falls were not relevant to stock valuation. The study results have proven that in the analysed time monetary authorities, as well as government and regulatory bodies, generated many decisions and announcements which were expected to influence the behaviour of the stock exchange investors. In short term it created artificially market anomalies, observed between the Q4 2014 and Q1 2016. The interventions interfered with the long term growth trend of SSI index, however did not shift this trend and after interventions ended it was apparently ongoing and not disturbed until 2...

Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highl...

The aim of the paper is to evaluate the effects of the intervention of the Chinese government undertaken during the 2015–2016 crisis on the Shanghai Stock Exchange (SSI). The following research hypothesis was set up: in the long run, both the initial efforts of the Chinese authorities to drive individual investors to invest in stock exchanges along with the interventions launched by the government to stop the market falls were not relevant to stock valuation. The study results have proven that in the analysed time monetary authorities, as well as government and regulatory bodies, generated many decisions and announcements which were expected to influence the behaviour of the stock exchange investors. In short term it created artificially market anomalies, observed between the Q4 2014 and Q1 2016. The interventions interfered with the long term growth trend of SSI index, however did not shift this trend and after interventions ended it was apparently ongoing and not disturbed until 2017.

This paper shows that Bayesian estimation and comparison of multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) and multivariate stochastic volatility (MSV) models with Markov Chain Monte Carlo methods could be straightforwardly and successfully conducted in WinBUGS package. And an algorithm based on the Cholesky decomposition is proposed to set as a prior for a correlation matrix. They are illustrated by applying three types of parsimonious MGARCH and MSV specifications nested in constant conditional correlations to weekly returns of five sector indexes of Shanghai Stock Exchange over the period of 28 June 2004 to 30 June 2008. Empirical results provide evidence for the superior performance of MSV models over MGARCH models and give support to the feasibility of the algorithm we presented. In addition, the estimation results also suggest the significant negative correlation between the persistency and the variability of volatilities in MSV models.

Our paper examines calendar effects in Chinese stock market, particularly monthly and daily effects. Using individual stock returns, we observe the change of the calendar effect over time. In Shanghai and Shenzhen, the year-end effect was strong in 1991 – but ...

Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highl...

This article explores the dynamics of the dependence between ‘A’ and ‘B’ share indices on the Shanghai and Shenzhen securities exchanges. While the marginal behaviour of each stock index is modelled by an asymmetric Student-t distribution, the nature of the dependence is captured through a copula representation. Our results confirm the already documented time-varying pattern of the dependence structure. Moreover, we show that regional and world shocks as represented by the Hang Seng Asia and the S&P 500 indices affect the marginal distributions of Chinese ‘A’ and ‘B’ stock indices, but do not influence the dynamics of their dependence.

Our paper examines calendar effects in Chinese stock market, particularly monthly and daily effects. Using individual stock returns, we observe the change of the calendar effect over time. In Shanghai and Shenzhen, the year-end effect was strong in 1991 -- but disappeared later. As the Chinese year-end is in February, the highest returns can be achieved in March and April.