Why Does Return Volatility Differ in Chinese Stock Markets?


Book Description

We estimate a modified mixture of distribution model (Andersen, 1996) to explore the underlying causes of the volatility differences between domestic A shares and foreign B shares listed in Chinese stock markets. Using return and trading volume data for 24 firms as well as value-weighted portfolios constructed, we obtain parameter estimates characterizing the distribution of the underlying news information flows. We find evidence that news enters the A-share market more intensively, is more correlated with A-share trading, and is more persistent for A shares than for B shares. Our cross-sectional test results also indicate that some of the greater return volatility for A-shares is due to variation in firm's profits, firm size, and a substantially larger number of investors leading to a high probability of trading on a given news flow.




Aspects of Volatility in the Chinese Stock Market


Book Description

This thesis analyses three sets of issues: 1) the cyclical behaviour of the Chinese stock markets, 2) the fitness of using realized volatility (RV) in the generalized autoregressive conditional heteroskedasticity (GARCH) model, and 3) the volatility spillover between the Chinese and Australian stock markets. After conducting an extensive literature review, the thesis examines the three sets of issues separately.First, a Markov regime switching model is applied to analyse the bull and bear cycles in the Chinese stock market, since the cycles of bull and bear markets can reflect economic development and investor confidence. Specifically, grouping stocks by industry and firm size, the results show the following: 1) Bear cycles between stocks and the index overlap heavily, indicating strong herding effects. A long bear market cycle is found and can be explained by widely diversified stock performance across the markets. 2) Certain shocks to one industry could have different impacts on the Shanghai and Shenzhen stock markets. 3) Firm size can have a significant impact on the performance of stocks in bull or bear cycles.The second topic focuses on estimating the RV of the Chinese stock markets and comparing it with the GARCH model. The actual volatility is inherently unobserved, while the RV could be treated as being directly observable and then be used to study time-varying behaviour and forecasting. Thus, a large number of studies use RV in GARCH models for volatility analysis. However, there is yet no study that discusses the correlation between RV and GARCH while using RV in GARCH models. This could lead to bias in estimation because of the different properties of RV and GARCH. The results show that GARCH models combined with RV could be more suitable for estimating volatility for large firms. When the firms are grouped in terms of positive/negative returns, similar results are found as when firms are grouped by firm size.The third topic estimates the volatility spillover between the Chinese and Australian stock markets, motivated by the lack of attention to spillover between these two markets in the literature. While economic interdependence between Australia and China has soared during the last two decades due to China's tight reliance on Australia's mining and resources, little research attention has been paid to these two countries. This study fills the literature gap and assesses the volatility spillover between the Chinese and Australian stock markets based on the CSI300 and ASX200 industry indices. To the best of my knowledge, this is the first study using Chinese industry data to discuss volatility spillover. The key findings of the thesis are that volatility spillover across these two markets is bidirectional, while there is one-sided or insignificant spillover across industries between these two countries. The findings of the thesis fill the literature gap, help clarify the debate about volatility spillover between the Chinese stock market and the world market, and provide a clearer idea of the channels through which volatility is transmitted across countries.




Testing for Expected Return and Market Price of Risk in Chinese A-B Share Market


Book Description

There exist dual-listed stocks which are issued by the same company in some stock markets. Although these stocks bare the same firm-specific risk and enjoy identical dividends and voting policies, they are priced differently. Some previous studies show this seeming deviation from the law of one price can be solved due to different expected return and market price of risk for investors holding heterogeneous beliefs. This paper provides empirical evidence for that argument by testing the expected return and market price of risk between Chinese A and B shares listed in Shanghai and Shenzhen stock markets. Models with dynamic of Geometric Brownian Motion are adopted, multivariate GARCH models are also introduced to capture the feature of time-varying volatility in stock returns. The results suggest that the different pricing can be explained by the difference in expected returns between A and B shares in Chinese stock markets. However, the difference between market prices of risk is insignificant for both markets if GARCH models are adopted.










The Role of Analysts


Book Description

Given the unique institutional setting and the role of analysts in the Chinese stock markets, we investigate the effect of analyst activities on idiosyncratic volatility (IVOL) anomaly. Our results show that the inverse relation between IVOL and future stock returns is more pronounced in the subsample of stocks without analyst coverage. For stocks with analyst coverage, revision activities further attenuate the negative relation between IVOL and future stocks returns. In fact, we find a positive relation between IVOL and future stock returns among the subsample of stocks with analyst upgrade revisions. We argue that our results are evidence of analysts playing the role of disseminating information and particularly reducing information asymmetry in the Chinese stock markets. Moreover, positive news is incorporated into stock prices more quickly in the Chinese stock markets. Finally, we show that our results are not driven by differences in limits-to-arbitrage or short-sale constraint among different stock subsamples.




Estimating Stock Return Volatility in Indian and Chinese Stock Market


Book Description

Investors step into the stock market with the objective of earning smart returns on their investments. The stock market can help in realising these goals of the investors, however, all investments are subject to risks. The origin of the risk is the uncertainty of realising the desired returns on the investment. This aspect is known as risk of the investment. This paper aims to search the best model to estimate and forecast volatility of Indian and Chinese stock market. The data for the paper is related to the two main indices of Indian Stock Market namely, SENSEX and NIFTY and two indices of Chinese stock market, namely, Shenzhen composite index and Shanghai composite index for the period July 2003 to June 2013. We applied symmetrical as well as asymmetrical GARCH models to the data. Among all the three models i.e. GARCH, EGARCH and TARCH, we found the GARCH (1,1) model as the best model to estimate and forecast the volatility of Chinese stock market for both the daily and weekly return series. For the Indian stock market, the recommended volatility estimation and forecasting model is EGARCH model that captures the leverage effect. We did not find volatility clustering and leverage effect for the monthly return series for both Indian and Chinese stock market. Thus, it is suggested to use the traditional time invariant volatility models for the monthly return series.




Stock Returns and Volatility on China's Stock Markets


Book Description

We examine time-series features of stock returns and volatility, as well as the relation between return and volatility in four of China's stock exchanges. Variance-ratio tests reject the hypothesis that stock return follows a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time-varying volatility and shows volatility is highly persistent and predictable. The results of GARCH-M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns.




The Effect of Bond Rating Changes on Stock Returns


Book Description

"Efficient Market Hypothesis has always been a hot topic for empirical study in Finance. In this paper, we examine the efficiencies of Mainland China and Hong Kong markets by analyzing the different reactions of stock price and volatility to credit rating changes. The study of impact of credit rating change also fills a gap of no empirical analysis of credit rating change effect in these two markets. In a semi-strong efficient market, investors cannot make profit based on public information. In this study, we select Chinese cross-listed A-H share companies as our sample and compare the effects of bond rating changes on A-share stock price and H-share stock price. The differences in the stock return and volatility reactions signify the differences in market efficiency. The results from an event study indicate that neither market is semi-strong efficient and Hong Kong market is more efficient in digesting credit rating change information. Both Mainland China and Hong Kong markets show statistically significant and negative abnormal returns after the announcement of credit rating downgrades and only Mainland China market shows statistically significant abnormal returns before the announcement. Hong Kong market shows statistically significant and positive abnormal returns around the announcement of credit rating upgrades and Mainland China market shows no statistically significant abnormal returns around the announcement. Concerning volatility, credit rating downgrades can cause significant positive abnormal volatility around the announcement date in both Mainland China and Hong Kong markets, while there is no significant abnormal volatility around the announcement of credit rating upgrades. In the cross-sectional analysis of return reactions to credit rating changes, pre-announcement abnormal returns and whether credit ratings moved to speculative grade have an impact on the abnormal returns during the announcement."--Author's abstract.