Empirical analysis of stock returns volatility in China market based on Shanghai and Shenzhen 300 Index

Pan, Wenrong

doi:10.1109/icfte.2010.5499433

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Furthermore, we find the Australian SRI market's volatility reacts differently to the good and bad news. Our findings on asymmetric volatilities are consistent with previous studies by Nelson (1991), Kearns and Pagan (1993), Zakoian (1994), Sentana (1995), Henry (1998), Koutmos (1998), Yavan and Aybar (1998), Mian and Adam (2001), Frijns et al (2010) and Pan (2010).…”

Section: Resultssupporting

confidence: 93%

Volatility of Socially Responsible Investments in Australia

Bian¹,

Fan²,

Wong³

2016

GRAF

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Socially responsible investment (SRI) has become increasingly popular in recent years, as there is strong evidence showing SRI is less volatile and generates higher returns than traditional investments. This paper examines how volatility is spilled over from stocks and bond markets into the Australian SRI market. We employ TGARCH model from 1994 to 2015 and find that the Australian SRI market is positively influenced by domestic stock market. Furthermore, we find that the Australian SRI market is less correlated with other markets, hence, there is potential for diversification. Our findings indicate performance persistence and asymmetric effect in the volatilities of the Australian SRI market.

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Section: Resultssupporting

confidence: 93%

Volatility of Socially Responsible Investments in Australia

Bian¹,

Fan²,

Wong³

2016

GRAF

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“…The authors drew a conclusion that the difference between China stock market and other stock markets is due to the character and behavior finance.  In [10], the authors analyzed the volatility of a stock in China on its returns series using the models of GARCH family. They found that the series of stock returns is stationary, and it has a significant ARCH effect, a volatility cluster exists in China stock market.…”

Section: Introductionmentioning

confidence: 99%

Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators

Qin¹,

Wang²,

Ge³

et al. 2011

JILSA

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While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use

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Empirical analysis of stock returns volatility in China market based on Shanghai and Shenzhen 300 Index

Cited by 2 publications

References 11 publications

Volatility of Socially Responsible Investments in Australia

Volatility of Socially Responsible Investments in Australia

Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators

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