scholarly journals An Exploration of Realized Volatility and Returns in the Chinese Stock Market

2021 ◽  
Vol 14 ◽  
pp. 304-314
Author(s):  
Kuaile Shi
Keyword(s):  
Stock Market ◽  
Turnover Rate ◽  
Predictive Power ◽  
Realized Volatility ◽  
Excess Return ◽  
Chinese Stock Market ◽  
Realized Variance ◽  
Market Returns ◽  
Stock Market Returns ◽  
Out Of Sample

This paper uses high-frequency stock index data to construct realized volatilities for the Chinese stock market and applies in-sample and out-of-sample  to test the predictive power of realized volatility on Chinese stock market returns. The empirical results show that realized volatility can significantly predict the excess return of the Chinese stock market in the next month, and the in-sample and out-of-sample regression models  are positive, and the out-of-sample  The p-value of the regression model is significant. And after controlling for a range of other stock predictor variables, we find that the regression coefficient of realized volatility is still significant, and we find that after adding realized volatility, the in-sample adj-  increases with the inclusion of realized volatility, suggesting that realized volatility does have components that are not explained by other economic variables. Also based on a different construction method, the realized variance still has significant predictive power after averaging the realized variance. After combining two different realized variance indicators, the predictive power is still better. In terms of economic interpretation, this paper finds that the predictive power of realized variance on stock returns is through influencing the turnover rate (market trading activity), which in turn influences stock market returns. We find that realized volatility has a significant effect on the turnover rate, and when we use realized volatility to predict the turnover rate, which in turn predicts the excess return, we find that the coefficient is highly significant, indicating that realized volatility can indeed cause changes in excess return by affecting the turnover rate.

scholarly journals What Goliaths and Davids among Swiss firms tell us about expected returns on Swiss asset markets

2019 ◽  
Vol 155 (1) ◽  
Author(s):  
David R. Haab ◽  
Thomas Nitschka
Keyword(s):  
Stock Market ◽  
Predictive Power ◽  
Bond Market ◽  
Asset Markets ◽  
Market Returns ◽  
Stock Market Returns ◽  
Asset Return ◽  
The Us ◽  
Out Of Sample ◽  
Sample Test

AbstractMotivated by recent US evidence, we evaluate the predictive power of changes in the weight of large firms in the aggregate stock market (“Goliath vs David” (GVD)) for Swiss stock market returns and bond market returns. Previous research suggests that the asset return dynamics in the US and Switzerland differ markedly. Forecasting Swiss asset returns hence constitutes a challenging “out-of-sample” test for GVD. Over the sample period from January 1999 to December 2017, we find that the Swiss version of GVD exhibits predictive power for Swiss stock and bond market returns even in the presence of global predictors. However, Swiss bond market returns are best predicted by the US term spread.

LONG-TERM MEMORY IN EMERGING MARKETS: EVIDENCE FROM THE CHINESE STOCK MARKET

2006 ◽  
Vol 05 (03) ◽  
pp. 495-501 ◽  
Author(s):  
CHAOQUN MA ◽  
HONGQUAN LI ◽  
LIN ZOU ◽  
ZHIJIAN WU
Keyword(s):  
Stock Market ◽  
Emerging Markets ◽  
Emerging Market ◽  
Long Term Memory ◽  
Chinese Stock Market ◽  
Market Returns ◽  
Stock Market Returns ◽  
Term Memory ◽  
Rescaled Range Analysis

The notion of long-term memory has received considerable attention in empirical finance. This paper makes two main contributions. First one is, the paper provides evidence of long-term memory dynamics in the equity market of China. An analysis of market patterns in the Chinese market (a typical emerging market) instead of US market (a developed market) will be meaningful because little research on the behaviors of emerging markets has been carried out previously. Second one is, we present a comprehensive research on the long-term memory characteristics in the Chinese stock market returns as well as volatilities. While many empirical results have been obtained on the detection of long-term memory in returns series, very few investigations are focused on the market volatility, though the long-term dependence in volatility may lead to some types of volatility persistence as observed in financial markets and affect volatility forecasts and derivative pricing formulas. By means of using modified rescaled range analysis and Autoregressive Fractally Integrated Moving Average model testing, this study examines the long-term dependence in Chinese stock market returns and volatility. The results show that although the returns themselves contain little serial correlation, the variability of returns has significant long-term dependence. It would be beneficial to encompass long-term memory structure to assess the behavior of stock prices and to research on financial market theory.

scholarly journals DETERMINANTS OF G7 AND CHINESE STOCK MARKET RETURNS DURING COVID-19 OUTBREAK

2020 ◽  
Vol S.I. (1) ◽  
pp. 256-266
Author(s):  
Ahmed JERIBI ◽  
◽  
Mohamed FAKHFEKH ◽  
Keyword(s):  
Growth Rate ◽  
Stock Market ◽  
Financial Markets ◽  
Portfolio Diversification ◽  
Chinese Stock Market ◽  
Market Returns ◽  
Stock Market Returns ◽  
Hedging Strategies ◽  
The Us

The purpose of this paper is to discuss the determinants of G7, and Chinese stock market returns during the COVID-19 outbreak. We find that Bitcoin and Ethereum can generate benefits from portfolio diversification and hedging strategies for G7 financial investors in early 2020. Our result reveals that Gold is neither hedge nor haven during the COVID-19 pandemic. In addition, the results indicated that the expected volatility of the US stock market has no effect on the Japanese and Chinese financial markets. Finally, our results suggest that the growth rate of confirmed COVID-19 cases and deaths has an impact only on the US stock market.

scholarly journals The Relevance of Cboe Volatility Index to Stock Markets in Emerging Economies

2015 ◽  
Vol 6 (1) ◽  
pp. 93-106
Author(s):  
Tamara Mariničevaitė ◽  
Jovita Ražauskaitė
Keyword(s):  
Stock Market ◽  
Emerging Economies ◽  
Stock Markets ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Global Risk ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Index

We examine the capability of CBOE S&P500 Volatility index (VIX) to determine returns of emerging stock market indices as compared to local stock markets volatility indicators. Our study considers CBOE S&P500 VIX, local BRIC stock market volatility indices and BRIC stock market MSCI indices daily returns in the period from January 1, 2009 to September 30, 2014. Research is conducted in two steps. First, we perform Spearman correlation analysis between daily changes in CBOE S&P500 VIX, local BRIC stock market VIX and MSCI BRIC stock market indices returns. Second, we perform multiple regression analysis with ARCH effects to estimate the relevance of CBOE S&P500 VIX and local VIX in determining BRIC stock market returns. Research reports weak correlation between CBOE S&P500 VIX and local VIX (except for Brazil). Furthermore, results challenge the assumption of CBOE S&P500 VIX being an indicator of global risk aversion. We conclude that commonly documented trends of rising globalization and stock markets co-integration are not yet present in emerging economies, therefore the usage of CBOE S&P500 VIX alone in determining BRIC stock market returns should be considered cautiously, and local volatility indices should be accounted for in analysis. Furthermore, the data confirms the presence of safe haven properties in Chinese stock market index.

scholarly journals The Nonlinear Relationship between Investor Sentiment, Stock Return, and Volatility

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Gang He ◽  
Shuzhen Zhu ◽  
Haifeng Gu
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Investor Sentiment ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Bear Market ◽  
Stock Market Return ◽  
Space Effect

Based on the DSSW model, we analyze the nonlinear impact mechanism of investor sentiment on stock return and volatility by adjusting its hypothesis in Chinese stock market. We examine the relationship between investor sentiment, stock return, and volatility by applying OLS regression and quantile regression. Our empirical results show that the effects of investor sentiment on stock market return are asymmetric. There is “Freedman effect” in Chinese stock market, but only optimistic sentiment has a significant nonlinear impact on stock market returns when the stock market is a balanced market or a bear market. Meanwhile, “create the space effect” does exist in Chinese stock market too. It only exists when the market is in equilibrium, and only pessimistic sentiment has the nonlinear effect on stock market volatility.

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