scholarly journals Can the Baidu Index predict realized volatility in the Chinese stock market?

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wei Zhang ◽  
Kai Yan ◽  
Dehua Shen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Power ◽  
Realized Volatility ◽  
Trading Strategies ◽  
Time Series Models ◽  
Chinese Stock Market ◽  
Continuous Components ◽  
Long Term Impact

AbstractThis paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index. Furthermore, the predictability of the Baidu Index is found to rise as the forecasting horizon increases. We also find that continuous components enhance predictive power across all horizons, but that increases are only sustained in the short and medium terms, as the long-term impact on volatility is less persistent. Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

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 Sector Volatility Spillover and Economic Policy Uncertainty: Evidence from China’s Stock Market

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1411
Author(s):  
Xiaqing Su ◽  
Zhe Liu
Keyword(s):  
Economic Policy ◽  
Stock Market ◽  
Asset Allocation ◽  
Policy Uncertainty ◽  
Volatility Spillover ◽  
Economic Policy Uncertainty ◽  
Long Run ◽  
Transmission Structure ◽  
Long Term Impact

Following generalized variance decomposition, we identify the transmission structure of financial shock among ten sectors in China. Then, we examine whether economic policy uncertainty (EPU) affects it through GARCH-MIDAS regression. We find that consumer discretionary, industrials, and materials sectors are systemically important industries during the sample period. Further research of dynamic analysis shows that each sector acts in a time-varying role in this structure. The results of the GARCH-MIDAS regression indicate that none of the selected EPU indexes has a significant long-term impact on the total volatility spillover of the inter-sector stock market in China. However, the EPUs do affect some sectors’ spillover indexes in the long run, and they are significantly heterogeneous. This paper can provide regulatory suggestions for policymakers and reasonable asset allocation and risk avoidance methods for investors.

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.

Empirical Study on the Non-Periodic Cycles of Fractal Time Series: Examples of Indices of the Chinese Stock Market

2015 ◽  
Vol 734 ◽  
pp. 637-641
Author(s):  
Yang Li ◽  
Wei Yu Zhang ◽  
Yong Wei ◽  
Jin Hui Sun
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Stock Market ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Biased Random Walk ◽  
Fractal Time ◽  
Study Support ◽  
Two Indices ◽  
Periodic Cycles

By R/S analysis, non-periodic cycles of the SSE Composite Index and SZSE Composite Index are studied in this paper. With a different determinant method from the previous works about fractal behaviors of the Chinese stock market, the empirical results obtained in this study support the non-periodic cycle results but with different values. With more data available, the analysis shows that the two indices follow a biased random walk with two non-periodic cycles, one about 4.5 years and another about 9 years, which may be tied to the economic and politic cycles.

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