Volatility Of Chinese Stock Market Research Articles

Economic Policy Uncertainty Shocks and Chinese Stock Market Volatility: An Empirical Analysis with SVAR

This paper proposes a framework for examining the interaction between stock market volatilities and economic uncertainty shocks, aiming to understand better the influence of economic uncertainty shocks on the Chinese stock market. The major empirical results include the followings. First, the economic policy uncertainty shocks push the Chinese stock volatility up, increasing the market risk. A 1‐standard‐deviation shock of economic policy uncertainty will enhance the stock volatility of the two composite indices by approximately 7% in 12 months. Second, the stock volatility reacted more intensely to fiscal and monetary economic policy uncertainty shocks, with a 1‐standard‐deviation shock that can enhance the stock volatility of the two composite indices by more than 10% in 12 months. Third, different stock indices exhibit different patterns of cumulative impulse responses, and the reaction of the volatility of the SSE real estate index to economic policy uncertainty shocks is more significantly intense than other indices. Besides, we have proved the robustness of empirical results by reestimating the models with a lag order of 2. Overall, our research results can provide policy and managerial insights for the sustainable development of the Chinese stock market and beyond.

Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach

AbstractIn this study, we propose a new family of the heterogeneous autoregressive realized volatility (HAR‐RV) models by considering truncated methods for predicting the RV in China's stock market. By adopting three types of critical values to recognize extremely large values of RV, we show that the modified models are simple but efficient to consistently deliver stronger in‐sample and out‐of‐sample forecasting performances than those of existing methods. Models that take truncated approaches into account can generate substantial economic gains in applications. We further provide evidence that the superiority of our proposed models is derived from the reduced variance of the measurement errors during days including truncated RVs. Additionally, the improved performances of the modified models still hold after considering the effects of jump components and leverage, as well as a wide range of extensions and robustness analyses.

Is Baidu index really powerful to predict the Chinese stock market volatility? New evidence from the internet information

PurposeThis paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.Design/methodology/approachFirst, the authors’ study commences with several HAR-RV-type models, then the study amplifies them respectively with the posting volume and search frequency to construct HAR-IF-type and HAR-BD-type models. Second, from in-sample and out-of-sample analysis, the authors empirically investigate the interpretive ability, forecasting performance (statistic and economic). Third, various robustness checks are utilized to reconfirm the authors’ findings, including alternative forecast window, alternative evaluation method and alternative stock market. Finally, the authors further discuss the forecasting performance in different forecast horizons (h = 5, 10 and 20) and asymmetric effect of information from Internet forum.FindingsFrom in-sample perspective, the authors discover that posting volume exhibits better analytical ability for Chinese stock volatility than search frequency. Out-of-sample results indicate that forecasting models with posting volume could achieve a superior forecasting performance and increased economic value than competing models.Practical implicationsThese findings can help investors and decision-makers obtain higher forecasting accuracy and economic gains.Originality/valueThis study enriches the existing research findings about the volatility forecasting of stock market from two dimensions. First, the authors thoroughly investigate whether the Internet information could enhance the efficiency and accuracy of the volatility forecasting concerning with the Chinese stock market. Second, the authors find a novel evidence that the information from Internet forum is more superior to search frequency in volatility forecasting of stock market. Third, they find that this study not only compares the predictability of the posting volume and search frequency simply, but it also divides the posting volume into “good” and “bad” segments to clarify its asymmetric effect respectively.HighlightsThis study aims to verify whether posting volume and search frequency contain predictive content for estimating the volatility in Chinese stock market.The forecasting model with posting volume can achieve a superior forecasting performance and increases economic value than competing models.The results are robust in alternative forecast window, alternative evaluation method and alternative market index.The posting volume still can help to forecast future volatility for mid- and long-term forecast horizons. Additionally, the role of posting volume in forecasting Chinese stock volatility is asymmetric.

How does investor sentiment impact stock volatility? New evidence from Shanghai A-shares market

PurposeThe focus of the current research is to examine whether mixed-frequency investor sentiment affects stock volatility in the China A-shares stock market.Design/methodology/approachMixed-frequency sampling models are employed to find the relationship between stock market volatility and mixed-frequency investor sentiment. Principal analysis and MIDAS-GARCH model are used to calibrate the impact of investor sentiment on the large-horizon components of volatility of Shanghai composite stocks.FindingsThe results show that the volatility in Chinese stock market is positively influenced by B–W investor sentiment index, when the sentiment index encompasses weighted mixed frequencies with different horizons. In particular, the impact of mixed-frequency investor sentiment is most significantly on the large-horizon components of volatility. Moreover, it is demonstrated that mixed-frequency sampling model has better explanatory powers than exogenous regression models when accounting for the relationship between investor sentiment and stock volatility.Practical implicationsGiven the various unique features of Chinese stock market and its importance as the major representative of world emerging markets, the findings of the current paper are of particularly scholarly and practical significance by shedding lights to the applicableness GARCH-MIDAS in the focused frontiers.Originality/valueA more accurate and insightful understanding of volatility has always been one of the core scholarly pursuits since the influential structural time series modeling of Engle (1982) and the seminal work of Engle and Rangel (2008) attempting to accommodate macroeconomic factors into volatility models. However, the studies in this regard are so far relatively scarce with mixed conclusions. The current study fills such gaps with improved MIDAS-GARCH approach and new evidence from Shanghai A-share market.

The role of US implied volatility index in forecasting Chinese stock market volatility: Evidence from HAR models

The US implied volatility index (VIX) is a popular proxy for global financial market uncertainty. This paper aims to assess the role of this proxy in forecasting the Chinese stock market volatility, as China’s globalization trend is strengthening. For this purpose, we develop six heterogeneous autoregressive (HAR) models allowing for VIX changes to forecast the Chinese stock market volatility. The in-sample and out-of-sample results show the VIX changes can improve the volatility forecasting in the Chinese stock market but mainly show improvement for the bad volatility forecasting rather than for good volatility forecasting. Additionally, such forecasting improvement is sizable at the short-run prediction horizon but weakens as the prediction horizon increases. Our results also remain robust by using alternative evaluation methods and alternative HAR models.

Economic Policy Uncertainty and Chinese Stock Market Volatility: A CARR‐MIDAS Approach

Intraday range (the difference between intraday high and low prices) is often used to measure volatility, which has proven to be a more efficient volatility estimator than the return‐based one. Meanwhile, a growing body of studies has found that economic policy uncertainty (EPU) has important impact on stock market volatility. In this paper, building on the range‐based volatility model, namely, the conditional autoregressive range (CARR) model, we introduce the CARR‐mixed‐data sampling (CARR‐MIDAS) model framework by considering intraday information to investigate the impact of EPU on the volatility of Chinese stock market and to explore the predictive ability of EPU for Chinese stock market. The empirical results show that both the China EPU (CEPU) and global EPU (GEPU) have a significantly negative effect on the long‐run volatility of Chinese stock market. Furthermore, we find that taking into account the CEPU and GEPU leads to substantial improvement in the ability to forecast the volatility of Chinese stock market. We also find that the CEPU provides superior volatility forecasts compared to the GEPU. Our findings are robust to different forecasting windows.

Volatility forecasting revisited using Markov‐switching with time‐varying probability transition

AbstractThis study proposes a novel model, Markov‐switching Heterogeneous Autoregressive (MS‐HAR) model with jump‐driven time‐varying transition probabilities (TVTP), to forecast the future volatility in Chinese stock market. The in‐sample results show that MS‐HAR models are more powerful than HAR‐RV‐type models; furthermore, the high‐volatility regime is short‐lived. Moreover, the out‐of‐sample results indicate that the MS‐HAR with TVTP model can achieve a superior forecasting performance and increase the economic value than the competing models including the simple HAR model and the MS‐HAR with fixed transition probabilities (FTP) model. The results are robust to several robustness checks including alternative forecast window, alternative evaluation method, alternative predictive model, sub‐sample analysis and alternative representative index.

Forecasting the volatility of Chinese stock market: An international volatility index

AbstractTo forecast the volatility of Chinese stock market, we use information from 28 international markets rather than relying on the Chinese market alone. A common factor, which we call the international volatility index, is constructed by the first principal component of all the 28 cross‐national stock market volatilities. We then add the international volatility index into the prevailing heterogeneous autoregressive model of realized volatility (HAR‐RV). The in‐sample estimation results show that the impact of this common index on future Chinese market volatility is statistically significant and positive. More importantly, the out‐of‐sample forecasting results suggest that our proposed model outperforms competing models including the HAR‐RV, kitchen sink model, and combination approaches. The results are similar when we use a wide range of robustness checks. Furthermore, the international volatility index also yields the highest economic value from an asset allocation perspective.

Asymmetric volatility spillovers between economic policy uncertainty and stock markets: Evidence from China

This study explores the spillovers between economic policy uncertainty (EPU) and stock market realized volatility (RV). The monthly index of Chinese and US EPU and RV are used to analyze the pairwise directional spillovers. We find that RV is a net receiver that is more vulnerable to shocks from U.S. EPU than to shocks from Chinese EPU. We further decompose the RV into good and bad volatility to test the asymmetric spillover effect between the stock market and EPU. The results suggest that EPU has a bigger effect on bad volatility in the stock market throughout most of the sample period. However, we find that good volatility spillovers become larger during periods of stimulated reform, whereas bad volatility spillovers become larger during periods of international disputes. We show that Chinese stock market volatility is sensitive to both U.S. and Chinese EPU and that the spillover is asymmetric in different periods.

Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching

The purpose of this paper is to investigate the role of regime switching in the prediction of the Chinese stock market volatility with international market volatilities. Our work is based on the heterogeneous autoregressive (HAR) model and we further extend this simple benchmark model by incorporating an individual volatility measure from 27 international stock markets. The in-sample estimation results show that the transition probabilities are significant and the high volatility regime exhibits substantially higher volatility level than the low volatility regime. The out-of-sample forecasting results based on the Diebold-Mariano (DM) test suggest that the regime switching models consistently outperform their original counterparts with respect to not only the HAR and its extended models but also the five used combination approaches. In addition to point accuracy, the regime switching models also exhibit substantially higher directional accuracy. Furthermore, compared to time-varying parameter, Markov regime switching is found to be a more efficient way to process the volatility information in the changing world. Our results are also robust to alternative evaluation methods, various loss functions, alternative volatility estimators, various sample periods, and various settings of Markov regime switching. Finally, we provide an extension of forecasting aggregate market volatility on monthly frequency and observe mixed results.

Economic policy uncertainty and the Chinese stock market volatility: Novel evidence

In this study, we investigate the impact of global economic policy uncertainty (GEPU) on Chinese stock market volatility. More importantly, for the first time, we explore the effects of directional GEPU based on the changing directions of GEPU and Chinese economic policy uncertainty (EPU). We make several noteworthy findings. First, the in-sample estimated results show that up and down GEPU can lead to substantially high stock market volatility for China. Second, the out-of-sample estimated results support the contention that the GEPU index is helpful for predicting volatility. Moreover, compared to GEPU alone, directional GEPU can provide more useful information that can increase the forecast accuracy. Third, we empirically find that directional GEPU is more effective in predicting Chinese stock market volatility when GEPU and EPU rise in the same month.

Economic policy uncertainty and the Chinese stock market volatility: new evidence

ABSTRACTThis study investigates the impacts of the economic policy uncertainty (EPU) indexes of China and the G7 countries on Chinese stock market volatility and further constructs a new diffusion index based on these indexes using principal component analysis (PCA) to achieve enhanced predictive ability. The in-sample results indicate that the EPU indexes of China and some of the G7 countries show a significantly negative impact on future volatility. Moreover, our constructed diffusion index also has a significantly negative impact. Furthermore, the out-of-sample results show that this diffusion index exhibits a significantly higher forecast accuracy than the EPU itself and combination forecasts. Finally, various robustness checks are consistent with our main conclusions. Overall, we construct a new and useful indicator that can substantially increase forecast accuracy with respect to the Chinese stock market.

Forecasting the Chinese stock volatility across global stock markets

In view of the growing concern of Chinese stock fluctuation, this paper forecasts the Chinese stock volatility by extracting global stock information by combining forecasts of time-varying parameter (TVP) volatility models. First, we construct individual constant coefficient (CC) models and TVP models across 27 global stock markets, and then use several strategies to combine their forecasts. The results show global stock information does forecast the future volatility of Chinese stock market. Both the forecast accuracy and economic values can be further improved by using strategies combining TVP models with global stock information. Specifically, the median combination shows its superiority for the volatile Chinese stock market. Our findings are robust to different estimation window sizes, volatility proxies, and evaluation criteria.

The Idiosyncratic Risk in Chinese Stock Market

Using daily stock return data of all listed firms in Chinese stock market from 1998 to 2018, we disaggregate the volatility of common stocks at the market, industry and firm levels. We find market volatility, on average, is the highest while firm volatility tends to lead to market and industry volatility series. None long-term trend time series behaviour exists for all three volatility series and firm volatility is best described by an autoregressive process with regime shifts associated with the structural market reforms or volatile market movements. We further proceed to identify the source of volatility at the industry level and find the idiosyncratic volatility in the largest manufacturing industry not only accounts for the largest proportion in the aggregate firm volatility but also is the lead indicator for the idiosyncratic volatility of other industries. Finally, unlike Brandt et al. [Review of Financial Studies 23(2): 863-899 (2010)], we find the idiosyncratic volatility in Chinese stock market is associated with high stock trading activities by institutional investors, the result of which is also robust when using other measures of idiosyncratic volatility.

Impact of US Uncertainty on Chinese Stock Market Volatility

ABSTRACTThe process of opening the Chinese stock market has sped up in the last two decades. This paper investigates the effects of the uncertainty in the US measured by news-implied volatility on the Chinese stock market volatility. Our empirical results for the full sample period (1997–2016) reveal that there is no clear effect of US uncertainty on fluctuations in China’s stock market. However, based on the subsample analysis along the opening progress in the Chinese stock market, we observe that US uncertainty had a significant effect on Chinese stock market volatility after Qualified Foreign Institutional Investors (QFII) and Renminbi QFII were allowed to invest in the Chinese stock market. The results are the same after the global financial crisis. Our results imply that the US uncertainty should be considered by investors deciding on investment strategy. The government should also consider the US uncertainty when enacting the policy on opening policy the Chinese stock market.

Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market

This paper investigates the impact of global economic policy uncertainty (GEPU) on the volatility of the Chinese stock market and whether GEPU has predictive power for the volatility of the Chinese stock market. We apply the generalized autoregressive conditional heteroscedastic mixed data sampling (GARCH–MIDAS) model to evaluate the impact of the monthly GEPU index on the daily Shanghai Composite index. Our empirical results show that GEPU has a positive and significant influence on the volatility of the Chinese stock market, which reflects that Chinese stock market has been gradually integrated into the global economy. In addition, the forecasts generated by the GARCH–MIDAS model with GEPU and RV (Realized Volatility) produce substantially smaller errors than the GARCH (1,1) model and the GARCH–MIDAS model with RV (measured by RMSE, RMAE, RMSD, RMAD loss functions and DM test), which confirms the important role of GEPU for predicting the volatility of the Chinese stock market. Overall, our findings imply that GEPU is an additional driving factor of long-term volatility in the Chinese stock market.

Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?

In this paper, we use a comprehensive look to investigate whether the G7 stock markets can contain predictive information to help in forecasting the Chinese stock market volatility. Our out-of-sample empirical results indicate the kitchen sink (HAR-RV-SK) model is able to attain better performance than the benchmark model (HAR-RV) and other models, implying that the G7 stock markets can help in predicting the one-day volatility of the Chinese stock market. Moreover, the kitchen sink strategy can beat the strategy of the simple combination forecasts. Finally, the G7 stock markets can indeed contain useful information, which can increase the accuracy forecasts of the Chinese stock market.

INSURANCE INCLUSION, TIME PREFERENCE AND STOCK INVESTMENT OF THE CHINESE HOUSEHOLDS

Using the China Household Finance Survey data in 2011, the estimation results of structural equation modeling demonstrate that the respondents with higher time preference rate have a significant higher probability of investing in stocks, which implies that the short-term households will prefer stock investment. The social insurance programs and insurance policies held by the family will have a significantly direct positive effect in promoting stock investment and also a significantly direct positive effect on the respondent’s time preference, which could further indirectly increase the family’s stock investment. These results show that the safety-net built by the Chinese government, including the social security and commercial insurance, is very likely to attract more short-term investors into the stock market. These empirical results provide new evidences to explain the extreme volatility of Chinese stock market and also testify the policy effect of building an environment for people to possess property income in China.

Baidu news information flow and return volatility: Evidence for the Sequential Information Arrival Hypothesis

This paper employs Baidu News as the proxy for information flow and investigates competing hypotheses on the relationships between information flow and return volatility in Chinese stock market. The empirical results show that: (1) trading volume and return volatility are not driven by the same variable, i.e., the information flow, and thus contradicts the predication of the Mixture of Distribution Hypothesis (MDH); (2) there exist significant lead-lag relationships between information flow and return volatility, which is in accordance with the Sequential Information Arrival Hypothesis (SIAH); (3) these findings are robust to alternative measurement of return volatility and subsample analysis. Generally speaking, these findings contradict the prediction of MDH and support the SIAH.

A Note on Conditional Variance and Decaying Rate: Chinese Equity Market

Conventional GARCH based models capture short run dependence structures in financial markets or shocks decaying at a faster exponential rate. However, the present study attempts to model long run dependence or memory in Chinese stock market volatility process by employing autoregressive (p)—fractionally integrated generalized autoregressive conditional heteroskedasticity model, i.e. AR (p)—FIGARCH model; following Bollerslev and Mikkelsen (J Econ 73(1):151–184, 1996) with a shift variable. Apart from FIGARCH (1, d, 1) model, conventional GARCH (p, 0, q) and Integrated GARCH (p, 1, q) based models are also employed in order to ensure comparability across integrated processes. The empirical findings advocate long run dependence structure in volatility process of Chinese equity market, whereby a shock to the future volatility decays at a slower hyperbolic rate. However, the integrated decaying parameter relates more to conventional GARCH (p, 0, q) model after considering structural breaks and foreign exchange rate movements, whereby exponential decaying rate is expected in the equity market. The findings bear strong implications for different market participants in their attempt to comprehend volatility component of the equity market.