Volatility Of Chinese Stock Market Research Articles

Geopolitical risks, investor sentiment and industry stock market volatility in China: Evidence from a quantile regression approach

From an industry perspective, we apply the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market, we find that China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility at the market level. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the impact of geopolitical risk on industry stock market volatility is highly heterogeneous, and its significance mostly occurs in the upper and lower tails. Second, China’s and U.S. GPR/IS can exacerbate industry stock market volatility in bullish and bearish markets. In addition, China’s and U.S. GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.

Economic policy uncertainty and stock market volatility in China: Evidence from SV-MIDAS-t model

This study combines the stochastic volatility (SV) model with a mixed data sampling (MIDAS) structure under t-distribution to investigate the effect of economic policy uncertainty (EPU) on Chinese stock market volatility. Furthermore, we compare eight volatility models regarding using “GARCH or SV” model, “with or without” the MIDAS structure, under “normal or t” distributions. The model comparison results show that SV-MIDAS-t model is the best in terms of data fitting, AIC, BIC, and various loss function criteria. Based on the SV-MIDAS-t model, we find that a rise in EPU index significantly increases the long-term component of volatility, and this impact has a time lag effect.

Forecasting Chinese stock market volatility with option-implied risk aversion: Evidence from extended realized EGARCH-MIDAS approach

This paper investigates the role of option-implied risk aversion (IRA) in forecasting the Chinese stock market volatility. We derive the IRA from the SSE 50ETF option prices using behavioral pricing kernel theory. We extend the realized EGARCH-MIDAS (REGARCH-MIDAS) model to incorporate the IRA. Furthermore, we propose the REGARCH-MIDAS-IRA-ES model accounting for the extreme IRA information to model and forecast the Chinese stock market volatility. Empirical results based on the extended REGARCH-MIDAS models show that the IRA has a significantly positive impact on the long-term volatility of Chinese stock market, and the extremely positive IRA has a greater impact on the long-term volatility compared to the extremely negative and normal IRA. Moreover, the IRA possesses predictive value for the Chinese stock market volatility. In particular, we observe that the extreme IRA can provide more valuable information to forecast the Chinese stock market volatility. Our proposed REGARCH-MIDAS-IRA-ES model outperforms a variety of competing models in terms of out-of-sample volatility forecast. We confirm that the superior forecasting performance of the model is robust to different out-of-sample evaluation approach, different out-of-sample forecast windows, different MIDAS lags and different volatility states. Finally, a volatility-timing strategy demonstrates that incorporating the IRA, and in particular the extreme IRA, leads to economic gains for a mean–variance utility investor.

International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models

This paper aims to forecast the volatility of Chinese stock market under the effects of international crude oil shocks. Eight individual models, including multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) and bidirectional gated recurrent unit (BiGRU) are constructed. The realized volatilities of the CSI 300 index and ten primary sector indices are taken as explained variables, respectively. Four oil shock indicators and the autoregressive terms of the realized volatilities are taken as explanatory variables. The SHAP method is used to analyze their effects on the stock indices. Based on eight individual models, four kinds of combination models, i.e., a mean combination (Mean), a median combination (Median), a trimmed mean combination (Trimmed Mean), and two discount mean squared forecasting error combinations (DMSPE (1) and DMSPE (0.9)) are proposed. We compare forecasting performance between combination and individual ones. Empirical results show that the effects of international crude oil shocks on Chinese stock market are significant and have strong predictability. The effects on the energy, industry, optional consumption, and public sectors are greater than those on the CSI 300 and other sectors. Most of the combination models can effectively improve forecasting accuracy. In addition, by changing the benchmark model, the lengths of the rolling window, and the historical lengths of oil shock indicators, we find that most of the combination models are robust in volatility forecasting. This study is of guiding significance for individual and institutional investors to understand the operating mechanism of Chinese stock markets.

Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products

This study investigates the role of investor attention to the price of petroleum products (APPP) in forecasting Chinese stock market volatility. In the in-sample analysis, we find that a higher APPP index can lead to higher stock market volatility in the next month. The out-of-sample results show that the APPP index has strong predictive power for Chinese stock market volatility. Moreover, the volatility predictability of the APPP index is stronger than that of all commonly used economic and economic policy uncertainty predictors. Furthermore, when compared with the commonly used economic and economic policy uncertainty predictors, we find that the APPP index contains more valuable information for predicting stock market volatility than traditional predictors. When performing two robustness tests, alternative forecasting windows and lags of RV, the results are still robust. Further analysis indicates that the forecasting power of the APPP index for stock market volatility concentrates on the low-volatility regime.

"Investor attention fluctuation and stock market volatility: Evidence from China".

This paper examines the linkage between Chinese stock market volatility and investor attention fluctuation. In Heterogeneous autoregressive (HAR) model, first, we analyzed the linkage between both decomposed and undecomposed stock market realized volatility and investor attention fluctuations across full-sample and two-year moving window sub-samples. Second, we compare the predictive power of four models in short-, medium-, and long-term volatility forecasting. Empirical results show large positive attention fluctuation amplified Chinese stock market volatility after the outbreak of COVID-19, and negative small attention fluctuation significantly stabilized stock market volatility before COVID-19, and the impact dwindled in after COVID-19. The model incorporating decomposed realized volatility and decomposed attention fluctuation performs better in volatility Forecasting. This research underscores a shift in the dynamics between stock market volatility and investor attention fluctuations, and investor attention fluctuation improves the volatility forecasting accuracy of the Chinese stock market.

Cross-market information transmission and stock market volatility prediction

The deep integration of global economic and financial activities accelerates the information transmission across financial markets. The cross-market information has become a crucial factor to influence the stock market fluctuation. This paper investigates the explanatory power and impacting mechanism of cross-market information flow in the prediction of Chinese stock market volatility. The empirical results show that the cross-market information flow exhibits significant linear and nonlinear influences on Chinese stock market volatility, and it also appears term-heterogeneous on the prediction accuracy, i.e., the cross-market information flow significantly contributes to medium-term and long-term prediction of Chinese stock market volatility, and the improvement of prediction accuracy is mainly due to nonlinear mechanism, whereas the cross-market information flow performs less value for short-term volatility prediction.

Categorial economic policy uncertainty indices or Twitter-based uncertainty indices? Evidence from Chinese stock market

This paper mainly investigates the performances of Chinese categorial economic policy uncertainty (EPU) indices and categorial Twitter-based uncertainty for predicting Chinese stock market volatility. Results show both types of uncertainty indices can predict Chinese stock market volatility, especially the categorial Twitter-based uncertainty indices, showing uncertainty indices constructed based on social media contain more valuable information than newspaper-oriented uncertainty indices. In addition, we highlight the predictive performances of the Least absolute shrinkage and selection operator (LASSO) model with regime switching for forecasting Chinese stock market volatility.

Chinese stock market volatility and herding behavior asymmetry during the COVID-19 pandemic

The primary purpose of this paper is to explore the herding behavior in the Chinese stock market during COVID-19 and the asymmetry of that behavior using the daily returns of A- and B-shares from 2 January 2019, to 15 October 2021. The study uses the cross-sectional absolute deviation model to analyze stock market herding behavior by non-linear polynomial regression. We show that the herding behavior in the Chinese stock market is more prominent during the COVID-19 pandemic. Herding behavior has a negative effect on stock market volatility. Moreover, such a suppressing effect weakened during the COVID-19 pandemic. There is an asymmetry in herding behavior during the bull and bear markets, which is helpful in our investigation of the market’s volatility during the COVID-19 pandemic. The pronounced asymmetry in the herding behavior of the Chinese stock market during COVID-19 is assessed using the E-GARCH (p, q) model. The empirical results of the present study contribute to the literature about herding asymmetry by showing the herding behavior during the health crisis and bull and bear markets. It also helps reconcile the debate about the impact of herding on market stability and provides insightful guidance for investors wishing to invest in the Chinese stock market.

Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators

We comparatively assess the influence of global economic uncertainty measures on Chinese stock market volatility. Using a model based on generalized autoregressive conditional heteroskedasticity and mixed-data sampling, the results show that the global economic policy uncertainty index, the geopolitical risk index, and the global economic condition index all significantly influence the long-term volatility of China’s equity market. We highlight which of these measures has the most explanatory power under differing contexts. As uncertainty measures have wide applicability, investors, policymakers, and academicians will be quite interested in our results.

Does climate policy uncertainty affect Chinese stock market volatility?

Global warming forces policymakers to change climate policies to achieve global carbon neutrality targets. Although policy changes and developments affect financial asset price volatility, unpredictable climate policy changes make policy decisions less certain. This study investigates the spillover effects of climate policy uncertainty (CPU) on Chinese stock market volatility in the realized GARCH-MIDAS framework. To capture more information on asset price volatility, we further employ 5-min high-frequency data. The empirical results provide strong evidence to support that our strategy can successfully improve volatility forecasting accuracy. First, in-sample results indicate that CPU has a significant effect on stock price volatility. Second, out-of-sample tests confirm that the extended model that considers both CPU and high-frequency data exhibits the best predictive ability. In addition, various robustness tests strongly demonstrate our main findings. Thus, this paper demonstrates that weather effects exist in the stock market and reminds investors that they should pay more attention to climate policies in the current period of the climate crisis.

Air quality index and the Chinese stock market volatility: Evidence from both market and sector indices

This paper mainly examines whether the air quality index can forecast the Chinese stock market volatility. By comparing the predictability of air quality index, leverage components, jump components, and overnight information, we find that air quality index is more efficient to predict the stock market volatility, during medium-term and long-term horizons. Importantly, from both market and sector indices, we find the air quality index is still more informative than the leverage components, jump components, and overnight information for forecasting the stock market volatility. In addition, leverage components have better performances during a longer horizon for both market and sector indices. This paper tries to provide new evidence for stock market volatility prediction based on the Chinese aggregate stock market and different stock sectors.

Investor attention on the Russia-Ukraine conflict and stock market volatility: Evidence from China

The Russia-Ukraine conflict has brought ripple effects to the global economy. This paper mainly investigates whether investor attention to the Russia-Ukraine conflict can affect the Chinese stock market volatility. Empirical results show investor attention to the Russia-Ukraine conflict contains more valuable information to predict Chinese stock market volatility than some popular predictors such as leverage, jump, geopolitical risk. Importantly, we find the model containing ATT_AU information and least absolute shrinkage and selection operator (LASSO) method performs best among the models, especially during long-term horizons.

Climate policy uncertainty and stock market volatility: Evidence from different sectors

This paper mainly investigates whether the climate policy uncertainty index (CPU) can predict the volatility of Chinese stock market volatility considering different sectors. Out-of-sample results show that climate policy uncertainty index can have a greater effect on the utility sector. We also investigate the effects of CPU based on longer horizons, different volatility levels and the COVID-19 pandemic. This paper tries to provide new evidence based on sector stock indices.

Dependence and spillover among oil market, China's stock market and exchange rate: new evidence from the Vine-Copula-CoVaR and VAR-BEKK-GARCH frameworks

We first employ the method of multivariate GARCH models and Vine-Copula-CoVaR to analyse relationships between dependence, systematic risk spillover, and volatility spillover between the USD/CNY exchange rate and the returns on WTI crude oil futures and the Chinese stock market since China's 2005 foreign exchange reform. We utilise daily data from 2005 to 2020. We find a more complex dependence of the USD/CNY exchange rate on stock markets and WTI crude oil prices. All have negative risk spillovers among paired markets, with WTI having the most substantial risk spillover. However, the strength of the systematic risk spillover varies across markets. Based on the results of the VAR(1)-BEKK-GARCH (1,1) and Wald tests confirm that there is a substantial mean spillover from the Chinese stock market and the USD/CNY exchange rate to the WTI crude oil price, whereas there is a more significant spillover from the WTI crude oil price to Chinese stock market volatility. The empirical findings extend the systematic understanding of the international crude oil price shocks to the dependence and transmission mechanism between the Chinese stock market and the USD/CNY exchange rate (USD/CNY). Our findings can help investors and policymakers to manage risk better and develop more sensible market rules.

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 Chinese stock market volatility under uncertainty

As the trend of world economic integration intensifies, global uncertainty also has a certain impact on the stock markets of various countries. To explore the volatility of China's stock market under uncertainty, we use a mixed-frequency data model and introduce uncertainty variables, namely the Global Economic Policy Uncertainty Index (GEPU), the United States (US EPU) and China (Chia EPU) economic policy uncertainty indices, the implied volatility index (VIX) and the geopolitical risk index (GPR), to build an extended GARCH- MIDAS model to analyse the impact of uncertainty indices on Chinese stock market volatility. The empirical results show that, except for the US EPU index, all other uncertainty indices have some impact on the Chinese stock market, and the out-of-sample forecasting results indicate that the introduction of these variables improves the forecasting effect of the model, with China's economic policy uncertainty index showing significant advantages in forecasting both weekly and monthly volatilities.

Modeling and managing stock market volatility using MRS-MIDAS model

This paper adds the decomposed components of realized volatility to investigate China's stock market volatility based on the mixed data sampling (MIDAS) framework. Empirical results show that considering extreme negative volatility and extreme positive volatility, and moderate volatility can have a significantly better performance than the benchmark model for predicting the Chinese stock market volatility. Importantly, we find that taking the regime switching into consideration can further improve forecasting accuracy. The results are robust in alternative evaluation method, different forecasting windows, the direction-of-change test, and China's stock bubble period, showing decomposing the realized volatility into extreme negative volatility and extreme positive volatility, and moderate volatility can further improve the forecasting accuracy of the models. This paper tries to give new evidence to stock market volatility prediction.

Forecasting the Chinese Stock Market Volatility with G7 Stock Market Volatilities: A Scaled PCA Approach

ABSTRACT In this study, we forecast the realized volatility (RV) of the Chinese stock market using the heterogeneous autoregressive (HAR) model and various extended models. To extract the volatility information from the G7 stock markets, we employ a newly proposed approach, the scaled principal component analysis (SPCA), to produce a diffusion index and extend the HAR benchmark (HAR-SPCA). To validate the effectiveness of the SPCA approach, we employ three other dimension reduction approaches, the kitchen sink model, and five popular forecast combinations to deal with multivariate information and make competing forecasts. The results suggest that the combined volatility information from the G7 stock markets significantly predicts Chinese stock market volatility. More importantly, the forecasts from the HAR-SPCA model are steadily more accurate than the benchmark and other competing models under various evaluation criteria. Finally, our results are persistent to various robustness checks and the evaluation of portfolio performance.

Forecasting the Chinese stock market volatility: A regression approach with a t-distributed error

ABSTRACT In this paper, we improve the ordinary least squares (OLS) estimation approach by replacing a normally distributed error with a t-distributed error. Empirically, we investigate the predictability of the Chinese stock market volatility based on this modified approach. Results show that the modified OLS method with a t-distributed error has a significantly stronger forecasting power than its counterpart with a normally distributed error. From an asset allocation perspective, the modified OLS approach can help a mean-variance investor obtain sizeable utility gains. We also conduct two extended empirical analyses and further verify the superiority of the regression approach with a t-distributed error. Our results are robust to a series of settings. Finally, we find that the regression approach with a t-distributed error shows greater tolerance for outliers by assigning smaller weights to them, thereby highlighting its superior performance.