Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

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

This 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.

Dynamics of volatility spillover between stock market and foreign exchange market: evidence from Asian Countries

Background:The purpose of this study is to examine volatility spillover effects between stock market and foreign exchange market in selected Asian countries;Pakistan,India,Sri Lanka,China,Hong Kong and Japan.This study considered daily data from 4th January,1999 to 1st January,2014.Methods:This study opted EGARCH(Exponential Generalized Auto Regressive Conditional Heteroskedasticity)model for the purpose of analyzing asymmetric volatility spillover effects between stock and foreign exchange market.Results:The EGARCH analyses reveal bidirectional asymmetric volatility spillover between stock market and foreign exchange market of Pakistan,China,Hong Kong and Sri Lanka.The results reveal unidirectional transmission of volatility from stock market to foreign exchange market of India.The analysis reveals no evidence of volatility transmission between the two markets in reference to Japan.Conclusions:The result of this study provide valuable insights to economic policy makers for financial stability perspective and to investors regarding decision making in international portfolio and currency risk strategies.

An empirical examination of investor sentiment and stock market volatility: evidence from India

Understanding the irrational sentiments of the market participants is necessary for making good investment decisions.Despite the recent academic effort to examine the role of investors’sentiments in market dynamics,there is a lack of consensus in delineating the structural aspect of market sentiments.This research is an attempt to address this gap.The study explores the role of irrational investors’sentiments in determining stock market volatility.By employing monthly data on market-related implicit indices,we constructed an irrational sentiment index using principal component analysis.This sentiment index was modelled in the GARCH and Granger causality framework to analyse its contribution to volatility.The results showed that irrational sentiment significantly causes excess market volatility.Moreover,the study indicates that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns.The findings are crucial for retail investors as well as portfolio managers seeking to make an optimum portfolio to maximise profits.

Cross-Sectional Variations of Illiquidity on Stock Returns,Idiosyncratic Volatility Biases in the Shanghai A’Share Stock Market

Under the NTS Reform(Non-Tradable Share Reform),this paper explores the cross-sectional relations between illiquidity and stock returns by considering the idiosyncratic volatility biases in the Shanghai A’Share stock market.Differing from prior studies,stock returns are decreasing in a stock’s illiquidity both before and after the NTS Reform.Regarding the negative relation between illiquidity and stock returns,we find that stock returns show no clear relation with illiquidity after controlling for idiosyncratic volatility biases.Furthermore,we use residual approach to eliminate the effect of idiosyncratic volatility,and find there exists a positive relation between illiquidity and stock returns after the NTS Reform.

Volatility Risk Management of Chinese Stock Grading Market——An Empirical Study of GARCH-VaR Model

This paper mainly through the comparison of GARCH-VaR China stock market board,small board and gem in the United States correspond to the three stock index volatility,volatility between stock indexes obtained U.S.stock market volatility risk multi-level market differences.As a suggestion and reference for investors,it can also provide reference for the supervision department of stock market risk.Based on the empirical research,analyzes the advantages and disadvantages of traditional risk measurement methods,and combined with GARCH model with high degree of complexity and the practice effect analysis,trying to find the objective measure stock model analysis.In the specific study of the volatility of the stock market,through the comparison of China’s three major plates and the market classification mechanism of mature U.S.stock market,combined with the objective situation of the market,draw conclusions and change expectations.From the empirical results,the U.S.stock market has recovered after the financial crisis,and its performance on risk volatility is better than China’s three major plates.From the comparison of the stock market in the same country,the small and medium-sized plates tend to have greater risks,while the risks of the main board and the gem have the characteristics of low average value but frequent fluctuations.

Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators

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.

Dependencies between price duration, volatility, volume and return on the Warsaw Stock Exchange

The successive changes of asset prices are the most visible manifestation of financial markets dynamics. There exist different views about factors generating these changes, but many researchers and practitioners agree that the most important among them is the impact of information flow. According to the market microstructure theories, it depends mainly on the behavior of informed and uniformed traders. In the paper, we investigate dependencies between the possible proxies of information process： price duration and corresponding to it volume change and return. Our main objective is to answer the question about the most important factor in the process of discovering information by uniformed traders. We apply a set of models for volatility, volume and duration data. Our analysis is performed for selected equities listed on the Warsaw Stock Exchange and uses tick-by-tick data. The obtained results show that the stock liquidity on this leading stock market in Central and Eastern Europe is the most important factor influencing the process of discovering information by uninformed traders.

Exploring Apple’s Stock Price Volatility Using Five GARCH Models

The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related fields.This paper evaluates the volatility of Apple Inc.(AAPL)returns using five generalized autoregressive conditional heteroskedasticity(GARCH)models:sGARCH with constant mean,GARCH with sstd,GJR-GARCH,AR(1)GJR-GARCH,and GJR-GARCH in mean.The distribution of AAPL’s closing price and earnings data was analyzed,and skewed student t-distribution(sstd)and normal distribution(norm)were used to further compare the data distribution of the five models and capture the shape,skewness,and loglikelihood in Model 4-AR(1)GJR-GARCH.Through further analysis,the results showed that Model 4,AR(1)GJR-GARCH,is the optimal model to describe the volatility of the return series of AAPL.The analysis of the research process is both,a process of exploration and reflection.By analyzing the stock price of AAPL,we reflect on the shortcomings of previous analysis methods,clarify the purpose of the experiment,and identify the optimal analysis model.

Dynamic relationship between volume and volatility in the Chinese stock market:evidence from the MS-VAR model

Since market uncertainty,or volatility,serves as a crucial gauge for assessing the traits of market fluctuations,the link between stock market volume and price continues to be a focal point of interest in finance.This study examines the dynamic,nonlinear correlations between Chinese stock volatility,trading volume,and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model(MS-VAR).The empirical findings are as follows:(1)The Chinese stock market can be divided into three regional systems:steady downward,steady upward,and high volatility.The three states have similar frequencies of occurrence,and their corresponding stable probabilities are not high,indicating that the Chinese stock market is unstable.(2)Asymmetric dynamic relationships exist between market volatility,investment return,and trading volume.For different regimes,while the effect of trading volume on volatility and return appears to be insignificant,the impacts of volatility and return on trading volume are considerably strong.(3)A regime-dependent,contemporaneous correlation between volatility and return is observed,which also reflects the behavior of the Chinese stock market“chasing up and down”.However,a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes,indicating that uncertainty in the Chinese stock market is closely related to information inflow.

Investor sentiments and stock marketsduring the COVID-19 pandemic

This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using variousmethods, including panel regression with fixed effects, panel quantile regressions, apanel vector autoregression (PVAR) model, and country-specific regressions. We proxyfor negative and positive investor sentiments using the Google Search Volume Indexfor terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significantrelationships between positive and negative investor sentiments and stock marketreturns and volatility. Specifically, an increase in positive investor sentiment leads toan increase in stock returns while negative investor sentiment decreases stock returnsat lower quantiles. The effect of investor sentiment on volatility is consistent acrossthe distribution: negative sentiment increases volatility, whereas positive sentimentreduces volatility. These results are robust as they are corroborated by Granger causalitytests and a PVAR model. The findings may have portfolio implications as they indicatethat proxies for positive and negative investor sentiments seem to be good predictorsof stock returns and volatility during the pandemic.

Modelling the dynamics of stock market in the gulf cooperation council countries:evidence on persistence to shocks

This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,Kuwait,Qatar,Saudi Arabia,and the United Arab Emirates,under different financial and economic circumstances.The empiri-cal investigation is conducted using daily data from June 1,2005 to July 1,2019.The analysis is conducted using a set of double long-memory specifications with some significant features such as long-range dependencies,asymmetries in conditional variances,non-linearity,and multiple seasonality or time-varying correlations.Our study indicates that the joint dual long-memory process can adequately estimate long-memory dynamics in returns and volatility.The in-sample diagnostic tests as well as out-of-sample forecasting results demonstrate the prevalence of the Autoregressive Fractionally Integrated Moving Average and Hyperbolic Asymmetric Power Autoregressive Conditional Heteroskedasticity modeling process over other competing models in fitting the first and the second conditional moments of the market returns.Moreover,the empirical results show that the proposed model offers an interesting framework to describe the long-range dependence in returns and seasonal persistence to shocks in conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations.A noteworthy finding is that the long-memory dependencies in the conditional variance processes of stock market returns appear important,asymmetric,and differ in their volatility responses to unexpected shocks.Our evidence suggests that these markets are not completely efficient in processing regional news,thus providing a sound alternative for regional portfolio diversification.

BM(book-to-market ratio) factor: mediumterm momentum and long-term reversal

To explain medium-term momentum and long-term reversal,we use the difference between the optional model and the CAPM model to construct a winner-loser portfolio.According to the CAPM model’s zero explanatory ability with respect to stock market anomalies,we obtain an anomaly interpretative model.This study shows that this anomaly interpretative model can explain stock market perceptions and medium-term momentum.Most importantly,BM is a critical factor in the model’s explanatory ability.We present a robustness test,which includes selecting new sample data,adding new auxiliary variables,changing sample years,and adding industry fixed effects.In general,the BM effect does have considerable explanatory power in medium-term momentum and long-term reversal.

Financial Integration Among the ASEAN 5 ＋ 3 Stock Markets： A Preliminary Look at the First 10 Years of the New Millenium

The purpose of this study is to investigate the financial integration of the stock markets of the ASEAN 5 ＋ 3 countries. These countries include Indonesia, Malaysia, Philippines, Singapore, Thailand, China, Japan, and South Korea. The research determined the stock return volatility for each country＇s index during the first decade of the new millennium. The findings showed that there is the presence of integration and co-integration with Philippine index＇s return with the index＇s returns of the following countries： Indonesia, Singapore, and Thailand. Furthermore, there is evidence of volatility clustering in these stock markets. The study concluded with the policy implications of greater integration in light of the planned cross trading among four ASEAN bourses, namely, Philippines, Singapore, Thailand, and Malaysia by 2012.

Nature of Volatility Patterns of Futures and Options on Nifty Index

Derivatives were introduced in Indian financial market to reduce volatility in the spot market.The present study attempts to study the impact of derivatives on stock market volatility.In the present study,data have been taken for Nifty Index for a period from 01-01-1996 to 05-02-2016.For analyzing the impact of introduction of derivatives on Nifty Index Volatility,we have taken proxy variable of Nifty Junior Index and Standard&Poor’s 500(S&P 500)Index returns.The data have also been classified into pre-futures(introduced on 12-06-2000)and post-futures and pre-options(introduced on 04-06-2001)and post-options period.The results show that volatility has reduced after introduction of futures and options.

Volatility Prediction via Hybrid LSTM Models with GARCH Type Parameters

Since the establishment of financial models for risk prediction,the measurement of volatility at risky market has improved,and its significance has also grown.For high-frequency financial data,the degree of investment risk,which has always been the focus of attention,is measured by the variance of residual sequence obtained following model regression.By integrating the long short-term memory(LSTM)model with multiple generalized autoregressive conditional heteroscedasticity(GARCH)models,a new hybrid LSTM model is used to predict stock price volatility.In this paper,three GARCH models are used,and the model that can best fit the data is determined.

经济不确定性对股票市场长期波动的非对称效应

本文运用A-GARCH-MIDAS模型深入探讨了宏观不确定性(MU)、经济政策不确定性(EPU)的总体和周期变化对股市长期波动的非对称效应,并进一步解析了两种经济不确定性非线性效应的风格投资异质性。结果显示:(1)MU存在显著的“好”“坏”性质非对称性,而EPU则存在显著的“上升”“下降”趋势非对称性。(2)经济不确定性对股市长期波动的推升作用在其中长期的“恶性”变化中体现,而长期经济不确定性的“良性”变化则具有显著的压降作用。(3)从市场风格来看,“好的”MU变化影响范围较小,但具有市场风格异质性,而EPU的“上升”变化影响范围较大,但影响强度较为接近;从行业风格来看,MU对行业指数长期波动仅存在单方向变化的“边际效应”,且不同性质的变化作用于不同行业,而EPU对行业指数长期波动的非对称效应更为明显。

ESG信息披露能否抑制股票价格波动风险?

基于2009—2022年沪深两市A股上市公司的样本数据,使用多时点双重差分法探究ESG信息披露对股票价格波动风险的影响。通过实证分析发现:ESG信息披露抑制了股票价格波动风险,这一结论在经过一系列稳健性检验后依然成立。机制检验结果显示:ESG披露主要通过降低机构投资者抱团来抑制股价波动。进一步分析发现:外部的媒体关注强化了ESG信息披露的风险降低效应。异质性检验表明:在制造业等特定行业、信息披露质量高以及中、东部地区的企业中,ESG披露对股价波动风险的抑制作用更强。研究在理论上为探索ESG信息披露的经济后果研究提供了新的视角,在实践上为资本市场的发展与稳定以及风险防范提供了一定的经验证据。

RCEP背景下中日韩股票市场的联动性与风险溢出衍变

在RCEP实施推进的背景下,从区域经济一体化引致金融一体化的视角切入对比了RCEP推进的不同阶段中日韩三国股票市场的联动情况和风险溢出情况。结果表明,三国股票市场的收益波动具有显著的杠杆效应,危机爆发时期股市间的联动性会显著提升;股市间的一体化程度正在加深,韩国是风险输入国,中国和日本是风险输出国。为了维护金融稳定,除了进一步加强宏观审慎监管,各国应协同合作,构建区域统一、协调的风险防范机制。

基本养老保险基金投资、股票收益率与股价波动性

以2017—2022年各季度基本养老保险基金投资数据为样本,考察基本养老保险基金投资对股票收益率与股价波动性的影响。结果显示:基本养老保险基金持股比例变化对股票未来收益率有一定预测效应,持股比例增加会加剧股价波动。异质性检验表明,被持股公司规模越大,持股比例变化对股票收益率的影响越不明显,对股价波动性影响的时滞性越强。对于短期持股而言,持股比例增加会加剧股价波动,而对于长期持股而言,持股比例增加有利于稳定股价。鉴于此,应继续推动基本养老保险基金全国统筹,进一步扩大其市场化投资规模、延长投资考核期限,提升基金可持续发展能力。