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The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

This study aims to advance knowledge on the direct impact of the investor’s protection level on the stock market volatility, that is, whether investor’s protection is an important stock market volatility determinant.

A panel data was estimated using a sample of 48 countries, from 2006 to 2018, totalizing 31,808 observations. To measure stock market volatility and the investor protection level, a generalized autoregressive conditional heteroskedasticity model and the World Bank Doing Business investor protection index were used, respectively.

The results evidence that the protection of investors’ rights reduces the stock market volatility. This result indicates that a high level of investor protection, which is the result of a better quality of laws and policies in place that protect investor’s rights, promotes the country as a “safe haven.”

The relationship that the authors intend to analyze becomes important, given that investor protection will give outsiders guarantees on the materialization of their investments. This study contributes important knowledge for investors and for the establishment of government policies as a way of attracting investment.

Although there have been a few studies addressing this relationship, to the knowledge, none of them directly analyses the influence of investor protection on the stock market volatility.

The purpose of this paper is to examine whether stock market liberalization creates excess stock return volatility in the Qatar Exchange (QSC).

The study utilizes two methods, simple analysis of variance and the EGARCH model with dummy variables.

Results reveal no change in market volatility following the partial removal of the restrictions on foreign participation. Results suggest, however, that the degree of persistence in volatility is high, which implies that once volatility increases it remains high over a long run. In addition, conditional volatility tends to rise when the absolute value of the standardized residuals was large. While, contrary to what has been found in the literature, the return volatility seems to be symmetric.

The finding of volatility persistence and clustering might imply an inefficient stock market. Therefore, policy makers should emphasize and direct their attention toward increasing the efficiency of the stock market.

Being able to make predictions about financial market volatility is of special importance to investors and policy makers since it makes available to them a measure of risk exposure in their investments and decisions.

This paper provides a contribution to the empirical literature on stock market volatility. It is the only study, to the authors' knowledge, that investigates the issue of QSC liberalization and volatility. The authors believe that QSC has its own unique characteristics, and the results of the study depend mainly on the market's specific conditions, the quality of its financial institutions and the extent of financial liberalization obtained.

It has been argued in the literature that structural changes in the financial markets, such as integration, have the potential to cause herding behavior or correlated behavioral patterns in traders. The purpose of this study is to investigate whether there is any financial herding behavior in the Latin American Integrated Market (MILA), a transnational stock market composed of Chile, Peru, Colombia and Mexico stock exchanges and whether there is any ARCH or GARCH effect in the herding behavior models.

This study uses the modified return dispersion approach on daily index return data. The sample period is from January 03, 2002 to May 07, 2019. The data are obtained from the MILA database. To count time-varying volatilities in herding models, the authors run ARCH family regression with GARCH (1,1) settings. Hwang and Salmon (2004) model is used as a robustness test.

The authors found strong herding behavior under the general market conditions and moderate and partial herding behavior under some specified markets circumstances, such as bull and bear markets and high-low volatility states. Moreover, the pre-MILA period exhibits more herding behavior than the post-MILA period. The empirical results show that most of the ARCH and GARCH effects are statistically significant, implying that the past information of stock returns and market volatility significantly affect the volatility of following periods, which can also explain the formation of herding tendency among investors. Finally, the results of the robustness tests (Hwang and Salmon, 2004) confirm herding in all periods, except full sample period for Mexico and post-MILA period for Mexico and Colombia.

This study investigates the herding behavior in the MILA market in terms of market return, volatility and timing. A limitation of the paper is that the authors have not included other factors on the formation of herding behavior, such as macroeconomic factors, effects of regional or international markets and policy influences. The authors will explore the issue in the extension of the paper.

As MILA is the first virtual integration of stock exchanges without merging, the study provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are useful for academics, investors and policymakers in their investment and decision makings.

The paper provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are not only useful in practical implications, but also in social implications.

This study contributes to the herding literature by examining four different hypotheses in respect of the unique case of transnational stock exchange without fusions or corporate mergers, where each market maintains its independence and regulatory autonomy. The authors also contribute to the literature by including both ARCH and GARCH effects in the herding behavioral models along the Hwang and Salmon (2004) approach.

The focus of the current research is to examine whether mixed-frequency investor sentiment affects stock volatility in the China A-shares stock market.

Mixed-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.

The 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.

Given 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.

A 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 purpose of this study is twofold: to examine the effects of the COVID-19 pandemic on the risk dynamics of stock and bond markets in G7 countries; and to examine if the stock-bond risk dynamics can be linked to government measures to contain the pandemic.

To examine the pandemic impact on the risk dynamics of the bond and stock markets, this study chooses G7 countries for their efficient financial market properties. This study uses standard generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) and exponential GARCH (1,1) models to determine the most volatile and sensitive market, most persistent market during the crisis and the leverage effect between stock and bond markets. This study then uses a panel study to investigate whether this volatility in stock and bond markets is affected by the COVID-19 cases and various government responses (fiscal stimulus packages, monetary policy, emergency investment in health care and vaccine investment).

The findings of the study confirm that the bad news of the pandemic is causing higher volatility than good news for all seven stock markets. Canadian stock and bond markets are the most volatile, and Italian bond and stock markets are the most sensitive G7 countries. Japan has shown the highest persistence, and the stock market exhibits higher leverage than the bond market. Fiscal stimulus packages are helping to reduce bond market volatility, but none of these measures are effective in the stock market.

The pandemic is still spreading, and the rate at which it spreads wildly will always pose a limitation to any attempt to examine its full effect.

Investigation of market volatility will help policymakers and market players formulate the best strategies to overcome and exit the crisis and plan post-pandemic solutions. It provides valuable insights for investors to rebalance their portfolios during highly volatile markets while preserving their risk appetite and investment objectives.

The paper provides evidence on the impact of the pandemic-induced crisis and the respective government responses on the volatility of competing capital markets (stock and bond) in countries that are considered most efficient in reflecting news.

Volatility is a permanent behavior of the stock market around the globe. The presence of the volatility in the stock price makes it possible to earn abnormal profits by risk seeking investors and creates hesitancy among risk averse investors as high volatility means high return with high risk. Investors always consider market volatility before making any investment decisions. Random fluctuations are termed as volatility of stock market. Volatility in financial markets is reflected because of uncertainty in the price and return, unexpected events and non-constant variance that can be measured through the generalized autoregressive conditional heteroscedasticity family models and that will give an insight for investment decision-making.

Daily data of the closing value of Bombay Stock Exchange (BSE) (Sensex) and National Stock Exchange (NSE) (Nifty) from April 1, 2011 to March 31, 2017 is collected through the web-portal of BSE (www.bseindia.com) and NSE (www.nseindia.com) for the analysis purpose.

The outcome of the study suggested that P-GARCH model is most suitable to predict and forecast the stock market volatility for both the markets.

Future research can be extended to other stock market segments and sectoral indices to explore and forecast the volatility to establish a trade-off between risk and return.

The results of previous studies available are not conducive to this research, and very limited scholarly work is available in the Indian context, so required to be re-explored to identify the appropriate model to predict market volatility.

The stock market has shown fluctuating degrees of volatility because of the recent COVID-19 pandemic in India. The present research aims to investigate the effect of the COVID-19 on the stock market volatility, and whether the economic package can control the market volatility or not, measured by a set of certain sector-level economic features and factors such as resilience variables.

We examine the correlation matrix, basic volatility model and robustness tests to determine the sector-level economic features and macroeconomic factors helpful in diminishing the volatility rising because of the COVID-19.

The outcomes of this study are significant as policymakers and financial analysts can apply these economic factors to set policy replies to handle the unexpected fluctuation in the stock market in sequence to circumvent any thinkable future financial crisis.

The originality of the paper is to measure the variables affecting the stock market volatility due to COVID-19, and understand the impact of capital market macroeconomic variables and dummy variables to theoretically explain the COVID-19 impact on stock market volatility.

The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times.

The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries.

The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

This paper explored the new features of emerging stock markets, in order to point out the most associated indicators of increasing stock return volatility, which may lead to instability of emerging markets. The study covers a sample of five geographical areas of emerging economies, including Mexico, Korea, South Africa, Turkey, and Malaysia. It used the backward multiple‐regression technique to examine the relationship between monthly changes of stock price indices as dependent variable and the associated predicting local as well as international variables, which represent possible causes of increasing price volatility and initiating crises in emerging stock markets. The study covered monthly data for a period of forty‐eight months from January 1997 to December 2000. The study revealed that stock trading volume and currency exchange rate respectively represent the highest positive correlation to the emerging stock price changes; thus represent the most predicting variables of increasing price volatility. International stock price index, deposit interest rate, and bond trading volume were moderate predicting variables for emerging stock price volatility. While changes in inflation rate showed the least positive correlation to stock price volatility, thus represents the least predicting variable.