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Given the dearth of thorough summaries in the literature, this systematic review and bibliometric analysis attempt to take a meticulous approach meant to present knowledge on the constantly developing subject of stock market volatility during crises. In outline, this study aims to map the extant literature available on stock market volatility during crisis periods.

The present study reviews 1,283 journal articles from the Scopus database published between 1994 and 2022, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow diagram. Bibliometric analysis through software like R studio and VOSviewer has been performed, that is, annual publication trend analysis, journal analysis, citation analysis, author influence analysis, analysis of affiliations, analysis of countries and regions, keyword analysis, thematic mapping, co-occurrence analysis, bibliographic coupling, co-citation analysis, Bradford’s law and Lotka’s law, to map the existing literature and identify the gaps.

The literature on the effects of crises on volatility in financial markets has grown in recent years. It was discovered that volatility intensified during crises. This increased volatility can be linked to COVID-19 and the global financial crisis of 2008, as both had massive effects on the world economy. Moreover, we identify specific patterns and factors contributing to increased volatility, providing valuable insights for further research and decision-making.

The present study is confined to the areas of economics, econometrics and finance, business, management and accounting and social sciences. Future studies could be conducted considering a broader perspective.

Most of the available literature has focused on the impact of some particular crises on the volatility of financial markets. The present study is not limited to some specific crises, and the suggested research directions will serve as a guide for future research.

The current study aims to examine the impact of two black swan events on the performance of six stock markets in Gulf Cooperation Council (GCC) economies (Abu Dhabi, Bahrain, Dubai, Oman, Qatar and Saudi Arabia). The two selected black swan events are the US Mortgage and credit crisis (Global Financial Crisis of 2008) and the COVID-19 pandemic.

The performance of all the six stock markets are represented by their return and price volatility behavior, which has been measured by applying ARCH/GARCH model. The comparative analysis is done by employing mean difference models. The data is collected from Bloomberg on a daily frequency.

The response of two black swan events on the GCC stock markets has been heterogenous in nature. During the financial crisis, the impact was heavily felt on most of the stock markets in the GCC countries. It is revealed that the financial crisis had a negative significant impact on four of the six countries. Whereas during the COVID-19 crisis, it is revealed that there is no significant impact on four of the six selected stock markets. The positive significant impact is felt on two stock markets, namely, the Abu Dhabi stock market and the Saudi stock market.

The present investigation attempts to fill the gap in the literature on the intended topic because it is evident from the literature on the chosen subject that no study has been undertaken to evaluate and contrast the impact of the GFC crisis and COVID-19 on the GCC stock markets.

The COVID-19 outbreak and its confinement resulted in an unexpected stock market crash, hence the interest in environmental, social and governance (hereafter, ESG) policies. This paper aims to examine the association between ESG performance and stock market volatility before and after the COVID-19 pandemic.

This paper examined 500 US companies listed in the S&P 500. The window period volatility refers to March 18, 2020, when the US President signed into law the Families First Coronavirus Response Act. Here, the Thomson Reuters database was used to collect ESG data and daily market information.

The findings suggest that companies with high ESG performance have lower stock price volatility than companies with poor ESG performance. In other words, strong ESG performance reduces stock price volatility resulting from the COVID-19 shock and promotes resilience and stock price stability.

This research contributes to current debates on emerging pandemics and unexpected risks and highlights the need to invest more in improving corporate sustainability.

The results have substantial implications for managers and investors, as it highlights the relevance of customer and investor loyalty to the durability of ESG stocks.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This study aims to examine the impact of institutional investors and their classes on the stock return volatility of an emerging market. The paper also determines the moderating role of firm size, crisis and turnover on such relationships.

The study covers nonfinancial companies of the Bombay Stock Exchange-100 index that are listed during the study period. The study uses fixed effects and systematic generalized method of moments estimators to look over the association between institutional investors and firms’ stock return volatility.

The study provides evidence that institutional investors destabilize the Indian stock market. It indicates that institutional investors do not engage in management activities; they earn short-term gains depending on information efficiency. Pressure-insensitive institutional investors have a significant positive relation with stock return volatility, while pressure-sensitive institutional investors do not. The study also reflects that pressure-sensitive institutional investors are underweighted in India, which jointly represents an insignificant nonlinear association between institutional ownership and stocks’ volatility. Furthermore, outcomes reveal that the intersection effect of the crisis, firm size and turnover is positively and significantly related to such relationships.

The outcomes encourage initiatives that keep track of institutional investors in the Indian stock market. To control the destabilizing effect of pressure-insensitive institutional investors, regulators should follow strict regulations on their trading patterns. Moreover, it guides the potential researchers that they should also take into account the impact of other classes of ownership structure or what type of ownership can help in stabilizing or destabilizing the Indian stock market.

Abundant literature studies the relationship between institutional ownership and firm performance in the Indian context. From the standpoint of making management decisions, the return and volatility of stock returns are both different aspects. However, this study examines the effect of institutional ownership and its groups on the volatility of stock return using the panel data estimator, which was previously not discussed in the literature.

To estimate the volatility of exchange and stock markets and examine its spillover within and across the member countries of BRICS during COVID-19 and the conflict between Russia and Ukraine.

The study utilizes the “dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH)” approach of Gabauer (2020). The volatility of the markets is calculated following the approach of Parkinson (1980). The sample dataset comprises the daily volatility of the stock and exchange markets for 35 months, from November 2019 to September 2022.

The study confirms the existence of contagion effects among member countries. Volatility spillover between exchange and stock markets is low within the country but substantial across borders. Russian contribution increased significantly during the conflict with Ukraine, and other countries also witnessed a surge in the spillover index during the pandemic and war.

It adds to the body of literature by emphasizing the necessity of comprehending the economies' behavior and interdependence. Offers insightful information to decision-makers who must be more watchful regarding the financial crisis and its regional spillover.

The study is the first to explore the contagion of volatility among the BRICS countries during the two biggest crisis periods of the decade.

The authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).

The authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.

According to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.

The authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.

This study examines the information transmission (return and volatility spillovers) among energy commodities (crude oil, natural gas, Brent oil, heating oil, gasoil, gasoline) and Asian stock markets which are net importers of energy (China, India, Indonesia, Malaysia, Korea, Pakistan, Philippines, Taiwan, Thailand).

The information transmission is investigated by employing the spillover index of Diebold and Yilmaz, using daily data for the period January 2000 to May 2021.

A Strong connectedness is documented between the two classes of asset, especially during crisis periods. Our findings reveal that most of the energy markets, except gasoil and natural gas, are net transmitters of information, whereas all the stock markets, excluding Indonesia and Korea, are net recipients.

The findings are helpful for portfolio managers and institutional investors allocating funds to various asset classes in times of crisis.

All data is original.

This paper aims to examine the dynamic spillover effects and network connectedness between the oil prices and the Islamic and conventional financial markets in the Gulf Cooperation Council countries. The focus is on network connectedness during the 2008–2009 global financial crisis, the 2014–2016 oil crisis and the COVID-19 pandemic. The authors use daily data covering the period from January 1, 2007 to April 14, 2022.

This study applies a spillover analysis and connectedness network to investigate the risk contagion among the Islamic and conventional stock–bond markets. The authors rely on Diebold and Yilmaz’s (2012, 2014) methodology to construct network-associated measures.

The results suggest that overall connectedness among financial market uncertainties increased during the global financial crisis, the oil price collapse of 2014–2016 and the COVID-19 crisis. In addition, the authors show that the contribution of oil shocks to the financial system is limited, as the oil market was a net receiver during the 2014 oil shock and the COVID-19 crisis. On the other hand, the Islamic and conventional stock markets are extensive sources of network effects on the oil market and Islamic and conventional bond markets. Furthermore, the authors found that the Sukuk market was significantly affected by the COVID-19 pandemic, whereas the conventional and Islamic stock markets were the highest transmitters of shocks during the COVID-19 pandemic outbreak. Moreover, oil revealed a weak connectedness with the Islamic and conventional stock markets during the COVID-19 health crisis, implying that it helps provide diversification benefits for international portfolio investors.

This study contributes to this field by improving the understanding of the effect of fluctuations in oil prices on the dynamics of the volatility connection between oil and Islamic and conventional financial markets during times of stress through a network connectedness framework. The main results of this study highlight the role of oil in portfolio allocation and risk minimization when investing in Islamic and conventional assets.

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.