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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.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

This paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.

The connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.

This analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.

The connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

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.

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 purpose of this study is twofold: first, to examine the relationship between foreign ownership and stock return volatility and second, to explore how COVID-19 impacts such a relationship.

This empirical research is based on the non-financial firms of the BSE-100 index over the 2013–2022 period. The ordinary least squares, fixed effects and system GMM (Generalized method of moment) techniques are used to analyze the effect of oversea investors on stock return volatility.

Results indicate an inverse association between foreign ownership and stock return volatility. The outcomes of the pre-and during-COVID-19 period show a negative but insignificant relationship between foreign ownership and stock return volatility. These results reflect foreign investors sold their stocks pessimistically, which badly affected the Indian stock market.

This study enriches the previous literature by exploring the impact of foreign investors on the stock return volatility of Indian firms. To date, no study has captured the impact of foreign ownership on stock return volatility during the COVID-19 pandemic.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0179

Prior studies use two measures of firm-specific return variation (FSRV): idiosyncratic volatility in absolute and relative terms, the latter of which is also termed stock price nonsynchronicity. Whereas most research focuses on investigating the idiosyncratic volatility puzzle, the authors carry out comparison of these two measures and further investigate which of the two constituents of nonsynchronicity explain the association between FSRV and stock returns, emphasising the importance of assessing which component drives stock returns.

The authors use the US individual stock returns from 1925 to 2016 and define the two measures of FRSV based on the Fama and French (1993) model. Specifically, the authors decompose the relative measure into two components: (i) absolute idiosyncratic volatility and (ii) systematic volatility. The authors conduct various tests based on high-minus-low, zero-investment quintile portfolio sorts and perform the Fama–MacBeth analysis by singling out each component.

The authors find a positive return on the portfolio sorted on relative idiosyncratic volatility or on systematic volatility, but find a negative return sorted on absolute idiosyncratic volatility. The results are robust after controlling for size, BM and other risk characteristics using a double-sorting approach. The Fama–MacBeth regression results show that a positive association between the relative measure and stock returns is driven primarily by the low-systematic-volatility anomaly across firms. The findings are robust to controlling for return residual momentum, skewness, jumps and information discreteness.

Extant research posits the idiosyncratic volatility puzzle and the low-volatility anomaly. The authors emphasize the importance of integrating these two streams of research. This study enhances the understanding of the driving force underlying the relationship between FSRV and cross-sectional stock returns.

This study aims to investigate the role of international price volatility and inventories on domestic market price dynamics in the case of agricultural commodities.

A structural model is employed to uncover relationships among commodity price, price volatility, inventories and convenience yield. Monthly producer price data along with annual data on trade, consumption, inventories and tariffs for 71 countries and 13 commodities covering 2010–2019 are assembled to estimate the model. With a first-stage Least Absolute Shrinkage and Selection Operator (LASSO) estimator to identify the best instrument set, a nonlinear approach is used to estimate the model.

Results show that international market information plays a critical role in domestic market price dynamics. International price volatility has a stronger effect on domestic prices than that of international inventories.

Current upheaval in commodity markets requires an understanding of how prices move together and inventories affect that movement. A country's internal price is not independent of the effects of global market events.

Although hypotheses exist that global market information (volatility and inventories) helps countries manage domestic commodity prices, there have been limited studies on this relationship, especially with a structured model and cross-country data.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.