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Stock Market Volatility and Return Analysis: A Systematic Literature Review

Roni bhowmik.

1 School of Economics and Management, Jiujiang University, Jiujiang 322227, China

2 Department of Business Administration, Daffodil International University, Dhaka 1207, Bangladesh

Shouyang Wang

3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China; nc.ca.ssma@gnawys

In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

1. Introduction

In the context of economic globalization, especially after the impact of the contemporary international financial crisis, the stock market has experienced unprecedented fluctuations. This volatility increases the uncertainty and risk of the stock market and is detrimental to the normal operation of the stock market. To reduce this uncertainty, it is particularly important to measure accurately the volatility of stock index returns. At the same time, due to the important position of the stock market in the global economy, the beneficial development of the stock market has become the focus. Therefore, the knowledge of theoretical and literature significance of volatility are needed to measure the volatility of stock index returns.

Volatility is a hot issue in economic and financial research. Volatility is one of the most important characteristics of financial markets. It is directly related to market uncertainty and affects the investment behavior of enterprises and individuals. A study of the volatility of financial asset returns is also one of the core issues in modern financial research and this volatility is often described and measured by the variance of the rate of return. However, forecasting perfect market volatility is difficult work and despite the availability of various models and techniques, not all of them work equally for all stock markets. It is for this reason that researchers and financial analysts face such a complexity in market returns and volatilities forecasting.

The traditional econometric model often assumes that the variance is constant, that is, the variance is kept constant at different times. An accurate measurement of the rate of return’s fluctuation is directly related to the correctness of portfolio selection, the effectiveness of risk management, and the rationality of asset pricing. However, with the development of financial theory and the deepening of empirical research, it was found that this assumption is not reasonable. Additionally, the volatility of asset prices is one of the most puzzling phenomena in financial economics. It is a great challenge for investors to get a pure understanding of volatility.

A literature reviews act as a significant part of all kinds of research work. Literature reviews serve as a foundation for knowledge progress, make guidelines for plan and practice, provide grounds of an effect, and, if well guided, have the capacity to create new ideas and directions for a particular area [ 1 ]. Similarly, they carry out as the basis for future research and theory work. This paper conducts a literature review of stock returns and volatility analysis based on generalized autoregressive conditional heteroskedastic (GARCH) family models. Volatility refers to the degree of dispersion of random variables.

Financial market volatility is mainly reflected in the deviation of the expected future value of assets. The possibility, that is, volatility, represents the uncertainty of the future price of an asset. This uncertainty is usually characterized by variance or standard deviation. There are currently two main explanations in the academic world for the relationship between these two: The leverage effect and the volatility feedback hypothesis. Leverage often means that unfavorable news appears, stock price falls, leading to an increase in the leverage factor, and thus the degree of stock volatility increases. Conversely, the degree of volatility weakens; volatility feedback can be simply described as unpredictable stock volatility that will inevitably lead to higher risk in the future.

There are many factors that affect price movements in the stock market. Firstly, there is the impact of monetary policy on the stock market, which is extremely substantial. If a loose monetary policy is implemented in a year, the probability of a stock market index rise will increase. On the other hand, if a relatively tight monetary policy is implemented in a year, the probability of a stock market index decline will increase. Secondly, there is the impact of interest rate liberalization on risk-free interest rates. Looking at the major global capital markets, the change in risk-free interest rates has a greater correlation with the current stock market. In general, when interest rates continue to rise, the risk-free interest rate will rise, and the cost of capital invested in the stock market will rise simultaneously. As a result, the economy is expected to gradually pick up during the release of the reform dividend, and the stock market is expected to achieve a higher return on investment.

Volatility is the tendency for prices to change unexpectedly [ 2 ], however, all kinds of volatility is not bad. At the same time, financial market volatility has also a direct impact on macroeconomic and financial stability. Important economic risk factors are generally highly valued by governments around the world. Therefore, research on the volatility of financial markets has always been the focus of financial economists and financial practitioners. Nowadays, a large part of the literature has studied some characteristics of the stock market, such as the leverage effect of volatility, the short-term memory of volatility, and the GARCH effect, etc., but some researchers show that when adopting short-term memory by the GARCH model, there is usually a confusing phenomenon, as the sampling interval tends to zero. The characterization of the tail of the yield generally assumes an ideal situation, that is, obeys the normal distribution, but this perfect situation is usually not established.

Researchers have proposed different distributed models in order to better describe the thick tail of the daily rate of return. Engle [ 3 ] first proposed an autoregressive conditional heteroscedasticity model (ARCH model) to characterize some possible correlations of the conditional variance of the prediction error. Bollerslev [ 4 ] has been extended it to form a generalized autoregressive conditional heteroskedastic model (GARCH model). Later, the GARCH model rapidly expanded and a GARCH family model was created.

When employing GARCH family models to analyze and forecast return volatility, selection of input variables for forecasting is crucial as the appropriate and essential condition will be given for the method to have a stationary solution and perfect matching [ 5 ]. It has been shown in several findings that the unchanged model can produce suggestively different results when it is consumed with different inputs. Thus, another key purpose of this literature review is to observe studies which use directional prediction accuracy model as a yardstick from a realistic point of understanding and has the core objective of the forecast of financial time series in stock market return. Researchers estimate little forecast error, namely measured as mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) which do not essentially interpret into capital gain [ 6 , 7 ]. Some others mention that the predictions are not required to be precise in terms of NMSE (normalized mean squared error) [ 8 ]. It means that finding the low rate of root mean squared error does not feed high returns, in another words, the relationship is not linear between two.

In this manuscript, it is proposed to categorize the studies not only by their model selection standards but also for the inputs used for the return volatility as well as how precise it is spending them in terms of return directions. In this investigation, the authors repute studies which use percentage of success trades benchmark procedures for analyzing the researchers’ proposed models. From this theme, this study’s authentic approach is compared with earlier models in the literature review for input variables used for forecasting volatility and how precise they are in analyzing the direction of the related time series. There are other review studies on return and volatility analysis and GARCH-family based financial forecasting methods done by a number of researchers [ 9 , 10 , 11 , 12 , 13 ]. Consequently, the aim of this manuscript is to put forward the importance of sufficient and necessary conditions for model selection and contribute for the better understanding of academic researchers and financial practitioners.

Systematic reviews have most notable been expanded by medical science as a way to synthesize research recognition in a systematic, transparent, and reproducible process. Despite the opportunity of this technique, its exercise has not been overly widespread in business research, but it is expanding day by day. In this paper, the authors have used the systematic review process because the target of a systematic review is to determine all empirical indication that fits the pre-decided inclusion criteria or standard of response to a certain research question. Researchers proved that GARCH is the most suitable model to use when one has to analysis the volatility of the returns of stocks with big volumes of observations [ 3 , 4 , 6 , 9 , 13 ]. Researchers observe keenly all the selected literature to answer the following research question: What are the effective GARCH models to recommend for performing market volatility and return analysis?

The main contribution of this paper is found in the following four aspects: (1) The best GARCH models can be recommended for stock market returns and volatilities evaluation. (2) The manuscript considers recent papers, 2008 to 2019, which have not been covered in previous studies. (3) In this study, both qualitative and quantitative processes have been used to examine the literature involving stock returns and volatilities. (4) The manuscript provides a study based on journals that will help academics and researchers recognize important journals that they can denote for a literature review, recognize factors motivating analysis stock returns and volatilities, and can publish their worth study manuscripts.

2. Methodology

A systematic literature examination of databases should recognize as complete a list as possible of relevant literature while keeping the number of irrelevant knocks small. The study is conducted by a systematic based literature review, following suggestions from scholars [ 14 , 15 ]. This manuscript was led by a systematic database search, surveyed by cross-reference snowballing, as demonstrated in Figure 1 , which was adapted from Geissdoerfer et al. [ 16 ]. Two databases were selected for the literature search: Scopus and Web-of-Science. These databases were preferred as they have some major depositories of research and are usually used in literature reviews for business research [ 17 ].

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Literature review method.

At first stage, a systematic literature search is managed. The keywords that were too broad or likely to be recognized in literature-related keywords with other research areas are specified below. As shown in Table 1 , the search string “market return” in ‘Title‘ respectively “stock market return”, “stock market volatility”, “stock market return volatility”, “GARCH family model* for stock return”, “forecasting stock return”, and GARCH model*, “financial market return and volatility” in ‘Topic’ separately ‘Article title, Abstract, Keywords’ were used to search for reviews of articles in English on the Elsevier Scopus and Thomson Reuters Web-of-Science databases. The asterisk (*) is a commonly used wildcard symbol that broadens a search by finding words that start with the same letters.

Literature search strings for database.

At second stage, suitable cross-references were recognized in this primary sample by first examining the publications’ title in the reference portion and their context and cited content in the text. The abstracts of the recognized further publications were examined to determine whether the paper was appropriate or not. Appropriate references were consequently added to the sample and analogously scanned for appropriate cross-references. This method was continual until no additional appropriate cross-references could be recognized.

At the third stage, the ultimate sample was assimilated, synthesized, and compiled into the literature review presented in the subsequent section. The method was revised a few days before the submission.

Additionally, the list of affiliation criteria in Table 2 , which is formed on discussions of the authors, with the summaries of all research papers were independently checked in a blind system method. Evaluations were established on the content of the abstract, with any extra information unseen, and were comprehensive rather than exclusive. In order to check for inter-coder dependability, an initial sample of 30 abstracts were studied for affiliation by the authors. If the abstract was not satisfactorily enough, the whole paper was studied. Simply, 4.61 percent of the abstract resulted in variance between the researchers. The above-mentioned stages reduced the subsequent number of full papers for examination and synthesis to 50. In order to recognize magnitudes, backgrounds, and moderators, these residual research papers were reviewed in two rounds of reading.

Affiliation criteria.

3. Review of Different Studies

In this paper, a large amount of articles were studied but only a few were well thought out to gather the quality developed earlier. For every published article, three groups were specified. Those groups were considered as index and forecast time period, input elements, econometric models, and study results. The first group namely “index and forecast time period with input elements” was considered since market situation like emerging, frontier, and developed markets which are important parameters of forecast and also the length of evaluation is a necessary characteristic for examining the robustness of the model. Furthermore, input elements are comparatively essential parameters for a forecast model because the analytical and diagnostic ability of the model is mainly supported on the inputs that a variable uses. In the second group, “model” was considered forecast models proposed by authors and other models for assessment. The last group is important to our examination for comparing studies in relationships of proper guiding return and volatility, acquired by using recommended estimate models, named the “study results” group.

Measuring the stock market volatility is an incredibly complex job for researchers. Since volatility tends to cluster, if today’s volatility is high, it is likely to be high tomorrow but they have also had an attractive high hit rate with major disasters [ 4 , 7 , 11 , 12 ]. GARCH models have a strong background, recently having crossed 30 years of the fast progress of GARCH-type models for investigating the volatility of market data. Literature of eligible papers were clustered in two sub groups, the first group containing GARCH and its variations model, and the second group containing bivariate and other multivariate GARCH models, summarized in a table format for future studies. Table 3 explains the review of GARCH and its variations models. The univariate GARCH model is for a single time series. It is a statistical model that is used to analyze a number of different kinds of financial data. Financial institutions and researchers usually use this model to estimate the volatility of returns for stocks, bonds, and market indices. In the GARCH model, current volatility is influenced by past innovation to volatility. GARCH models are used to model for forecast volatility of one time series. The most widely used GARCH form is GARCH (1, 1) and this has some extensions.

Different literature studies based on generalized autoregressive conditional heteroskedastic (GARCH) and its variations models.

Notes: APARCH (Asymmetric Power ARCH), AIC (Akaike Information Criterion), OHLC (Open-High-Low-Close Chart), NSE (National Stock Exchange of India), EWMA (Exponentially Weighted Moving Average), CGARCH (Component GARCH), BDS (Brock, Dechert & Scheinkman) Test, ARCH-LM (ARCH-Lagrange Multiplier) test, VAR (Vector Autoregression) model, VEC (Vector Error Correction) model, ARFIMA (Autoregressive Fractional Integral Moving Average), FIGARCH (Fractionally Integrated GARCH), SHCI (Shanghai Stock Exchange Composite Index), SZCI (Shenzhen Stock Exchange Component Index), ADF (Augmented Dickey–Fuller) test, BSE (Bombay Stock Exchange), and PGARCH (Periodic GARCH) are discussed.

In a simple GARCH model, the squared volatility σ t 2 is allowed to change on previous squared volatilities, as well as previous squared values of the process. The conditional variance satisfies the following form: σ t 2 = α 0 + α 1 ϵ t − 1 2 + … + α q ϵ t − q 2 + β 1 σ t − 1 2 + … + β p σ t − p 2 where, α i > 0 and β i > 0 . For the GARCH model, residuals’ lags can substitute by a limited number of lags of conditional variances, which abridges the lag structure and in addition the estimation method of coefficients. The most often used GARCH model is the GARCH (1, 1) model. The GARCH (1, 1) process is a covariance-stationary white noise process if and only if α 1 + β < 1 . The variance of the covariance-stationary process is given by α 1   /   ( 1 − α 1 − β ) . It specifies that σ n 2     is based on the most recent observation of φ t 2   and the most recent variance rate σ n − 1 2 . The GARCH (1, 1) model can be written as σ n 2 = ω + α φ n − 1 2 + β σ n − 1 2 and this is usually used for the estimation of parameters in the univariate case.

Though, GARCH model is not a complete model, and thus could be developed, these developments are detected in the form of the alphabet soup that uses GARCH as its key component. There are various additions of the standard GARCH family models. Nonlinear GARCH (NGARCH) was proposed by Engle and Ng [ 18 ]. The conditional covariance equation is in the form: σ t 2 = γ + α ( ε t − 1 − ϑ σ t − 1   ) 2 + β σ t − 1 2 , where α ,   β ,   γ > 0 . The integrated GARCH (IGARCH) is a restricted version of the GARCH model, where the sum of all the parameters sum up to one and this model was introduced by Engle and Bollerslev [ 19 ]. Its phenomenon might be caused by random level shifts in volatility. The simple GARCH model fails in describing the “leverage effects” which are detected in the financial time series data. The exponential GARCH (EGARCH) introduced by Nelson [ 5 ] is to model the logarithm of the variance rather than the level and this model accounts for an asymmetric response to a shock. The GARCH-in-mean (GARCH-M) model adds a heteroskedasticity term into the mean equation and was introduced by Engle et al. [ 20 ]. The quadratic GARCH (QGARCH) model can handle asymmetric effects of positive and negative shocks and this model was introduced by Sentana [ 21 ]. The Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model was introduced by Glosten et al. [ 22 ], its opposite effects of negative and positive shocks taking into account the leverage fact. The threshold GARCH (TGARCH) model was introduced by Zakoian [ 23 ], this model is also commonly used to handle leverage effects of good news and bad news on volatility. The family GARCH (FGARCH) model was introduced by Hentschel [ 24 ] and is an omnibus model that is a mix of other symmetric or asymmetric GARCH models. The COGARCH model was introduced by Klüppelberg et al. [ 25 ] and is actually the stochastic volatility model, being an extension of the GARCH time series concept to continuous time. The power-transformed and threshold GARCH (PTTGARCH) model was introduced by Pan et al. [ 26 ], this model is a very flexible model and, under certain conditions, includes several ARCH/GARCH models.

Based on the researchers’ articles, the symmetric GARCH (1, 1) model has been used widely to forecast the unconditional volatility in the stock market and time series data, and has been able to simulate the asset yield structure and implied volatility structure. Most researchers show that GARCH (1, 1) with a generalized distribution of residual has more advantages in volatility assessment than other models. Conversely, the asymmetry influence in stock market volatility and return analysis was beyond the descriptive power of the asymmetric GARCH models, as the models could capture more specifics. Besides, the asymmetric GARCH models can incompletely measure the effect of positive or negative shocks in stock market return and volatility, and the GARCH (1, 1) comparatively failed to accomplish this fact. In asymmetric effect, the GJR-GARCH model performed better and produced a higher predictable conditional variance during the period of high volatility. In addition, among the asymmetric GARCH models, the reflection of EGARCH model appeared to be superior.

Table 4 has explained the review of bivariate and other multivariate GARCH models. Bivariate model analysis was used to find out if there is a relationship between two different variables. Bivariate model uses one dependent variable and one independent variable. Additionally, the Multivariate GARCH model is a model for two or more time series. Multivariate GARCH models are used to model for forecast volatility of several time series when there are some linkages between them. Multivariate model uses one dependent variable and more than one independent variable. In this case, the current volatility of one time series is influenced not only by its own past innovation, but also by past innovations to volatilities of other time series.

Different literature studies based on bivariate and other multivariate GARCH models.

The most recognizable use of multivariate GARCH models is the analysis of the relations between the volatilities and co-volatilities of several markets. A multivariate model would create a more dependable model than separate univariate models. The vector error correction (VEC) models is the first MGARCH model which was introduced by Bollerslev et al. [ 66 ]. This model is typically related to subsequent formulations. The model can be expressed in the following form: v e c h   ( H t ) = ℂ + ∑ j = 1 q X j   v e c h   ( ϵ t − j   ϵ t − j ' ) + ∑ j = 1 p Y j   v e c h   ( H t − j   )   where v e c h is an operator that stacks the columns of the lower triangular part of its argument square matrix and H t is the covariance matrix of the residuals. The regulated version of the VEC model is the DVEC model and was also recommended by Bollerslev et al. [ 66 ]. Compared to the VEC model, the estimation method proceeded far more smoothly in the DVEC model. The Baba-Engle-Kraft-Kroner (BEKK) model was introduced by Baba et al. [ 67 ] and is an innovative parameterization of the conditional variance matrix H t . The BEKK model accomplishes the positive assurance of the conditional covariance by conveying the model in a way that this property is implied by the model structure. The Constant Conditional Correlation (CCC) model was recommended by Bollerslev [ 68 ], to primarily model the conditional covariance matrix circuitously by estimating the conditional correlation matrix. The Dynamic Conditional Correlation (DCC) model was introduced by Engle [ 69 ] and is a nonlinear mixture of univariate GARCH models and also a generalized variety of the CCC model. To overcome the inconveniency of huge number of parameters, the O-GARCH model was recommended by Alexander and Chibumba [ 70 ] and consequently developed by Alexander [ 71 , 72 ]. Furthermore, a multivariate GARCH model GO-GARCH model was introduced by Bauwens et al. [ 73 ].

The bivariate models showed achieve better in most cases, compared with the univariate models [ 85 ]. MGARCH models could be used for forecasting. Multivariate GARCH modeling delivered a realistic but parsimonious measurement of the variance matrix, confirming its positivity. However, by analyzing the relative forecasting accuracy of the two formulations, BEKK and DCC, it could be deduced that the forecasting performance of the MGARCH models was not always satisfactory. By comparing it with the other multivariate GARCH models, BEKK-GARCH model was comparatively better and flexible but it needed too many parameters for multiple time series. Conversely, for the area of forecasting, the DCC-GARCH model was more parsimonious. In this regard, it was significantly essential to balance parsimony and flexibility when modeling multivariate GARCH models.

The current systematic review has identified 50 research articles for studies on significant aspects of stock market return and volatility, review types, and GARCH model analysis. This paper noticed that all the studies in this review used an investigational research method. A literature review is necessary for scholars, academics, and practitioners. However, assessing various kinds of literature reviews can be challenging. There is no use for outstanding and demanding literature review articles, since if they do not provide a sufficient contribution and something that is recent, it will not be published. Too often, literature reviews are fairly descriptive overviews of research carried out among particular years that draw data on the number of articles published, subject matter covered, authors represented, and maybe methods used, without conducting a deeper investigation. However, conducting a literature review and examining its standard can be challenging, for this reason, this article provides some rigorous literature reviews and, in the long run, to provide better research.

4. Conclusions

Working on a literature review is a challenge. This paper presents a comprehensive literature which has mainly focused on studies on return and volatility of stock market using systematic review methods on various financial markets around the world. This review was driven by researchers’ available recommendations for accompanying systematic literature reviews to search, examine, and categorize all existing and accessible literature on market volatility and returns [ 16 ]. Out of the 435 initial research articles located in renowned electronic databases, 50 appropriate research articles were extracted through cross-reference snowballing. These research articles were evaluated for the quality of proof they produced and were further examined. The raw data were offered by the authors from the literature together with explanations of the data and key fundamental concepts. The outcomes, in this research, delivered future magnitudes to research experts for further work on the return and volatility of stock market.

Stock market return and volatility analysis is a relatively important and emerging field of research. There has been plenty of research on financial market volatility and return because of easily increasing accessibility and availability of researchable data and computing capability. The GARCH type models have a good model on stock market volatilities and returns investigation. The popularity of various GARCH family models has increased in recent times. Every model has its specific strengths and weaknesses and has at influence such a large number of GARCH models. To sum up the reviewed papers, many scholars suggest that the GARCH family model provides better results combined with another statistical technique. Based on the study, much of the research showed that with symmetric information, GARCH (1, 1) could precisely explain the volatilities and returns of the data and when under conditions of asymmetric information, the asymmetric GARCH models would be more appropriate [ 7 , 32 , 40 , 47 , 48 ]. Additionally, few researchers have used multivariate GARCH model statistical techniques for analyzing market volatility and returns to show that a more accurate and better results can be found by multivariate GARCH family models. Asymmetric GARCH models, for instance and like, EGARCH, GJR GARCH, and TGARCH, etc. have been introduced to capture the effect of bad news on the change in volatility of stock returns [ 42 , 58 , 62 ]. This study, although short and particular, attempted to give the scholar a concept of different methods found in this systematic literature review.

With respect to assessing scholars’ articles, the finding was that rankings and specifically only one GARCH model was sensitive to the different stock market volatilities and returns analysis, because the stock market does not have similar characteristics. For this reason, the stock market and model choice are little bit difficult and display little sensitivity to the ranking criterion and estimation methodology, additionally applying software is also another matter. The key challenge for researchers is finding the characteristics in stock market summarization using different kinds of local stock market returns, volatility detection, world stock market volatility, returns, and other data. Additional challenges are modeled by differences of expression between different languages. From an investigation perception, it has been detected that different authors and researchers use special datasets for the valuation of their methods, which may put boundary assessments between research papers.

Whenever there is assurance that scholars build on high accuracy, it will be easier to recognize genuine research gaps instead of merely conducting the same research again and again, so as to progress better and create more appropriate hypotheses and research questions, and, consequently, to raise the standard of research for future generation. This study will be beneficial for researchers, scholars, stock exchanges, regulators, governments, investors, and other concerned parties. The current study also contributes to the scope of further research in the area of stock volatility and returns. The content analysis can be executed taking the literature of the last few decades. It determined that a lot of methodologies like GARCH models, Johansen models, VECM, Impulse response functions, and Granger causality tests are practiced broadly in examining stock market volatility and return analysis across countries as well as among sectors with in a country.

Author Contributions

R.B. and S.W. proposed the research framework together. R.B. collected the data, and wrote the document. S.W. provided important guidance and advice during the process of this research. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Journal of Modelling in Management

ISSN : 1746-5664

Article publication date: 14 November 2023

Issue publication date: 13 March 2024

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.

  • Volatility spillover
  • Stock market
  • Diebold Yilmaz


Funding : The present research received no specific grant from any of the funding agencies in the public, private or non-profit sectors.

Conflict of interest : The authors declare that there is no conflict of interest.

Dhingra, B. , Batra, S. , Aggarwal, V. , Yadav, M. and Kumar, P. (2024), "Stock market volatility: a systematic review", Journal of Modelling in Management , Vol. 19 No. 3, pp. 925-952. https://doi.org/10.1108/JM2-04-2023-0080

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News sentiment and stock market volatility

  • Original Research
  • Published: 16 March 2021
  • Volume 57 , pages 1093–1122, ( 2021 )

Cite this article

stock market volatility research paper

  • Yen-Ju Hsu   ORCID: orcid.org/0000-0003-4840-6926 1 , 2 ,
  • Yang-Cheng Lu 3 &
  • J. Jimmy Yang 4  

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This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation process to quantify financial news that may attract investor attention. This study distinguishes between unclassified market news sentiment and macroeconomic-related news effects. The evidence suggests that both contemporaneous and lagged news are determinants of market volatility. The effect is especially strong with the market aggregate news sentiment index ( ANSI ) and the negative ANSI , particularly during the 2008–2009 financial crisis period. This analysis of news sentiment improves the accuracy of in-sample and out-of-sample volatility forecasting.

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Examples include Chan ( 2003 ), Vega ( 2006 ), Tetlock ( 2007 ), Fang and Peress ( 2009 ), Engelberg and Parsons ( 2011 ), Griffin et al. ( 2011 ), Groß-Klußmann and Hautsch ( 2011 ), and Demers and Vega ( 2014 ).

Gilbert ( 2011 ) documents that macroeconomic announcement revisions also have a strong relationship with stock market indexes.

Examples include Savor and Wilson ( 2013 ), Baker et al. ( 2016 ), Heston and Sinha ( 2016 ) and Fisher et al. ( 2018 ).

In our research period, the 7% upward and 7% downward price limits of daily stock trading were applied from 2007 to May 2015. The price limits were widened from 7 to 10% on June 1, 2015. Please refer to the TWSE’s website for more detailed information on the Taiwan stock market. http://www.tse.com.tw/en/

We also examine news sentiment indexes across high- and low-beta firms. Our results reveal that the news sentiment effect on market volatility is stronger for high-beta firms than it is for low-beta firms. However, the forecasting errors are rather high for both firm types. The results are available upon request.

In addition to the reported variables, we consider the interaction of firm-specific and macroeconomic news sentiment in both the GARCH and GJR-GARCH models. However, only the interaction terms of NegANSI and NegNSCat reveal marginally positive effects on market volatility. The in-sample and out-of-sample forecasting results also exhibit a high degree of measurement error in the volatility models with the interaction term. These results are available upon request.

We thank  an  anonymous reviewer  for suggesting the performance comparison between our models and existing models in the literature.

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We thank Cheng-Few Lee (the editor), anonymous referee, Yanzhi Wang, Kuang-Chien Yen, and participants at the 2019 World Finance Conference in Santiago. This work was financially supported by the Center for Research in Econometric Theory and Applications (Grant no. 109L900202) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and by the Ministry of Science and Technology (MOST) of Taiwan, under Grant Nos. MOST 109-2634-F-002-045, MOST 108-2410-H-030-086 and MOST 109-2410-H-030-020.

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Hsu, YJ., Lu, YC. & Yang, J.J. News sentiment and stock market volatility. Rev Quant Finan Acc 57 , 1093–1122 (2021). https://doi.org/10.1007/s11156-021-00971-8

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Issue Date : October 2021

DOI : https://doi.org/10.1007/s11156-021-00971-8

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Us equities, fixed income, bonds what is the vix – when volatility spikes think quality bonds.

Over time, the VIX has earned the moniker of “fear index”. In this note we provide a brief explainer on the VIX, discuss some of the recent market volatility, and highlight the importance of quality bonds in times of turmoil.

stock market volatility research paper

The VIX is an index created by the Chicago Board Options Exchange (CBOE), used as a thermometer of market sentiment and volatility. The more intense is perceived volatility, the greater the VIX – or “fear”. (UBS)

What is the VIX exactly? – how to read it?

As per CBOE, it is “a financial benchmark designed to be an up-to-minute market estimate of the expected volatility in the S&P 500 Index, and is calculated by using the midpoint of real time S&P 500 option bid/ask quotes.”

More specifically, the VIX Index is a calculation designed to produce a measure of constant, 30-day expected volatility of the US stock market. The headline VIX figure is derived from prices of S&P 500 call and put options. When there is rising demand for call and put options – which partly serve to hedge risks – the price of options increase, and thus the VIX rises. When the urge to hedge risks is low, there’s no out-of-the-ordinary pressure in option prices, and hence VIX levels are rather contained.

In sum, the more intense the perceived volatility, the greater the VIX – or “fear”. As per several research papers, the seeming conclusion is that VIX levels under 15-20 points suggest a somewhat standard volatility; in turn, when then the index surpasses 25-30 points – volatility is seen as high.

For instance, back in March 2020 – when the COVID-19 pandemic triggered market selloffs – the VIX reached levels of up to 80 points. Meanwhile, between 2015 and 2019 (a pre-pandemic benchmark), the VIX averaged about 15 points.

How has the VIX been doing as of late?

The VIX has averaged ~14 points year-to-date, with volatility spiking beyond the 20 point level during mid-April. This was a time in which weaker-than-expected GDP data, along with upside inflation surprises, worsened investment sentiment and fueled (unwarranted) discussions about stagflation. So far this month, the VIX has been at 'normal' levels; unsurprisingly, in line with a positive performance in equity markets.

Just like in life, a sudden spike in “fear” should not trigger you – cool heads generally prevail

As UBS CIO Investment Strategist Justin Waring recently highlighted, “viewing markets through the lens of short-term returns can skew your perception of risk and reward, and make you more susceptible to emotional decision-making.”

“Focusing on long-term returns can improve the investment experience, and embracing a longer investment time horizon can help you unlock greater growth potential,” Waring stated.

There’s nothing wrong with keeping an eye on the market’s movements, which can be sudden and surprising. Yet, for the most part, your investment objectives should be based on long-term targets, which have much to do with fundamental analysis and personal goals, and rather little with short-term asset volatility.

A note to the wise: A level of fear will always be there and at some point will spike. Thus, remember, it's important to keep your cool, even when that seems a hard thing to do.

Yet, in “fearful” times – high-quality bonds generally perform

Moments of market distress tend to result in “flight-to-quality” moves. Investors in their attempt to cap losses tend to sell risky positions and allocate funds to safe-haven assets such as US Treasuries, high-quality corporate credit, and gold.

In fact, one of our current Messages in Focus at the Chief Investment Office is Buy high-quality bonds . Our macro base case accounts for lower interest rates and yields in the year ahead. In the most plausible scenarios, we think high-quality debt can outperform cash over the next 12 months, so locking in today’s high yields may maximize returns.

As a reference, the 10-year US Treasury yield has risen from 3.8% in late December to ~4.5% today. As such, this appears as a good opportunity to lock in yields; we expect 10Y US Treasury yields to close the year at 3.85%.

For further information, please see our notes:

Quality bonds – your soft landing gear , published on 6 May, 2024

Macro and market outlook: April showers followed by May flowers , published on 5 May, 2024

Fear is the path to underperformance , published on 1 May, 2024

Messages in Focus: Buy quality bonds , published on 26 April, 2024.

Main contributor: Alberto Rojas, Investment Communications Writer – CIO Americas

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Stock Market Volatility: Ten Years After the Crash

Stock volatility has been unusually low since the 1987 stock market crash. The large increase in stock prices since 1987 means that many days during 1996 and 1997 experienced near record changes in the Dow Jones Industrial Average, even though the volatility of stock returns has not been high by historical standards. I compare volatility of returns to U.S. stock indexes at monthly, daily, and intraday intervals, and I also show the volatility of returns to stock indexes implied by traded options contracts. Finally, I compare the volatility of U.S. stock market returns with the volatility of returns to stock markets in the United Kingdom Australia, and Canada. All of the evidence leads to the conclusion that volatility has been very low in the decade since the 1987 crash. The mini-crash of October 27 to reevaluate the current system of circuit breakers so that they are triggered less easily. Part of the problem is caused by trigger points that are expressed as absolute changes in market indexes.

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Schwert, G. William. "Stock Market Volatility: Ten Years after the Crash." Brookings-Wharton Papers on Financial Services (1998): 65-99.

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GameStop stock ( GME ) soared as much as 110% Monday before paring gains, and it was halted for volatility numerous times after "Roaring Kitty," the person who is seen as the kick-starter of the meme stock frenzy during the pandemic, posted online for the first time since 2021.

The stock crossed above $30 per share on Monday to close up 75%. Shares had been on an upward trend, rising about 60% over the past two weeks.

"Roaring Kitty," who was identified that year as Keith Gill, became a prominent figure on the WallStreetBets subreddit and YouTube for his bullish stance on GameStop ( GME ).

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pic.twitter.com/YgjVqtgcNS — Roaring Kitty (@TheRoaringKitty) May 13, 2024

Short interest on GameStop sits at around 24% of the float, according to S3 Partners data.

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Monday's short squeeze follows a recent rally in meme-related stocks. Theater chain operator AMC ( AMC ) gained as much as 50% during the session, while Trump Media & Technology ( DJT ) gained 8%.

"Short sellers may be in for a bumpy and bloody ride in these stocks," said Dusaniwsky.

As Yahoo Finance’s Jared Blikre recently noted, the recent meme stock surge doesn't appear to be the ominous signal it has been in the past, but rather a healthy risk appetite for investments.

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Share market today live updates: among sectoral indices, auto, financial service, it, psu bank, metal, realty, commodities and pse remained in green territory. on the contrary, pharma, fmcg, and healthcare settled in red..

stock market volatility research paper

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Earlier today, Indian frontline indices BSE Sensex and NSE Nifty 50 opened little changed with Nifty starting above 22,100 and Sensex near 72,700. Sensex opened 79.51 points, or 0.11  per cent  lower at 72,696.70 while Nifty inched 8.90 points, or 0.04  per cent higher at 22,112.90.

stock market volatility research paper

GIFT Nifty is trading at 143.00 points, or 0.65 per cent higher at 22,315.50.

Among sectoral indices, auto, financial service, IT, PSU Bank, metal, realty, commodities and PSE remained in green territory. On the contrary, pharma, FMCG, and healthcare settled in red.

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The rupee closed at 83.51 against the US dollar, barely changing from its previous close at 83.53. The dollar index stood at 105.2 while other Asian currencies were largely muted.

In Asia-Pacific region, Japan’s Nikkei share average drifted in a narrow range as investors braced for more local corporate earnings results and US inflation figures. The Asian markets also remained cautious over a weakening currency and rising domestic bond yields.

The Nikkei inched nearly 180 points lower at 38,356.06 while South Korea’s Kospi slipped 0.11 per cent at 2,730.43.

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Stock market today live updates: after flattish start, sensex, nifty close higher.

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Stock Market Today Live Updates: Analysts take on current market volatility

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Stock Market Today Live Updates: Passenger vehicle wholesales rise 1.3% to 3,35,629 units in April, says SIAM.

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Stock Market Today Live Updates: Nifty Auto, Metal, Realty, Commodities indices in green

Share Market Today Live Updates: Among sectoral indices, auto, financial service, IT, metal, realty, commodities and PSE remained in green territory. 

On the contrary, pharma, FMCG, and healthcare are trading in red.

Stock Market Today Live Updates: Sensex, Nifty open in mixed territory

Share Market Today Live Updates: BSE Sensex and NSE Nifty 50 opened little changed with Nifty starting above 22,100 & Sensex near 72,700.

Sensex opened 79.51 points, or 0.11 per cent lower at 72,696.70 while Nifty inched 8.90 points, or 0.04 per cent higher at 22,112.90.

Stock Market Today Live Updates: Rupee opens stronger against US dollar

Share Market Today Live Updates: The Indian rupee opened at 83.52 against the US dollar. The rupee closed on Monday at 83.53.

Stock Market Today Live Updates: GIFT Nifty indicates positive start for Sensex, Nifty

Share Market Today Live Updates: GIFT Nifty is trading at 83.50 points, or 0.38 per cent higher at 22,250.00.

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  1. Forecasting stock returns with industry volatility concentration

    In this paper, we show that industry volatility concentration is a strong predictor for aggregate stock market returns. Our monthly industry volatility concentration (IVC) index displays significant predictive ability, with in-sample and out-of-sample R 2 statistics of 0.686% and 0.712%, respectively, which outperforms a host of prevailing return predictors.

  2. Stock Market Volatility and Return Analysis: A Systematic Literature

    However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008-2019) and in 50 different papers.

  3. (PDF) Stock Market Volatility and Return Analysis: A Systematic

    this review study is to conduct a content analysis of return and volatility literature reviews over a. period of 12 years (2008-2019) and in 50 di ff erent papers. The study found that there ...

  4. Forecasting stock market volatility: The sum of the parts is more than

    Conclusion. This paper explores an alternative SOP forecasting method to predict the U.S. stock market volatility by modeling upside and downside volatilities separately. We find that lagged upside and downside volatilities as well as the leverages have asymmetrical impacts on these two volatilities.

  5. Stock market volatility prediction: Evidence from a new bagging model

    The purpose of this study is to investigate which model can improve the precision of the categorical economic policy uncertainty indices in predicting volatility in the U.S. stock market. In this study, a new model is constructed by combining autoregressive model and bagging method. The empirical outcomes indicate that machine learning models ...

  6. Emerging stock market volatility and economic fundamentals: the

    This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our ...

  7. Stock market volatility: a systematic review

    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."

  8. News sentiment and stock market volatility

    This study investigates the effect of news sentiment on stock market volatility using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and measures the asymmetric effect with the GJR-GARCH model. We adopt patented linguistic analysis that considers the semantic orientation process to quantify financial news that may attract investor attention. This study ...

  9. Determinants of Stock Price Volatility: A Literature Review

    Purpose: This paper reviews the theoretical background and the empirical results of the stock price. volatility determinants under three categories: macroeconomic, company-specific fu ndamentals ...

  10. PDF Macroeconomic Volatility and Stock Market Volatility, Worldwide NBER

    6 The latter volatility measure is more relevant for our purposes, so we focus on it for the remainder of this paper. The empirical results are qualitatively unchanged, however, when we use the former measure. 7 Again, however, we focus on the condition version for the remainder of this paper.-4-differently, a measure of the volatility of innovations to fundamentals.6

  11. Macroeconomic Volatility and Stock Market Volatility, Worldwide

    Working Paper 14269. DOI 10.3386/w14269. Issue Date August 2008. Notwithstanding its impressive contributions to empirical financial economics, there remains a significant gap in the volatility literature, namely its relative neglect of the connection between macroeconomic fundamentals and asset return volatility. We progress by analyzing a ...

  12. Entropy

    This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed.

  13. Policy News and Stock Market Volatility

    Policy News and Stock Market Volatility. Scott R. Baker, Nicholas Bloom, Steven J. Davis & Kyle J. Kost. Working Paper 25720. DOI 10.3386/w25720. Issue Date March 2019. We create a newspaper-based Equity Market Volatility (EMV) tracker that moves with the VIX and with the realized volatility of returns on the S&P 500.

  14. Global Stock Market Volatility and Its Spillover on the Indian Stock

    Ashutosh Verma is an Associate Professor at IIFM Bhopal, India; Chartered Accountant, Company Secretary and holds a PhD in finance. He has published research papers in the areas of CSR, earnings management and stock market efficiency and anomalies. He has guided FPM/PhD students in areas ranging from corporate sustainability disclosure practices to commodity futures market.

  15. PDF arXiv:2012.05906v1 [q-fin.ST] 10 Dec 2020

    A growing number of research papers use NLP methods to access how sentiment of firm-specific news, financial reports, or social media impact stock market returns. An important early work (2007) by Tetlock [14] explores possible correlations between the media and the stock market using information from the Wall Street Journal and finds that

  16. 'Indian Stock Market Volatility': A Study of Inter-linkages and

    Modeling daily volatility of the Indian stock market using intra-day data (Working Paper Series No. 588). Calcutta: IIM. ... National Bureau of Economic Research, NBER Working Paper, 8554. Google Scholar. Fleming J., Kirby C., & Ostdiek B. (1998). Information and volatility linkages in the stock, bond, and money markets. ...

  17. PDF Financial Market Volatility and Investor Behavior: Insights From Stock

    This study presents an overview of the intricate relationship between financial market volatility and investor behavior, with a focus on insights gained from studying stock market fluctuations. RESEARCH METHODOLOGY: This study is based on secondary sources of data such as articles, books, journals, research papers, websites and other sources.

  18. (PDF) Stock Market Volatility

    Stock market volatility is an important concept, for understanding the investors responsiveness, and thereby facilitates to work on investment strategy .This paper examines the volatility in ...

  19. Impact of foreign and domestic investment in stock market volatility

    The volatility of stock market has attracted a lot of attention from academic researchers since the past few decades. Stock market liberalization opened the doors of emerging capital markets for foreign investors. As a result, the pursuit of high returns on investments encourages the FPIs and DIIs to invest in emerging stock markets.

  20. PDF Policy News and Stock Market Volatility National Bureau of Economic

    In a first step, we identify articles about stock market volatility in leading U.S. newspapers and use them to construct an Equity Market Volatility (EMV) tracker. Figure 1 displays the resulting measure, which runs from January 1985 to October 2018 and is scaled to match the mean value of the VIX from 1985 to 2015.

  21. PDF Derivatives and Volatility on Indian Stock Markets

    Derivative products like futures and options on Indian stock markets have become important instruments of price discovery, portfolio diversification and risk hedging in recent times. This paper studies the impact of introduction of index futures on spot market volatility on both S&P CNX Nifty and BSE Sensex using ARCH/GARCH technique.

  22. [PDF] Macroeconomic Factors and Stock Price Volatility at Nairobi

    The stock price volatility has been a problem in stock markets affecting the stock returns of firms listed at Nairobi Securities Exchange (NSE). Although some scholars have focused on the issue of high share price volatility, the issue persists and still hinders the development of the market. This study main objective is to determine the effects of macroeconomic factors on volatility of stock ...

  23. What is the VIX?

    More specifically, the VIX Index is a calculation designed to produce a measure of constant, 30-day expected volatility of the US stock market. The headline VIX figure is derived from prices of S&P 500 call and put options. When there is rising demand for call and put options - which partly serve to hedge risks - the price of options ...

  24. MarketBeat: Stock Market News and Research Tools

    Get 30 Days of MarketBeat All Access Free. View the latest news, buy/sell ratings, SEC filings and insider transactions for your stocks. Compare your portfolio performance to leading indices and get personalized stock ideas based on your portfolio. Get daily stock ideas from top-performing Wall Street analysts.

  25. (PDF) Indian Derivatives Market: A Study of Impact on Volatility and

    The present paper examines the impact of equity derivatives trading on spot market volatility, particularly the effect of equity derivatives introduction on spot market volatility in Indian stock ...

  26. Stock Market Volatility: Ten Years After the Crash

    Stock Market Volatility: Ten Years After the Crash. G. William Schwert. Share. Working Paper 6381. DOI 10.3386/w6381. Issue DateJanuary 1998. Stock volatility has been unusually low since the 1987 stock market crash. The large increase in stock prices since 1987 means that many days during 1996 and 1997 experienced near record changes in the ...

  27. GameStop stock soars over 70% as 'Roaring Kitty' revival reignites meme

    AMC. GameStop stock ( GME) soared as much as 110% Monday before paring gains, and it was halted for volatility numerous times after "Roaring Kitty," the person who is seen as the kick-starter of ...

  28. Tesla Is Still Down 25% in 2024 Despite Its Monster Jump. Is the Stock

    Being a Tesla ( TSLA -2.04%) shareholder isn't for the weak stomach. The company's stock movements can be erratic, and many people may worry if they're not used to the volatility. Tesla's stock ...

  29. Stock Market Today Live Updates: Sensex inches 300 pts higher, Nifty

    Share Market Live: Sensex, Nifty 50 inch marginally incher (File Image) Stock Market Live: After opening on a flattish note on Tuesday (May 14), the Indian frontline indices soared higher to close in green territory.Sensex closed 328.48 points, or 0.45 per cent higher at 73,104.61 while Nifty inched 118.55 points, or 0.54 per cent higher at 22,222.60.

  30. A Study on Stock Market Volatility Pattern of BSE and NSE in India

    Exchange (NSE) and Bomba y Stock Exchange (BSE) for period from1990 to 2016. The main focus of this. research paper is to examine the nature of the volatility in the Indian stock markets. In this ...