research paper on stock market volatility

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Entropy (Basel)

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 ].

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.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• PubMed/Medline
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Stock market volatility and return analysis: a systematic literature review.

1. Introduction

2. methodology, 3. review of different studies.

4. Conclusions

Author contributions, conflicts of interest.

• Snyder, H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 2019 , 104 , 333–339. [ Google Scholar ] [ CrossRef ]
• Harris, L. Trading and Exchanges: Market Microstructure for Practitioners ; Oxford University Press: Hong Kong, China, 2003. [ Google Scholar ]
• Engle, R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. Inflation. Economics 1982 , 50 , 987–1008. [ Google Scholar ] [ CrossRef ]
• Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. J. Econ. 1986 , 31 , 307–327. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Nelson, D.B. Conditional heteroskedasticity in asset returns: A new approach. Economics 1991 , 59 , 347–370. [ Google Scholar ] [ CrossRef ]
• Leung, M.T.; Daouk, H.; Chen, A.S. Forecasting stock indices: A comparison of classification and level estimation models. Int. J. Forecast. 2000 , 16 , 173–190. [ Google Scholar ] [ CrossRef ]
• Bhowmik, R.; Wu, C.; Kumar, J.R.; Wang, S. A study on the volatility of the Bangladesh stock market—Based on GARCH type models. J. Syst. Sci. Inf. 2017 , 5 , 193–215. [ Google Scholar ]
• Yao, J.; Tan, C.L. A case study on using neural networks to perform technical forecasting of forex. Neurocomputing 2000 , 34 , 79–98. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hussain, S.; Murthy, K.V.B.; Singh, A.K. Stock market volatility: A review of the empirical literature. IUJ J. Manag. 2019 , 7 , 96–105. [ Google Scholar ]
• Dhanaiah, G.; Prasad, S.R. Volatility and co-movement models: A literature review and synthesis. Int. J. Eng. Manag. Res. 2017 , 7 , 1–25. [ Google Scholar ]
• Reddy, Y.V.; Narayan, P. Literature on stock returns: A content analysis. Amity J. Financ. 2016 , 1 , 194–207. [ Google Scholar ]
• Mamtha, D.; Srinivasan, K.S. Stock market volatility: Conceptual perspective through literature survey. Mediterr. J. Soc. Sci. 2016 , 7 , 208–212. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Scott, L.O. Financial market volatility: A survey. Staff Pap. Int. Monet. Fund 1991 , 38 , 582–625. [ Google Scholar ] [ CrossRef ]
• Easterby-Smith, M.; Thorpe, R.; Jackson, P. Management and Business Research ; Sage: Thousand Oaks, CA, USA, 2015. [ Google Scholar ]
• Tranfield, D.; Denyer, D.; Smart, P. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manag. 2003 , 14 , 207–222. [ Google Scholar ] [ CrossRef ]
• Geissdoerfer, M.; Savaget, P.; Bocken, N.M.P.; Hultink, E.J. The circular economy—A new sustainability paradigm? J. Clean. Prod. 2017 , 143 , 757–768. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hopp, W.J. Ten most influential papers of Management Science’s first fifty years. Manag. Sci. 2004 , 50 , 1763–1893. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Engle, R.F.; Ng, V.K. Measuring and testing the impact of news on volatility. J. Financ. 1993 , 48 , 1749–1778. [ Google Scholar ] [ CrossRef ]
• Engle, R.F.; Bollerslev, T. Modelling the persistence of conditional variances. Econ. Rev. 1986 , 5 , 1–50. [ Google Scholar ] [ CrossRef ]
• Engle, R.F.; Lilien, D.; Robins, R. Estimating time varying risk premia in the term structure: The ARCH-M model. Economics 1987 , 55 , 391–408. [ Google Scholar ] [ CrossRef ]
• Sentana, E. Quadratic ARCH models. Rev. Econ. Stud. 1995 , 62 , 639–661. [ Google Scholar ] [ CrossRef ]
• Glosten, L.; Jagannathan, R.; Runkle, D. Relationship between the expected value and the volatility of the nominal excess return on stocks. J. Financ. 1993 , 48 , 1779–1801. [ Google Scholar ] [ CrossRef ]
• Zakoian, J.M. Threshold heteroskedastic models. J. Econ. Dyn. Control 1994 , 18 , 931–955. [ Google Scholar ] [ CrossRef ]
• Hentschel, L. All in the family: Nesting symmetric and asymmetric GARCH models. J. Financ. Econ. 1995 , 39 , 71–104. [ Google Scholar ] [ CrossRef ]
• Klüppelberg, C.; Lindner, A.; Maller, R. A continuous-time GARCH process driven by a Lévy process: Stationarity and second-order behaviour. J. Appl. Probab. 2004 , 41 , 601–622. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Pan, J.; Wang, H.; Tong, H. Estimation and tests for power-transformed and threshold GARCH models. J. Econ. 2008 , 142 , 352–378. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Alberg, D.; Shalit, H.; Yosef, R. Estimating stock market volatility using asymmetric GARCH models. App. Financ. Econ. 2008 , 18 , 1201–1208. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Olowe, R.A. Stock return volatility, global financial crisis and the monthly seasonal effect on the Nigerian stock exchange. Afr. Rev. Money Financ. Bank. 2009 , 73–107. [ Google Scholar ]
• Girard, E.; Omran, M. On the relationship between trading volume and stock price volatility in CASE. Int. J. Manag. Financ. 2009 , 5 , 110–134. [ Google Scholar ] [ CrossRef ]
• Neokosmidis, I. Econometric Analysis of Realized Volatility: Evidence of Financial Crisis. 2009, pp. 1–22. Available online: http://www.lse.ac.uk/europeanInstitute/research/hellenicObservatory/pdf/4th_%20Symposium/PAPERS_PPS/APPLIED_ECONOMICS/NEOKOSMIDIS.pdf (accessed on 12 January 2020).
• Tripathy, T.; Gil-Alana, L.A. Suitability of volatility models for forecasting stock market returns: A study on the Indian National Stock Exchange. Am. J. Appl. Sci. 2010 , 7 , 1487–1494. [ Google Scholar ]
• Liu, H.C.; Hung, J.C. Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models. Expert Syst. Appl. 2010 , 37 , 4928–4934. [ Google Scholar ]
• Joshi, P. Modeling volatility in emerging stock markets of India and China. J. Q. Econ. 2010 , 8 , 86–94. [ Google Scholar ]
• Wong, A.; Cheung, K.Y. Measuring and visualizing the asymmetries in stock market volatility: Case of Hong Kong. Int. Res. J. Appl. Financ. 2011 , 2 , 1–26. [ Google Scholar ]
• Chang, C.H.; Cheng, H.I.; Huang, I.H.; Huang, H.H. Lead-lag relationship, volatility asymmetry, and overreaction phenomenon. Manag. Financ. 2011 , 37 , 47–71. [ Google Scholar ] [ CrossRef ]
• Koutmos, D. Time-varying behavior of stock prices, volatility dynamics and beta risk in industry sector indices of the Shanghai Stock Exchange. Account. Financ. Res. 2012 , 1 , 109–125. [ Google Scholar ] [ CrossRef ]
• Chen, X. Empirical Investigations into Stock Market Integration and Risk Monitoring of the Emerging Chinese Stock Markets ; University of St Andrews: St Andrews, UK, 2012; pp. 1–314. Available online: https://research-repository.st-andrews.ac.uk/handle/10023/3208 (accessed on 25 January 2020).
• Abdalla, S.Z.S.; Suliman, Z. Modelling stock returns volatility: Empirical evidence from Saudi Stock Exchange. Int. Res. J. Financ. Econ. 2012 , 85 , 166–179. [ Google Scholar ]
• Maheshchandra, J.P. Long memory property in return and volatility: Evidence from the Indian stock markets. Asian J. Financ. Account. 2012 , 4 , 218–230. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Li, W.; Wang, S.S. Empirical studies of the effect of leverage industry characteristics. WSEAS Trans. Bus. Econ. 2013 , 10 , 306–315. [ Google Scholar ]
• Hou, A.J. Asymmetry effects of shocks in Chinese stock markets volatility: A generalized additive nonparametric approach. J. Int. Financ. Mark. Inst. Money 2013 , 23 , 12–32. [ Google Scholar ] [ CrossRef ]
• Purohit, H.; Chhatwal, H.; Puri, H. An empirical investigation of volatility of the stock market in India. Pac. Bus. Rev. Int. 2014 , 7 , 64–73. [ Google Scholar ]
• Shalini, A.P. An emperical study of volatility of sectoral indices (India). Indian Res. J. 2014 , 1 , 78–95. [ Google Scholar ]
• Ghorbel, A.; Attafi, Z. Dependence between stock markets of MENA countries after sub-prime crisis using bivariate extreme value theory. Int. J. Appl. Manag. Sci. 2014 , 6 , 343–364. [ Google Scholar ] [ CrossRef ]
• Gupta, R.K.; Jindal, N.; Gupta, A. Conditional volatility and stock market behavior in NSE. Int. J. Innov. Eng. Manag. 2014 , 3 , 16–20. [ Google Scholar ]
• Nadhem, S.; Samira, C.; Nejib, H. Forecasting returns on a stock market using Artificial Neural Networks and GARCH family models: Evidence of stock market S&P 500. Decis. Sci. Lett. 2015 , 4 , 203–210. [ Google Scholar ]
• Banumathy, K.; Azhagaiah, R. Modelling stock market volatility: Evidence from India. Managing global transitions. Int. Res. J. 2015 , 13 , 27–42. [ Google Scholar ]
• Okičić, J. An empirical analysis of stock returns and volatility: The case of stock markets from Central and Eastern Europe. South East Eur. J. Econ. Bus. 2015 , 9 , 7–15. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lum, Y.C.; Islam, S.M.N. Time varying behavior of share returns in Australia: 1988–2004. Rev. Pac. Basin Financ. Mark. Policies 2016 , 19 , 1650004. [ Google Scholar ] [ CrossRef ]
• Jebran, K.; Iqbal, A. Examining volatility spillover between Asian countries’ stock markets. China Financ. Econ. Rev. 2016 , 4 , 1–13. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yang, J.; Feng, Y.; Wang, H. Estimation of volatility of CSI 300 index based on regime switching PTTGARCH model. Xitong Gongcheng Lilun yu Shijian/Sys. Eng. Theory Prac. 2016 , 36 , 2205–2215. [ Google Scholar ]
• Varughese, A.; Mathew, T. Asymmetric volatility of the Indian stock market and foreign portfolio investments: An empirical study. Indian J. Financ. 2017 , 11 , 36–49. [ Google Scholar ] [ CrossRef ]
• Pati, P.C.; Barai, P.; Rajib, P. Forecasting stock market volatility and information content of implied volatility index. Appl. Econ. 2017 , 50 , 2552–2568. [ Google Scholar ] [ CrossRef ]
• Pele, D.T.; Lazar, E.; Dufour, A. Information entropy and measures of market risk. Entropy 2017 , 19 , 226. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bhowmik, R.; Ghulam, A.; Wang, S. Return and volatility spillovers effects: Study of Asian emerging stock markets. J. Syst. Sci. Inf. 2018 , 6 , 97–119. [ Google Scholar ]
• Kim, M.; Lee, S. Test for tail index constancy of GARCH innovations based on conditional volatility. Ann. Inst. Stat. Math. 2018 , 71 , 947–981. [ Google Scholar ] [ CrossRef ]
• Amudha, R.; Muthukamu, M. Modeling symmetric and asymmetric volatility in the Indian stock market. Indian J. Financ. 2018 , 12 , 23–36. [ Google Scholar ] [ CrossRef ]
• Chronopoulos, D.K.; Papadimitriou, F.I.; Vlastakis, N. Information demand and stock return predictability. J. Int. Money Financ. 2018 , 80 , 59–74. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bhowmik, R.; Wang, S. An investigation of return and volatility linkages among stock markets: A study of emerging Asian and selected developed countries. J. Int. Trad. Com. 2018 , 14 , 1–29. [ Google Scholar ] [ CrossRef ]
• Kapusuzoglu, A.; Ceylan, N.B. Trading volume, volatility and GARCH effects in Borsa Istanbul. In Strategic Design and Innovative Thinking in Business Operations ; Springer: Cham, Switzerland, 2018; pp. 333–347. [ Google Scholar ]
• Wang, Y.C.; Tsai, J.J.; Li, X. What drives China’s 2015 stock market Surges and Turmoil? Asian Pac. J. Financ. Stud. 2019 , 48 , 410–436. [ Google Scholar ] [ CrossRef ]
• Shanthi, A.; Thamilselvan, R. Univariate GARCH models applied to the bombay stock exchange and national stock exchange stock indices. Int. J. Manag. Bus. Res. 2019 , 9 , 22–33. [ Google Scholar ]
• Bhowmik, R.; Wang, S. Is the emerging Asian stock markets really predictable- based on the Operations and Information Management. Int. J. Supply Chain Manag. 2019 , 8 , 600–621. [ Google Scholar ]
• Dixit, J.; Agrawal, V. Foresight for stock market volatility: A study in the Indian perspective. Foresight 2019 , 22 , 1–13. [ Google Scholar ] [ CrossRef ]
• Kumar, A.; Biswal, S.K. Impulsive clustering and leverage effect of emerging stock market with special reference to Brazil, India, Indonesia, and Pakistan. J. Adv. Res. Dyn. Control Syst. 2019 , 11 , 33–37. [ Google Scholar ] [ CrossRef ]
• Bollerslev, T.; Engle, R.F.; Wooldridge, J.M. A capital asset pricing model with time-varying covariances. J. Politic Econ. 1988 , 96 , 116–131. [ Google Scholar ] [ CrossRef ]
• Baba, Y.; Engle, R.F.; Kraft, D.F.; Kroner, K.F. Multivariate Simultaneous Generalized ARCH. Manuscript ; University of California, Department of Economics: San Diego, CA, USA, 1990. [ Google Scholar ]
• Bollerslev, T. Modeling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Rev. Econ. Stat. 1990 , 72 , 498–505. [ Google Scholar ] [ CrossRef ]
• Engle, R.F. Dynamic conditional correlation—A simple class of multivariate GARCH models. J. Bus. Econ. Stat. 2002 , 20 , 339–350. [ Google Scholar ] [ CrossRef ]
• Alexander, C.O.; Chibumba, A. Multivariate Orthogonal Factor GARCH ; University of Sussex Discussion Papers in Mathematics: Brighton, UK, 1996. [ Google Scholar ]
• Alexander, C.O. Orthogonal methods for generating large positive semi-definite covariance matrices. ISMA Cent. Discuss. Pap. Financ. 2000 . Available online: https://core.ac.uk/download/pdf/7056485.pdf (accessed on 3 February 2020). [ CrossRef ]
• Alexander, C.O. (Ed.) Orthogonal GARCH in C.O. In Mastering Risk ; Financial Times Prentice Hall: London, UK, 2001; Volume 2, pp. 21–38. [ Google Scholar ]
• Bauwens, L.; Laurent, S.; Rombouts, J.V.K. Multivariate GARCH models: A survey. J. Appl. Econ. 2006 , 21 , 79–109. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Singh, P.; Kumar, B.; Pandey, A. Price and Volatility Spillovers across North American, European and Asian Stock Markets: With Special Focus on Indian Stock Marke ; Indian Institute of Management Ahmedabad: Ahmedabad, India, 2008; pp. 1–45. Available online: http://vslir.iima.ac.in:8080/jspui/handle/11718/17115 (accessed on 15 January 2020).
• Rao, A. Analysis of volatility persistence in Middle East emerging equity markets. Stud. Econ. Financ. 2008 , 25 , 93–111. [ Google Scholar ] [ CrossRef ]
• Maniya, R.S.; Magnusson, F. Bear Periods Amplify Correlation: A GARCH BEKK Approach. rapport nr., Master Degree Project, University of Gothenburg, Gothenburg, Sweden, 2010; pp. 1–55. Available online: https://gupea.ub.gu.se/bitstream/2077/22675/1/gupea_2077_22675_1.pdf (accessed on 12 November 2019).
• Princ, M. Relationship between Czech and European Developed Stock Markets: DCC MVGARCH Analysis ; Working Papers IES; Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies: Prague, Czech, September 2010; pp. 1–35. [ Google Scholar ]
• Yong, F.T.; Holmes, M.; Choi, D. Volatility transmission and asymmetric linkages between the stock and foreign exchange markets: A sectoral analysis. Stud. Econ. Financ. 2011 , 28 , 36–50. [ Google Scholar ] [ CrossRef ]
• Athukoralalage, K.P.I. Modelling Australian Stock Market Volatility. 2011, pp. 1–161. Available online: https://ro.uow.edu.au/cgi/viewcontent.cgi?referer=https://scholar.google.com/&httpsredir=1&article=4435&context=theses (accessed on 5 October 2019).
• Kouki, I.; Harrathi, N.; Haque, M. A volatility spillover among sector index of international stock markets. J. Money Invest. Bank. 2011 , 22 , 32–45. [ Google Scholar ]
• Walid, C.; Chaker, A.; Masood, O.; Fry, J. Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach. Emerg. Mark. Rev. 2011 , 12 , 272–292. [ Google Scholar ] [ CrossRef ]
• Katzke, N. South African Sector Return Correlations: Using DCC and ADCC Multivariate GARCH Techniques to Uncover the Underlying Dynamics ; Stellenbosch Economic Working Papers: 17/13; University of Stellenbosch: Stellenbosch, South Africa, 2013; pp. 1–31. [ Google Scholar ]
• Peng, C.L.; Chung, C.F.; Tsai, C.C.; Wang, C.T. Exploring the returns and volatility spillover effect in Taiwan and Japan stock markets. Asian Econ. Financ. Rev. 2017 , 7 , 175–187. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lv, Q.; Han, L.; Wan, Y.; Yin, L. Stock net Entropy: Evidence from the Chinese growth enterprise market. Entropy 2018 , 20 , 805. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Matei, M.; Rovira, X.; Agell, N. Bivariate volatility modeling with high-frequency data. Economics 2019 , 7 , 41. [ Google Scholar ] [ CrossRef ] [ Green Version ]

Click here to enlarge figure

Share and Cite

Bhowmik, R.; Wang, S. Stock Market Volatility and Return Analysis: A Systematic Literature Review. Entropy 2020 , 22 , 522. https://doi.org/10.3390/e22050522

Bhowmik R, Wang S. Stock Market Volatility and Return Analysis: A Systematic Literature Review. Entropy . 2020; 22(5):522. https://doi.org/10.3390/e22050522

Bhowmik, Roni, and Shouyang Wang. 2020. "Stock Market Volatility and Return Analysis: A Systematic Literature Review" Entropy 22, no. 5: 522. https://doi.org/10.3390/e22050522

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

To read this content please select one of the options below:

Please note you do not have access to teaching notes, stock market volatility: a systematic review.

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.

Design/methodology/approach

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.

Originality/value

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

Acknowledgements

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

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

Macroeconomic Volatility and Stock Market Volatility, Worldwide

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 broad international cross section of stock markets covering approximately forty countries. We find a clear link between macroeconomic fundamentals and stock market volatilities, with volatile fundamentals translating into volatile stock markets.

We gratefully dedicate this paper to Rob Engle on the occasion of his 65th birthday. The research was supported by the Guggenheim Foundation, the Humboldt Foundation, and the National Science Foundation. For outstanding research assistance we thank Chiara Scotti and Georg Strasser. For helpful comments we thank the Editor and Referee, as well as Joe Davis, Aureo DePaula, Jonathan Wright, and participants at the Penn Econometrics Lunch, the Econometric Society 2008 Winter Meetings in New Orleans, and the Engle Festschrift Conference. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

Advertisement

Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises

• S.I.: Risk Management Decisions and Value under Uncertainty
• Open access
• Published: 21 April 2021
• Volume 313 , pages 1077–1116, ( 2022 )

Cite this article

You have full access to this open access article

• M. Karanasos   ORCID: orcid.org/0000-0001-5442-3509 1 ,
• S. Yfanti   ORCID: orcid.org/0000-0001-8071-916X 2 &
• J. Hunter   ORCID: orcid.org/0000-0002-9693-2878 1  

6574 Accesses

24 Citations

1 Altmetric

Explore all metrics

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 macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

Similar content being viewed by others

Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak

The US financial crisis, market volatility, credit risk and stock returns in the Americas

The impact of the COVID 19 pandemic on stock market volatility: evidence from a selection of developed and emerging stock markets

Avoid common mistakes on your manuscript.

1 Introduction

A common stylized fact about emerging economies is the high volatility of their stock markets (De Santis 1997 ; Aggarwal et al. 1999 ; Xu 1999 ; Cano-Berlanga and Giménez-Gómez 2018 ). The liberalization of the emerging world’s financial markets, which attracted a significant amount of capital flows by foreign institutional investors, has been the first step of an integration process with significant economies’ interdependence and asset markets’ synchronization. Given that emerging economies are characterized by critical vulnerabilities to external shocks, they exhibit higher equity market fluctuations than the developed markets and it is worth investigating how US and global common economic forces affect their intra-daily volatility. In general, modeling the volatility of financial returns has crucial implications for asset allocation, risk management practices, and financial stability oversight. Robust modeling and reliable forecasting of the volatility trajectory of financial instruments has been the main task and objective of financial economics applications for business operations, given that volatility constitutes one of the fundamental input variables in estimations and decision processes of any corporation on investing and funding choices. Financial volatility is also closely inspected by policymakers since it is perceived to constitute an early warning crisis signal, entailing critical destabilizing threats for the financial system (see, for example, Kürüm et al. 2018 ).

This paper applies an extension of the bivariate HEAVY Footnote 1 system, firstly, with asymmetries and power transformations, through the APARCH structure of Ding et al. ( 1993 ). Motivated by the widely-recognized merits of the this framework, which considerably improves Bollerslev’s ( 1986 ) GARCH process by adding leverage and power effects (see, for example, Karanasos and Kim 2006 ), we similarly extend the HEAVY system with these two main features to demonstrate its superiority over the benchmark specification introduced by Shephard and Sheppard ( 2010 ). The optimal estimation of the power term and the asymmetric response to positive and negative shocks embedded in the time-varying volatility pattern have already proved to be one of the most pivotal innovations in the GARCH family of models (see, for example, Brooks et al. 2000 ). One of our first findings is that each of the two powered conditional variances is significantly affected by the first lags of both power transformed variables, that is, squared negative returns, and realized variance. Secondly, we extend the asymmetric power specification with macro-effects from US economic policy and financial uncertainty, bond and commodity market global benchmarks, and the infectious disease news impact on US equity market volatility, providing a competing framework of volatility modeling to the well-established practice of financial instruments trading and risk measuring based on economic fundamentals. We apply the macro-augmented model on five emerging stock market indices from two different regions: the Americas and Asia-Pacific. The realized measure receives significant positive impact from all macro-variables included with further improvement of the model’s forecasting performance. Moreover, we examine not only the direct destabilizing effect of the policy uncertainty volatility spillover on stock market realized volatility, by using it as a conditional variance regressor, but also the policy uncertainty level impact on each parameter of the system (indirect impact), showing that higher US policy uncertainty inflates the leverage and the macro-environment’s effects from financial uncertainty, credit conditions, commodity markets, and infectious diseases on the realized measure. Finally, we explore the crisis influence on emerging stock markets and find that both global financial (the 2008 financial turmoil) and health (the Covid-19 outbreak) crisis events magnify the markets turbulence and the volatility macro-drivers effect.

At the beginning of the 2008 global financial crisis, when volatilities increased sharply and persistently with crucial systemic risk externalities, we witnessed a reigniting interest of regulators and academics in meaningful volatility estimates, while, at the same time, practitioners remained alert to improving the relevant volatility frameworks on a day-to-day basis. Financial economics scholars focused on volatility as a potent catalyst of systemic risk build-up, which policymakers tried to limit. We demarcate this study from the extant finance bibliography by applying the extended HEAVY model with asymmetries, power transformations, and macro-effects, a well-defined framework that adequately fits the volatility process and considerably outperforms the traditional econometric approaches for modeling returns [GARCH(1, 1) specifications] and realized volatility (long memory specifications: Autoregressive Fractionally Integrated Moving Average-ARFIMA(1, d , 1) and Heterogeneous Autoregressive Realized Variance-HAR-RV). Our framework contributes to two main strands of empirical macro-finance literature: the research on volatility modeling and the macro-financial linkages in emerging economies, with the investigation of the crucial US uncertainty spillover effects and crisis events on emerging financial market stability. Most importantly, we intend to complement existing evidence by bridging the research on the macro-relevance of financial volatility with the high-frequency financial data domain. Filling a notable gap of the academic literature related to the high-frequency macro-financial linkages in emerging economies our novel findings are summarized as follows: (i) higher volatility of US Economic Policy Uncertainty (EPU), elevated US financial uncertainty, tighter credit conditions, increased commodity prices, and stronger infectious disease news impact on US equities, all five economic forces intensify emerging stock markets volatilities, (ii) the economic uncertainty channel (proxied by the US EPU level) further exacerbates asymmetries and macro-ramifications on emerging equities, and (iii) both global crisis events, the subprime crisis and the current pandemic-led crash, boost emerging stock market volatility through the time-varying pattern of the HEAVY’s parameters with the macro-effects included, as well.

In this vein, our analysis focuses on the macro-financial linkages running from the macroeconomy to the financial sector by incorporating important economic fundamentals in emerging equity volatility modeling for a long sample period covering the recent health crisis-driven market crash. We aim at contributing to the existing empirical evidence on the macro-factors driving financial volatility by using daily macro-variables (instead of monthly or quarterly proxies included in the existing literature) and on the effects of the global financial turmoil and the recent pandemic on volatility dynamics. The higher the frequency of economic news incorporated in forecasting the volatility pattern, the more accurately the predicted values will be. Daily volatility forecasts, updated with daily shocks from the continuously evolving macro-environment, offer the necessary tools for market participants closely watching day-to-day volatility dynamics, trading and investing in the markets, or supervising and regulating the financial system. On the contrary, forecasts based on macro-shocks with a one- or three-month lag cannot reflect the up-to-date influence which economic fundamentals exert on financial markets. The use of the high-frequency macro domain in volatility modeling becomes even more crucial in crisis periods where the macro-conditions change very rapidly. Therefore, we explore the response of emerging equity markets to the unprecedented pandemic shock we experience nowadays apart from the global financial turmoil of the previous decade. The Covid-19 outbreak can be characterized as a generational phenomenon leading to a unique crisis. We are seeing a global socio-economic meltdown that quickly unfolds following the explosive Covid-related new deaths or infections and the unprecedented ways policymakers have responded. These are uncharted waters, and thus it is crucial that our macro-informed financial volatility modeling approach should rely on the highest possible frequency of economic news affecting the markets and not on data releases that refer to the previous month or quarter. The bivariate system of the two volatility equations is ready-to-use not only on stock market returns but also on further asset classes or financial instruments (e.g. exchange rate, cryptocurrency, commodity, real estate, and bond returns, associating them with alternative macro-proxies besides uncertainty) and multiple financial economics applications of business operations, such as bonds investing, foreign exchange trading and commodities hedging, core daily functions in the treasuries of most financial and non-financial corporations.

Overall, our volatility framework improves the HEAVY model and beats the standard variance specifications (GARCH, ARFIMA, and HAR models), with significant implications for market practitioners and policymakers on forecasting the financial returns’ second moment. Volatility modeling and forecasting are essential for asset valuation and risk management strategies. A reliable volatility forecast, exploiting in full the high-frequency domain and the macro-financial linkages, is the input variable of paramount importance for the processes of derivatives pricing, effective cross-hedging, Value-at-Risk measurement, investment allocation, portfolio optimization with different asset classes and financial instruments. Moreover, the robust volatility modeling approach we apply provides a useful tool not only for market players but also for policymakers. Policymaking includes continuous oversight duties and prudential regulation practices. Thus, it is imperative for the authorities to account for the volatility of financial markets across every aspect of the financial system’s policy responses, both post-crisis through stabilization policy reactions and pre-crisis through proactive assessment of financial risks. Regulators in emerging economies should consider, among the threats of their financial markets, the global destabilizing factors, beyond the local characteristics. In particular, the significant repercussions of uncertainty about US economic policies constitute the focus of attention in political debates, nowadays, with the widespread anxiety of world market players immediately after Trump’s victory and inauguration. The trade war uncertainty from the very early days of Trumponomics (e.g. steel and aluminum unconventional import tariffs) was followed by the overall fear of economic agents about future government initiatives. More intriguingly, our study is relevant to a highly topical issue nowadays, the Coronavirus pandemic, and contributes to the rapidly growing literature on the pandemic’s socio-economic effects and policy responses.

The remainder of the paper is structured as follows. Section  2 reviews the relevant literature. In Sect.  3 , we detail the HEAVY formulation enriched with asymmetries, power transformations, and macro-effects. Section  4 describes the data and Sect.  5 presents the results for the benchmark, the macro-augmented asymmetric power models, and the crisis effect on volatility modeling parameters. In Sect.  6 , we compute the multiple-step-ahead forecasts to measure the out-of-sample performance of the various specifications. The following Section focuses on the uncertainty effects across the parameters of the HEAVY specification and Sect.  8 discusses the policy implications of our findings. Finally, Sect.  9 concludes the analysis.

2 Literature review

There is a large body of literature focusing on modeling and forecasting realized volatility, applying non-parametric estimation methods to high-frequency data. Following the first studies that formalized the daily realized measures on intra-daily returns (e.g. the realized variance established by Andersen et al. 2001 , and the realized kernel by Barndorff-Nielsen et al. 2008 ), Andersen et al. ( 2001 ) and Corsi ( 2009 ) proposed long memory models for the conditional mean of the realized variance, that is the ARFIMA and HAR-RV specifications, respectively. In order to improve the forecasting accuracy of the various volatility models, econometricians have developed specifications combining daily with intra-daily measures. Engle ( 2002 ) estimated the daily GARCH-X specification adding the realized measure as an exogenous variable in the GARCH(1, 1) equation to capture the incremental information of the higher-frequency domain. Accordingly, Shephard and Sheppard ( 2010 ) introduced the HEAVY framework and Hansen et al. ( 2012 ) followed with the Realized GARCH (see also Barunik et al. 2016 ). Both HEAVY and Realized GARCH jointly estimate daily returns’ conditional variance and realized measure’s conditional mean. The HEAVY system of equations is proved to adopt to information arrival more rapidly than the classic daily GARCH process. A key advantage is the system’s robustness to certain forms of structural breaks, especially during crisis periods, since the mean reversion and short-run momentum effects result in higher quality performance in volatility level shifts and more reliable forecasts (Shephard and Sheppard 2010 ).

The financial econometrics literature on realized volatility dynamics mostly ignores important macro-factors that may affect the volatility pattern in the high-frequency domain. The empirical evidence on the economic drivers of equity volatility mostly employs lower- than daily-frequency macro-variables (monthly or quarterly). The first studies that explained monthly stock volatility with the business cycle dynamics were Schwert ( 1989 ) and Hamilton and Lin ( 1996 ). Engle and Rangel ( 2008 ) and Engle et al. ( 2013 ) use Spline- and MIDAS (Mixed-Data Sampling)—GARCH to link daily volatility with lower-frequency macro-proxies through the mixed-frequencies approach. Corradi et al. ( 2013 ) explore the economic impact on monthly returns, volatilities, and volatility risk-premia. Finally, Conrad and Loch ( 2015 ) test quarterly macro-regressors of daily conditional variance, and Meligkotsidou et al. ( 2019 ) include monthly macro-financial factors in monthly realized volatility quantile forecasting. The common finding of this area of research is the counter-cyclical behavior of financial volatility vis-a-vis economic activity indices. The economic intuition underpinning the link between equities and the macro-environment can be described as follows: equities volatility is tightly related to the uncertainty over the future expected cash flows of the firms issuing the stocks traded. These cash flows constitute the direct result of the firms’ performance which is, in turn, strongly affected by the business cycle dynamics. Besides the cash flow volume, the economic stance also determines the valuation of the cash flows through the discount rates used to define their present value (for a more detailed discussion on the economic theory supporting the stock volatility-macro conditions countercyclical nexus see Paye 2012 ; Christiansen et al. 2012 , and Mittnik et al. 2015 ). Therefore, volatility modeling practice should rely on macroeconomic condition metrics to demonstrate the macro-financial effects on stock market volatility driven either by the cash flow or the discount rates channel.

Moreover, the unprecedented economic impact of the current pandemic and the high speed with which the crisis is evolving introduce uncertainty into models that assess the disastrous effects of the virus spread (Baker et al. 2020c ). Caggiano et al. ( 2020 ) have predicted a huge decrease in the world output due to the Covid-induced uncertainty shock. Baker et al. ( 2020b ) are the first to quantify this Covid-induced economic uncertainty combining three sources: stock market volatility, newspaper-based and business expectations survey-based uncertainties. Baker et al. ( 2020a ) have investigated the disease’s detrimental impact on stock markets and demonstrate that the Covid-19 stock market effects have been by far more powerful than those of previous diseases (e.g. Spanish flu) due to the current pandemic’s severity, the more rapid diffusion of pandemic news, and the tighter macro-financial cross-border interconnectedness in the current globalization era. Turning to the pandemic shock on stock market volatility, Wang et al. ( 2020 ) have applied the HAR-RV (Heterogeneous Autoregressive-Realized Variance) model to predict daily stock market volatility during the Covid outbreak period. They extended the HAR equation with two alternative daily US uncertainty proxies: (i) the VIX index, which is the implied volatility metric of S&P500 used as a financial uncertainty source, and (ii) the US Economic Policy Uncertainty. Their forecasting exercise for nineteen equity indices indicates which uncertainty proxy produces more accurate forecasts for each market during the current crisis. Wang et al. ( 2020 ) investigated the US uncertainty spillovers on financial volatility across the different markets globally while the present paper focuses on the US uncertainty spillover effect alongside other US and global macro-factors on emerging stock market volatilities applying the sophisticated HEAVY framework for both returns and realized dispersion measures. Our macro-augmented specification with daily macro-proxies driving the volatility pattern during the last two decades with the 2008 turmoil and the pandemic period included, also advances the volatility modeling research which does not consider significant macro-determinants of the volatility process in the high-frequency domain.

In the present paper, we focus on the crucial role of economic uncertainty, besides other macro-variables, in volatility predictions applying the news-based Economic Policy Uncertainty index, the only macroeconomic uncertainty measure with a daily frequency provided by Baker et al. ( 2016 ) for the United States and the United Kingdom. The widely-recognized main advantage of the EPU index is its inclusivity since it incorporates both economic and policy-related factors giving rise to uncertainty. We investigate the effect of daily US EPU on emerging equity volatility modeling and its impact during the financial and health crises. Exploring the effects of uncertainty on financial volatility is very topical in the aftermath of the global financial crisis of 2007/08, since there has been renewed interest in this ‘amorphous’ concept (Bloom 2014 ). Based on the Knightian uncertainty definition (Knight 1921 ) and the seminal papers on uncertainty by Bernanke ( 1983 ) and Dixit and Pindyck ( 1994 ), researchers have focused on measuring this latent variable affecting the decision-making process by economic agents (Bekaert et al. 2013 ; Jurado et al. 2015 ; Mumtaz and Theodoridis 2018 ; Carriero et al. 2018 ; Jo and Sekkel 2019 ). Knight observed that households’ saving and consumption, corporations’ recruitments, investing and funding, traders’ portfolio selection, and regulators’ policy choices are deeply influenced by their ‘inability to forecast the likelihood of events happening’ (Bloom 2014 ). There is ample evidence that uncertainty disrupts the real economy (Colombo 2013 ; Jones and Olson 2013 ; Caggiano et al. 2017 ; Connolly et al. 2018 ; Tarassow 2019 ) through its effects on financial markets, namely by restraining economic agents from doing business due to their loss of confidence. At times of high uncertainty or lower confidence, individuals forsake consumption and turn to more savings while corporations restrict new investment projects or new recruitments (Gulen and Ion 2015 ). Further, asset market participants become more cautious, asset prices fall (either through the discount rate or the cash flow channel), volatilities and correlations soar (Pastor and Veronesi 2012 , 2013 ; Li et al. 2015 ; Kelly et al. 2016 ; Bernal et al. 2016 ; Andreasson et al. 2016 ). A higher risk premium increases the cost of capital and generally the corporate funding costs (Alessandri and Mumtaz 2019 ) and erodes confidence in the financial system (see also Wisniewski and Lambe 2015 ; Bordo et al. 2016 ; Boumparis et al. 2017 ; Caliendo et al. 2018 ).

Despite the substantial advances in uncertainty research, the literature on the realized volatility dynamics of high-frequency financial variables associated with uncertainty is still in its infancy. Reviewing the few commendable attempts to explain the behavior of stock market volatility with EPU, we can trace back this link to Pastor and Veronesi ( 2013 ), who were the first to connect stock markets with monthly EPU using simple OLS regressions of monthly stock returns, volatilities, and correlations (unconditional) on the EPU index, whose parameter sign was consistently positive for correlations and volatilities and negative for returns. Antonakakis et al. ( 2013 ) further compute the dynamic conditional correlations between EPU, S&P 500 Stock index returns, and implied volatility (VIX) pairwise on a monthly frequency. The EPU–VIX correlation is positive and the EPU-returns negative, as expected, since elevated uncertainty depresses stock market performance and goes alongside higher stock market volatility. More recently, Fang et al. ( 2018 ) have related daily gold futures volatility with the monthly Global EPU index through the GARCH-MIDAS framework. They provide evidence to support the strong positive effect of uncertainty on gold volatility and its power in forecasting the monthly realized volatility of gold futures. Finally, Cho et al. ( 2018 ) highlight the fact that high exchange rate volatility is linked with elevated EPU leading to carry trade losses.

3 The econometric framework

Building on the benchmark HEAVY model of Shephard and Sheppard ( 2010 ) who combine two volatility estimators in a bivariate system, we apply the HEAVY extension which accounts for downside risk (asymmetries), power terms, and economic effects, and estimate an augmented version including these three additional features to improve volatility modeling and forecasting.

3.1 The benchmark specification

The HEAVY model uses two variables: the close-to-close stock returns ( \(r_{t}\) ) and the realized measure of variation based on high-frequency data, \({\textit{RM}}_{t}\) . We first calculate the signed square rooted (SSR) realized measure as follows: \(\widetilde{{\textit{RM}}_{t}}=\textit{sign}(r_{t})\sqrt{{\textit{RM}}_{t}}\) , where sign \((r_{t})=1\) , if \(r_{t}\geqslant 0\) and sign \((r_{t})=-1\) , if \(r_{t}<0\) .

We assume that the returns and the SSR realized measure are characterized by the following relations:

where the stochastic term \(e_{it}\) is independent and identically distributed ( i.i.d ), \(i=r,R\) ; \(\sigma _{it}\) is positive with probability one for all t and it is a measurable function of \({\mathcal {F}} _{t-1}^{(XF)}\) , that is the filtration generated by all available information through time \(t-1\) . We will use \({\mathcal {F}}_{t-1}^{(HF)}\) ( \(X=H\) ) for the high-frequency past data, i.e., for the case of the realized measure, or \({\mathcal {F}}_{t-1}^{(LoF)}\) ( \(X=Lo\) ) for the low-frequency past data, i.e., for the case of the close-to-close returns. Hereafter, for notational convenience, we will drop the superscript XF .

In the HEAVY/GARCH model \(e_{it}\) has zero mean and unit variance. Therefore, the two series have zero conditional means and their conditional variances are given by

where \({\mathbb {E}}(\cdot )\) denotes the expectation operator. The returns equation is called HEAVY- r and, similarly, the realized measure equation is denoted as HEAVY- R .

3.2 The macro-augmented asymmetric power model

The asymmetric power (AP) specification for the HEAVY(1, 1) model consists of the following equations (in what follows, for notational simplicity, we drop the order of the model if it is (1, 1)):

where L is the lag operator, \(\delta _{i}\) \(\in {\mathbb {R}}_{>0}\) (the set of the positive real numbers), for \(i=r,R\) , are the power parameters and \( s_{t}=0.5[1-\textit{sign}(r_{t})]\) , that is, \(s_{t}=1\) if \(r_{t}<0\) and 0 otherwise; \(\gamma _{ii}\) , \(\gamma _{ij}\) ( \(i\ne j\) ) are the own and cross leverage parameters, respectively Footnote 2 ; positive \( \gamma _{ii}\) , \(\gamma _{ij}\) means a larger contribution of negative ‘shocks’ in the volatility process. In this specification the powered conditional variance, \((\sigma _{it}^{2})^{\delta _{i}/2}\) , is a linear function of the lagged values of the powered transformed squared returns and realized measure.

We will distinguish between three different asymmetric cases: the double one (DA: \(\gamma _{ij}\ne 0\) for all i and j ) and two more, own asymmetry (OA: \(\gamma _{ij}=0\) for \(i\ne j\) only) and cross asymmetry (CA: \(\gamma _{ii}=0\) ).

The \(\alpha _{iR}\) and \(\gamma _{iR}\) are called the (four) Heavy parameters (own when \(i=R\) and cross when \(i\ne R\) ). These parameters capture the impact of the realized measure on the two conditional variances. Similarly, the \(\alpha _{ir}\) and \(\gamma _{ir}\) (four in total) are called the Arch parameters (own when \(i=r\) and cross for \(i\ne r\) ). They capture the influence of the squared returns on the two conditional variances.

The asymmetric power model is equivalent to a bivariate AP-GARCH process for the returns and the SSR realized measure (see, for example, Conrad and Karanasos 2010 ). If all four Arch parameters are zero, then we have the AP version of the benchmark HEAVY specification, where the only unconditional regressor is the first lag of the powered \({\textit{RM}}_{t}\) .

Furthermore, we should mention that all the parameters in this bivariate system should take non-negative values (see, for example, Conrad and Karanasos 2010 ). We extend the realized measure equation with the non-negative macro-proxies: the volatility of US Economic Policy Uncertainty, \({\textit{EPUvol}}_{t}\) , the US financial uncertainty (the S&P 500 implied volatility index), \({\textit{VIX}}_{t}\) , the Bond market (the Merrill Lynch MOVE treasury bonds implied volatility index or the Moody’s BAA over AAA corporate bonds spreads), \({\textit{BO}}_{t}\) , the Commodity benchmarks (the S&P GSCI all commodities index or the S&P GSCI crude oil index prices), \({\textit{CO}}_{t}\) , and the infectious disease news effect on stock markets, \({\textit{ID}}_{t}\) . The macro-augmented (m) AP-HEAVY system is characterized by the following equation for the realized measure:

Equation ( 2 ) incorporates four Macro-parameters, \(\phi _{R}\) , \(\lambda _{R}\) , \(\zeta _{R}\) , \(\vartheta _{R}\) , and \(\eta _{R}\) , which capture the macro-effects on the power transformed realized measure. The returns equation remains the same as in the non-augmented specification, without the direct effect from the macro-variables ( \(\phi _{r},\lambda _{r},\zeta _{r},\vartheta _{r},\eta _{r}=0\) ).

To sum up, the benchmark model is characterized by two conditional variance equations, the GARCH(1, 0)-X formulation for returns and the GARCH(1, 1) formulation for the SSR realized measure:

Equation ( 2 ) gives the general formulation of our macro-augmented extension for \({\textit{RM}}_{t}\) , which adds asymmetries and power transformations to the benchmark specification (see also the Supplementary Appendix for our theoretical considerations). We use the existing Gaussian quasi-maximum likelihood estimators (QMLE) and multistep-ahead predictors already applied (Ding et al. 1993 ) in the APARCH framework (see, for example, He and Teräsvirta 1999 ; Laurent 2004 ; Karanasos and Kim 2006 ). We will first estimate both conditional variance equations in the general form with all Heavy, Arch, and their Asymmetry parameters given by Eq. ( 2 ) and in case a parameter is insignificant, we will exclude it and this will result in a reduced form that is statistically preferred for each volatility process. For example, in the returns and realized measure conditional variances estimation, the own and cross Arch parameters ( \(\alpha _{rr}\) and \( \alpha _{Rr}\) respectively) prove to be insignificant and, therefore, are excluded (see Sect.  5.2 , Table  3 , Panels A and B) since this is the way to reach the returns and realized measure formulations that are statistically preferred.

4 Data description

The HEAVY framework is estimated for five emerging stock index returns and realized volatilities. According to the analysis in Shephard and Sheppard ( 2010 ), this formulation considerably improves the volatility modeling by allowing momentum and mean reversion effects and adjusting quickly to the structural breaks in volatility. We extend their benchmark specification, by adding the features of power transformed conditional variances, leverage, and macro-effects in the volatility process.

4.1 Stock index data

We use daily data for two American and three Asian stock market indices extracted from the Oxford-Man Institute’s (OMI) realized library version 0.3 (Heber et al. 2009 ): Brazil’s Bovespa index (BRAZIL) and Mexico’s IPC index (MEXICO) from the Americas, Shanghai Composite index (CHINA), India’s Nifty 50 index (INDIA), and South Korea’s KOSPI index (KOREA) from Asia. We choose the particular emerging markets due to data (stock index and sample size) availability in the OMI realized library. Our sample covers the period from 03/01/2000 to 30/11/2020 and three out of five indices belong to the BRICS group of emerging markets (Brazil, India, and China). Regarding our research interest in the two Americas’ emerging markets, Brazil plays a dominant economic role internationally and Mexico is the second largest economy in Latin America. For the three Asian countries included, China and Korea are considered among the fastest growing economies while Indian stock markets have progressively undergone a fundamental financial liberalization process since early 1990s.

The OMI’s realized library includes daily stock market returns and several realized volatility measures calculated on high-frequency data from the Reuters DataScope Tick History database. The data are first cleaned and then used in the realized measures calculations (see also the library’s documentation in Heber et al. 2009 ). We use the daily closing prices, \( P_{t}^{C}\) , to form the daily returns as follows: \(r_{t}=[\ln (P_{t}^{C})-\ln (P_{t-1}^{C})]\times 100\) , and two realized measures as drawn from the library: the 5-min realized variance and the realized kernel. The estimation results using the two alternative measures are very similar, so we present only the ones with the realized variance (the results for the realized kernel are available upon request). Table  1 presents the five stock indices extracted from the database and provides volatility estimations for each one’s squared returns and realized variances time series for the respective sample period (see also the stock index series graphs in the Supplementary Appendix, Figure E.1). We calculate the standard deviation of the series and the annualized volatility. Annualized volatility is the square rooted mean of 252 times the squared return or the realized variance. The standard deviations are always lower than the annualized volatilities. The realized variances have lower annualized volatilities and standard deviations than the squared returns since they ignore the overnight effects and are affected by less noise. The returns represent the close-to-close yield and the realized variances the open-to-close variation. The annualized volatility of the realized measure is between 15 and 20%, while the squared returns show figures from 20 to 29%.

4.2 Macroeconomic proxies

In order to shed light on the macro-financial linkages, we augment the financial volatility HEAVY process with five non-negative macro-proxies of daily frequency. Research on the economic drivers of financial volatility lacks evidence on daily macro-factors of the daily or intra-daily stock index volatility pattern. Motivated by this literature gap, we augment the model of both daily and intra-daily volatility with daily macro-variables that proxy the business cycle conditions used in the existing monthly or quarterly studies of volatility determinants. In line with Conrad and Loch ( 2015 ), we proxy the macroeconomic environment through economic activity, monetary and business conditions, and sentiment daily variables that could explain stock index realized variance. Since GDP, industrial production, unemployment, inflation, consumer sentiment, or any commonly-used activity, monetary base, and sentiment index are not measured with a daily frequency, we turn to relevant daily variables. The EPU index is directly related to the business cycle with significant contractive effects on investment and employment (Baker et al. 2016 ). It is used here in place of the activity variables included in all prior studies. We expect the opposite sign effect from the sign previously observed for economic activity variables since uncertainty is negatively correlated to activity and higher uncertainty is strongly associated with recessions. The EPU index applied is also considered as an alternative to sentiment and macroeconomic volatility (Conrad and Loch 2015 ). The S&P 500 implied volatility index is our financial uncertainty proxy (VIX index in Corradi et al. 2013 ). Daily credit condition variables are chosen to account for the business and monetary conditions’ impact on financial volatility, following Schwert ( 1989 ), who uses financial leverage variables, interest rate and corporate bond returns volatility. We further use daily commodity price indices motivated by the fact that commodity price increases and oil, in particular, are often associated with recessions in the macroeconomy (Barsky and Kilian 2004 ). Therefore, we expect a significant surge in stock market volatility following a rise in commodity prices, which has proved harmful for real economic activity. Lastly, we consider the infectious disease news effect on equity market volatility predicting a positive relationship with emerging markets variability.

Our first macro-variable, the news-based Economic Policy Uncertainty index is established by Baker et al. ( 2016 ) and retrieved from http://www.policyuncertainty.com/ . The site, maintained by Baker, Bloom, and Davis, provides daily EPU data for the US starting from 1985. The EPU index effectively captures the broad ‘amorphous’ concept of economic uncertainty (Bloom 2014 ). The 2008 global financial crisis has brought the previously overlooked notion of economic uncertainty to the frontline of academics’, policymakers’ and practitioners’ interest. We are now witnessing an extensive burgeoning literature with uncertainty as its principal topic and exploring the widely-recognized countercyclical uncertainty effects on macroeconomic and financial indicators across the business cycle. In particular, for unique crisis events and long-lasting recession periods, academics try to scrutinize all possible factors from their arsenal of indicators, which could prove to be forces behind the poor economic performance. Uncertainty in the agents’ thoughts has been recently verified as a crucial factor explaining a substantial part of economic fluctuations. Our motivation and recognition of the relative merits of the news-based EPU metric are discussed in the literature review (Sect.  2 ). We further include one more US factor for financial uncertainty, the VIX index of S&P 500 implied volatility.

Moving to the credit market conditions, we use two alternative Bond market global benchmarks: the Merrill Lynch MOVE 1 month index (MOVE) and the Moody’s corporate bonds default spread, defined as the difference of BAA over AAA bond yields (BAA_AAA). The MOVE index is an estimate of the option implied volatility of US treasury bonds. It is the treasury counterpart of the ‘fear’ index (VIX) for S&P 500 and captures the sovereign credit market stance. Higher sovereign bond volatility denotes higher turbulence in the credit channel for sovereigns with direct spillovers to financial and non-financial corporations’ credit conditions. The Moody’s index provides daily averages of global AAA and BAA corporate bond yields (higher spreads denote higher credit risk pricing for corporations, that is higher cost of financing). The Moody’s default spread is used as an alternative to the MOVE index for the credit channel. Moreover, the Commodity market conditions are proxied by two alternative global factors: the S&P GSCI index (GSCI) and the S&P GSCI Crude Oil subindex (GSCI_OIL). Both capture the cost of production for firms in the economy, where rising commodity values can lead to production and investment deterioration due to increased cost effects on economic activity. On the one hand, the S&P Goldman Sachs Commodity Index (all commodities included) is the widely-recognized commodity markets performance benchmark. On the other hand, GSCI crude oil subindex represents the most important commodity as an energy source across all economies. The GSCI_OIL is used as our alternative macro-regressor to the GSCI, where, besides oil, most liquid commodities are incorporated. VIX and the four bonds and commodities variables are retrieved from Thomson Reuters Datastream. Finally, we include the infectious disease detrimental impact on equity market volatility proxied by the Infectious Disease Equity Market Volatility Tracker (ID_EMV) of Baker et al. ( 2020a ). ID_EMV is a newspaper-based metric (available at https://www.policyuncertainty.com ) that quantifies the crucial role of news about infectious diseases (e.g. epidemics/pandemics, MERS, SARS, H1N1, Covid-19, etc.) on US stock market volatility. Given that our sample covers the Coronavirus pandemic crash, we expect a significant impact on realized volatility from disease outbreaks at least during the current health crisis.

VIX, MOVE, GSCI, and GSCI_OIL are log-transformed and together with the BAA_AAA spread and ID_EMV level, they are all included in the realized measure equation, where they are shown to be jointly significant. The squared return series of the EPU index, signifying the volatility of EPU (EPUvol), is added in the realized measure equation to estimate the direct EPU volatility spillover effect, whereas for the indirect effect we use the log-level of EPU Footnote 3 (see Sect.  7 ). In the macro-augmentation of the HEAVY model, we are restricted to using only non-negative variables with estimated parameters of positive sign due to the GARCH positivity constraints. Consequently, we focused our analysis of the macro-financial linkages on the EPU and VIX indices for uncertainty, the four bonds and commodities variables, and the disease proxy, which are characterized by non-negative values only and exert an inflating impact on realized volatility. Increased uncertainty, bond spreads and volatility, commodity prices, and disease impact, all contribute to financial volatility heightening, apparent especially during economic downturns. Figures  1 , 2 , 3 , 4 and 5 clearly show that higher realized volatility is observed mainly in times of elevated uncertainty, credit market turbulence, boosts in commodity prices, and disease outbreaks (mostly during the Covid-19 period).

US EPU and KOSPI Realized Variance

US EPU and Financial uncertainty

US EPU and the Credit market proxies

US EPU and the Commodity market proxies

US EPU and the Infectious disease news impact on equity volatility

Before selecting the five macro-financial regressors for the realized variance equation, we tested a plethora of real activity, monetary, and financial candidates in daily frequency as discussed in the relevant literature. We chose the combination of jointly significant variables that minimize the information criteria and maximize the log-Likelihood score. Given the GARCH positivity constraints, we had to exclude the variables with negative values and the variables with a negative impact on volatility. For example, confidence indices (e.g. the daily News Sentiment Index-NSI from the FRB San Francisco dataset, Buckman et al. 2020 ) were excluded given their negative-signed effect on volatility and replaced by the uncertainty proxies (the sentiment antipode of confidence). Furthermore, the 3-month libor and treasury bill yields, as well as the daily global Financial Stress Index-FSI of the Office of Financial Research (OFR) could not be included as credit condition proxies since their time series take negative values. We additionally tested a non-negative proxy of the real estate market (the log-transformed Dow Jones [DJ] REIT index). This proved to be highly significant but we should exclude it since the negative sign of the relevant parameter violates our econometric framework constraints. Better performance of the real estate sector is associated with higher REIT’s level mostly in economic growth periods and is consistently negatively related to financial volatility. Finally, the realized variance receives sound negative impact from two economic activity indicators with values not bounded to the positive territory of real numbers and, therefore, they have been excluded. We used the Aruoba–Diebold–Scotti (ADS) Business Conditions index (Aruoba et al. 2009 ) and the Yield Curve slope, which are among the unique economic activity indicators available on a daily frequency. The ADS index tracks daily real business conditions based on economic data releases and the Yield Curve slope, as calculated by the difference of the 10-year minus the 3-month treasury bond yields, and it has proved to be a powerful predictor of future economic activity (Estrella and Hardouvelis 1991 ). Financial volatility receives a significant negative effect from both variables, as expected since lower ADS and term structure slope values indicate economic worsening associated with higher stock market volatility. This opens several paths for future research on macro-financial linkages in the high-frequency domain to connect real activity variables (such as DJ REIT, ADS, Yield Curve slope), excluded here, with realized variation measures in the absence of positivity constraints of the econometric framework applied. Footnote 4

5 Empirical findings

Building upon the introduction of the GARCH-X process by Engle ( 2002 ) to include realized measures as exogenous regressors in the conditional variance equation, Han and Kristensen ( 2014 ) and Han ( 2015 ) studied the asymptotic properties of this new specification with a fractionally integrated (nonstationary) process included as covariate (see also Francq and Thieu 2019 ). Moreover, Nakatani and Teräsvirta ( 2009 ) and Pedersen ( 2017 ) focused on the multivariate case, the so-called extended constant conditional correlation, which allows for volatility spillovers and they developed inference and testing for the QMLE parameters (see also Ling and McAleer 2003 for the asymptotic theory of vector ARMA-GARCH processes). For the extended HEAVY models, we employ the existing Gaussian QMLE and multistep-ahead predictors applied in the APARCH framework (see, for example, He and Teräsvirta 1999 ; Laurent 2004 ; Karanasos and Kim 2006 ). Following Pedersen and Rahbek ( 2019 ), we first test for arch effects and after rejecting the conditional homoscedasticity hypothesis we apply one-sided significance tests of the covariates added to the estimated GARCH processes.

5.1 The benchmark HEAVY results

Within the HEAVY framework, we first estimate the benchmark formulation as in Shephard and Sheppard ( 2010 ), that is, without asymmetries, power transformations, and macro-effects, obtaining very similar results (Table  2 ). The only unconditional regressor in both equations is the first lag of the \({\textit{RM}}_{t}\) . In other words, the chosen returns equation is a GARCH(1, 0)-X process dropping out the own Arch effect, \(\alpha _{rr}\) , from lagged squared returns since it becomes insignificant when we add the cross effect of the lagged realized measure as regressor, with a Heavy parameter, \(\alpha _{rR}\) , high in value and significance across all indices. The momentum parameter, \(\beta _{r}\) , is estimated around 0.47–0.80. For the SSR realized variance, the best-chosen model is the GARCH(1, 1) without the cross effect from lagged squared returns. The Heavy term, \(\alpha _{RR}\) , is estimated between 0.32 and 0.50 and the momentum, \(\beta _{R}\) , is around 0.49–0.67. The benchmark HEAVY system of equations chosen, after testing all three alternative GARCH models of order (1, 1), (1, 1)-X, and (1, 0)-X, is the same as in Shephard and Sheppard ( 2010 ) with similar parameter values and the identical conclusion that the realized measure of variation does all the work at moving around the conditional variances of stock returns and the SSR realized variance. The benchmark’s conclusion, as we show in this study, does not hold for the more richly parametrized macro-augmented asymmetric power model. More importantly, according to the Sign Bias test (SBT) of Engle and Ng ( 1993 ), the asymmetric effect is obviously omitted from the benchmark specification with the sign parameter always significant (SBT p-values lower than 0.04).

5.2 The macro-augmented asymmetric power HEAVY results

Moving to the extension of the benchmark HEAVY system, Table  3 presents the estimation results for the chosen macro-augmented asymmetric power specifications. Wald and t -tests are used to test the significance of the Heavy and Arch parameters, rejecting the null hypothesis at 10% in all cases. We should highlight the fact that since all the parameters take non-negative values, we use one-sided tests (see, for example, Pedersen and Rahbek 2019 ). For both returns and realized variance, we statistically prefer either the double asymmetric power (DAP) specification since both power transformed conditional variances are significantly affected by own and cross asymmetries or the cross (own) asymmetric power—CAP (OAP)—model when only the cross (own) asymmetries are included. We estimate the power terms separately with a two-stage procedure, as follows: We, first, estimate univariate asymmetric power specifications for the returns and the realized measure. The Wald tests for the estimated power terms (available upon request) reject the hypotheses of \(\delta _{i}=1\) and \(\delta _{i}=2\) in most cases. In the second stage, we use the estimated powers, \(\delta _{r} \) and \(\delta _{R}\) , from the first step to power transform each series’ conditional variance and incorporate them into the bivariate model. The sequential procedure produces the fixed power term values, which are the same for both specifications ( \(\delta _{r}\) and \(\delta _{R}\) are common for Panels A and B).

For the returns (Panel A), the estimated power, \(\delta _{r}\) , lies between 1.30 and 1.60 (see Panel C). The Heavy cross effect parameter, \(\alpha _{rR}\) , is significant in most cases, except for Mexico. The Heavy cross asymmetry, \(\gamma _{rR}\) , is insignificant and excluded in China and India equations. Consequently, DAP returns specification is preferred for three out of five indices (Brazil, Mexico, Korea) and OAP is chosen for the other two cases (China, India). Although \(\alpha _{rr}\) is insignificant and is excluded in all cases, the own asymmetry parameter is always significant with \(\gamma _{rr}\in [0.05,0.14]\) . In other words, the lagged values of both powered variables, that is, the realized measure and the squared negative returns, drive the model of the power transformed conditional variance of the returns. Moreover, the momentum parameter, \(\beta _{r}\) , is estimated to be around 0.77 to 0.93. All five indices generated very similar DAP/OAP specifications without macro-effects since we statistically prefer to include the macro-regressors in the realized measure equation. Similarly, for the realized measure the most preferred specification is the m-CAP one (for Mexico only, we choose the m-DAP model). The power, \(\delta _{R}\) , is estimated from 1.00 to 1.20 and is consistently lower than the returns power term (see Panel C). The Heavy parameter, \(\alpha _{RR}\) , is always significant and around 0.19 (min. value) to 0.40 (max. value), while the own asymmetry, \(\gamma _{RR}\) , appears only in the realized measure equation of Mexico (see Panel B). Moreover, the cross asymmetry Arch parameter is always significant with \(\gamma _{Rr}\in [0.02,0.05]\) . This means that the power transformed conditional variance of \({\widetilde{{\textit{RM}}}} _{t}\) is significantly affected by the lagged values of both powered variables: squared negative returns and realized measure. Further, the momentum parameter, \(\beta _{R}\) , is estimated to be around 0.52 to 0.73.

Lastly, the lagged macro-effects are highly significant, with the expected positive sign in all cases (see Panel B). The power transformed realized variance receives the boosting impact from higher volatility of the US EPU index in all but one case (China), \(\phi _{R}\in [0.01,0.02]\) , in line with Pastor and Veronesi ( 2013 ), who were the first to associate stock market volatilities with EPU, resulting in a positive link. The US financial uncertainty effect of VIX, \(\lambda _{R}\) , is significant only for Brazil and Korea. The uncertainty effects confirm the finding of Conrad and Loch ( 2015 ), among others, on the negative effect of consumer confidence (University of Michigan Consumer Sentiment index), which is the opposite sentiment to uncertainty and is estimated here with the expected opposite sign, as well. Regarding the bond and commodity markets, we prefer to use common global proxies across all emerging stock markets. Bond market conditions are captured by either the MOVE index (Brazil, Korea) or the Moody’s default spread (Mexico, China, India). Increased US treasury implied volatility or elevated corporate bond default spreads raise realized volatility in stock markets ( \(\zeta _{R}\in [0.01,0.06]\) ), as expected since the turbulence in the credit markets always gives significant volatility spillover effects to stock markets. Hereby, we confirm, among others, Engle and Rangel ( 2008 ), who estimate a positive effect of short-term government bond interest rate volatility on stock market volatility through the Spline-GARCH specification. Turning to commodities ( \( \vartheta _{R}\in [0.01,0.04]\) ), we prefer the GSCI all commodities index in three cases, while, for Mexico and India, the GSCI oil subindex is the chosen commodity regressor. Lower commodity prices mean decreased cost of supplies for firms in the economy, propelling productivity, investment, and, more generally, economic growth and, at the same time, reducing stock market volatilities. Given that increased oil prices are mostly coincident with recession periods (Barsky and Kilian 2004 ), the positive link of realized variance and commodity prices, captured by \(\vartheta _{R}\) , supports the negative association of economic activity with stock market volatility, in accordance with the existing literature. All prior volatility determinant studies have provided sound evidence on the negative sign effect of economic activity proxies on stock market volatility (see, for example, the GDP growth parameters in Engle and Rangel 2008 ). Finally, the coefficient of the fifth macro-regressor, \(\eta _{R}\) , is significant for three out of five cases with the ID_EMV index tracking the infectious disease news impact on US equity volatility and spreading the disease effect to Brazil’s, India’s, and Korea’s stock markets.

Overall, our results show strong Heavy effects (captured by the \(\alpha _{rR} \) , \(\gamma _{rR}\) , and \(\alpha _{RR}\) parameters), as well as asymmetric Arch influences (the estimated \(\gamma _{rr}\) and \(\gamma _{Rr}\) are always significant) and macro-impacts (measured by \(\phi _{R}\) , \(\lambda _{R}\) , \(\zeta _{R}\) , \(\vartheta _{R}\) , and \(\eta _{R}\) ). According to the log-Likelihood ( \({\textit{lnL}}\) ) values reported, the log-Likelihood is always higher for the m-DAP specifications compared to the benchmark one, that is without asymmetries, powers, and macro-effects, proving the superiority of our model’s in-sample estimation (see also the comparison of the two models in terms of the Bovespa standardized residuals graphs in the Supplementary Appendix, Figure E.2). The SBT statistics further show that the asymmetric effect is not omitted any more since the sign parameters are insignificant with p-values consistently higher than 0.17. Table  9 (in the Appendix) provides additional results for the realized measure equation with the DAP extension before including the macro-effects. We followed the particular stepwise estimation procedure before selecting our final chosen model with powers, asymmetries, and all five macro-factors.

5.3 Macro-effects discussion

From an economic point of view, the macro-effects on stock volatility observed through the m-DAP framework confirm prior studies on the upward volatility trajectory during economic downturns. This counter-cyclical behavior has been mainly shown by the negative sign effect of economic activity leading or coincident indicators with a monthly or quarterly frequency (Engle and Rangel 2008 ). Turning to the high-frequency domain of the macro-financial linkages, the monthly activity variables should be replaced by possible daily proxies of economic activity to be included as explanatory variables in the realized variance equation. Given the non-negativity restriction, we could not use, among others, the daily term spread, a reliable predictor of GDP (Estrella and Hardouvelis 1991 ) and significant in the monthly context as evidenced by Conrad and Loch ( 2015 ). Based on the rich empirical evidence of the adverse uncertainty effects on economic activity (Caggiano et al. 2017 ; Colombo 2013 ; Jones and Olson 2013 ), we select the daily EPU index to associate stock market volatility with a variable directly linked to economic activity contractive forces. The positive sign consistently estimated here across all specifications for the EPUvol variable is in accordance with prior findings on the positive sign given to macroeconomic uncertainty (Schwert 1989 ) and unemployment, and the negative sign of the real GDP, industrial production, and consumer sentiment growth (Conrad and Loch 2015 ). All forces associated with a positive real economic impact exert a negative influence on stock market fluctuations, while the depressive forces exacerbate volatility and are estimated with a positive sign irrespective of the specification chosen by different scholars. Therefore, it is economically plausible for both uncertainty proxies to drive financial volatility higher, at the same time weakening the prevailing macroeconomic conditions.

Against this backdrop, we also selected the sovereign bond yield volatility (or, alternately, the corporate bond default spread level) to identify the credit channel effect on stock markets. Increased volatility in the sovereign bond market (Engle and Rangel 2008 ) or corporate debt spreads are reasonably correlated with macroeconomic turbulence since they increase the cost of financing for firms and investors and, consequently, reduce activity. Accordingly, the global bond factor parameters are consistently estimated with positive signs across all stock market volatility models (see also Asgharian et al. 2013 ). Further, the commodity index or, alternately, the oil subindex are included as a volatility determinant, which is found positive and highly significant in all cases. Motivated by the widespread discussion and empirical evidence about the commodity price effects on the macroeconomy in Kilian’s research works (see, for example, Barsky and Kilian 2004 ), we complement the volatility macro-determinants literature by enriching the set of significant macro-variables for the volatility pattern with commodities and observe the destabilizing impact of higher daily commodity prices, mostly associated with economic downturns, on stock market realized variance. Increased commodity costs for firms’ production supplies impair economic activity and exacerbate equities’ volatility. Finally, we demonstrate that the infectious disease effect on US equities has a detrimental impact exacerbating emerging markets turbulence.

Hence, apart from contributing to the emerging markets realized variance modeling research through the asymmetric, power, and macro-augmentation of the benchmark HEAVY specification applied on emerging economies, we also contribute to the economic sources of volatility by exploring the macro-financial linkages in the high-frequency domain with daily macro-proxies. All daily economic variables that exacerbate developing stock market volatility are associated with weak economic conditions: higher uncertainty, tighter credit conditions, increased commodity prices, and significant disease news impact on equity markets. Moreover, we bridge the macro-finance literature with the high-frequency volatility studies by using the sole economic uncertainty index computed daily. The daily US EPU is applied in the present emerging markets study to reveal the uncertainty spillovers from the US across emerging market economies in Asia and the Americas. The US-led spillover is crucial given its direct connection to the turbulence that surrounds the policy initiatives under Trump’s administration on trade relations or Covid-19 spread, for instance, and the expected governance by the recent new President-elect, which trigger agents’ uncertainty feelings spread over the whole world.

5.4 The crisis effect on realized volatility

After investigating the significant macro-financial linkages in emerging economies, we further explore the significant effect of two crisis events on equity markets, one financial and one health crisis: the 2008 Global Financial Crisis (GFC) and the Covid-19 pandemic period (COVID). The current pandemic has already ignited a new and probably deeper global socio-economic crisis with massive fiscal and monetary stimulus provided by governments, by far larger than the response to the 2008 crisis (Snower 2020 ). Market turbulence is already observed through markedly increased volatilities close to the peak reached during the 2008 global crisis. Markets are seriously affected by the generalized fear about controversial economic policies to support societies and the financial system, especially in the case of the heavily criticized US government’s delayed and deficient response. Given an unprecedented and challenging threat, namely the rapidly contagious virus across the whole universe, economic agents feel uncertainty about future government policy choices, their implementation, and their potential impact, as well. Even if governments reassure them that the harmful effects of Covid-19 are manageable, skepticism, criticism, and loss of confidence are still there and captured by soaring uncertainty index levels.

Stock market volatility reached a record peak in mid-March when the World Health Organization (WHO) characterized the Coronavirus outbreak as a pandemic while daily EPU levels jumped and still remain in higher territories than during the pre-Covid era. In this vein, we assess the GFC and COVID effect on the daily macro-financial linkages explored in this study by enriching the m-DAP-HEAVY- R equation [see Eq. ( 2 )] with the crisis slope dummies ( \(D_{{\textit{CRISIS}},t}\) ) on each Heavy, Arch, and Macro parameter [see Eq. ( 3 )], capturing the two crises impact. Based on the Bank for International Settlements (BIS) and WHO timelines the two crisis subsamples are defined as follows:

GFC: 09/08/2007–31/03/2009. The GFC period starts with the announcement that three major BNP Paribas investment funds are suspended and ends in the first quarter of 2009 with gradual restoration of markets’ ‘tranquillity’.

COVID: 09/01/2020–30/11/2020. The COVID period starts with the first death reported by China in January 2020 while the pandemic crisis is still in place until the end of our sample.

Following the GFC and COVID timelines, we first construct the respective crisis dummies \(D_{{\textit{CRISIS}},t}\) , with \({\textit{CRISIS}}={\textit{GFC}},{\textit{COVID}}\) , as follows:

\(D_{{\textit{GFC}},t}=1\) , if t in the GFC period else \(D_{{\textit{GFC}},t}=0\)

\(D_{{\textit{COVID}},t}=1\) , if t in the COVID period else \(D_{{\textit{COVID}},t}=0\) .

Second, we multiply the crisis dummies with the m-DAP-HEAVY- R equation’s variables to construct the slope dummies for the respective Heavy, Arch, and Macro effect. The realized variance equation with the crisis impact is estimated as follows:

where the superscript \(^{{\textit{CRISIS}}}\) denotes the coefficients of the crisis slope dummies.

Table  4 summarizes the financial and health crisis effect as estimated through alternative restricted forms of Eq. ( 3 ) by including separately each crisis slope dummy of the Heavy, Arch, and Macro parameters. The GFC and COVID impacts (Table  4 , Panel A and B, respectively) magnify most Heavy terms ( \(\alpha _{RR}^{{\textit{GFC}}}\) , \(\alpha _{RR}^{{\textit{COVID}}}\) ) and Arch asymmetries ( \(\gamma _{Rr}^{{\textit{GFC}}}\) , \(\gamma _{Rr}^{{\textit{COVID}}}\) ). Additional results with both crises dummies jointly significant in the bivariate m-DAP system are reported in Table  10 of the Appendix, where we present the whole equations’ estimations with the preferred combination of GFC and COVID dummies incorporated in the returns and realized measure specifications. The log-Likelihood score of the m-DAP-HEAVY model with crisis dummies demonstrates a slight improvement of the model’s in-sample fit (compare Tables  3 and  10 ) which is not transferred to the out-of-sample forecasting performance (see Sect.  6 ). Turning to the crisis impact on the macro-drivers of realized variance, we observe that both GFC- and Covid-induced turbulence in equity markets is important with an inflating impact on the positive effect of the Macro parameters, similarly to the crisis increment estimated for the Heavy and Arch terms. Interestingly, the EPU crisis coefficient ( \(\phi _{R}^{{\textit{GFC}}}\) , \(\phi _{R}^{{\textit{COVID}}}\) ) is always significant, even in the Chinese case, which is estimated insignificant for the whole sample (Table  3 , Panel B). Financial uncertainty during GFC and COVID ( \(\lambda _{R}^{{\textit{GFC}}}\) , \(\lambda _{R}^{{\textit{COVID}}}\) ) is estimated for Brazil and Korea only and remains insignificant in the Korean case during GFC. The credit conditions proxies ( \(\zeta _{R}^{{\textit{GFC}}}\) , \(\zeta _{R}^{{\textit{COVID}}}\) ) are also amplified in crisis periods for all indices whereas the commodity factors ( \(\vartheta _{R}^{{\textit{CRISIS}}}\) ) become insignificant during the current pandemic in most emerging markets (except for Korea where the crisis effect on commodity regressors is insignificant for both GFC and COVID). The lack of a commodity effect during COVID is rather expected given the sharp drop of crude oil prices following the Covid-19 outbreak (the West Texas Intermediate-WTI crude oil price fell to negative territory instantly in April 2020 for the first time in history), which was partly rebounded with a moderate increase after April 2020 (see also Fig.  4 ). The GSCI indices remained non-negative during the drop phase and therefore they were preferred compared to alternative commodity variables such as the WTI crude oil price. Finally, the infectious disease devastating news impact is more important during COVID, as expected, while in the GFC subsample, a period closer to the H1N1 pandemic started from the US, the ID_EMV crisis dummy is significant only for India.

Overall, the financial and health crisis detrimental impact on the realized variance is demonstrated through the positive increments added to the variance parameters by the slope dummies included in Eq. ( 3 ). Along this line, we, hereby, show once more the counter-cyclical volatility pattern given that the macro-factors associated with weaker economic stance (higher uncertainty, tighter credit, elevated commodity prices, and heavier infectious disease news impact) exacerbate volatility with an upshot intensified during crisis periods.

6 Forecasting performance

Beyond demonstrating the in-sample superiority of the m-DAP extension compared to the benchmark model, we investigate the out-of-sample performance of the augmented specification. From a utilitarian point of view, the success of our model can only be claimed through the strong evidence of its superior predictive power. Therefore, we calculate multistep-ahead out-of-sample forecasts in order to compare the forecasting accuracy of our proposed specification with the benchmark model of Shephard and Sheppard ( 2010 ) for both returns and realized variance, and the three standard models: the GARCH(1, 1) for returns and the common ARFIMA(1,  d , 1) and HAR-RV specification for realized variance.

We compute 1-, 5-, 10-, 20-, and 100-step-ahead variance forecasts for the benchmark HEAVY, the DAP, its macro-augmented extension, the m-DAP with crisis dummies (see Table  10 , in the Appendix), and the standard models [GARCH(1, 1), ARFIMA(1,  d , 1) and HAR-RV]. We apply a rolling window in-sample estimation using 3000 observations (the initial in-sample estimation period for BRAZIL spans from 3/1/2000 until 7/3/2012). Each model is re-estimated daily based on a 3000-day rolling sample. The resulted out-of-sample forecasts of each specification calculated for BRAZIL are as follows: 2146 one-step-ahead, 2142 five-step-ahead, 2137 ten-step-ahead, 2127 twenty-step-ahead, and 2047 one-hundred-step-ahead forecasted variances. We then use the time series of the forecasted values to compute the mean square error (MSE) and the QLIKE Loss Function (Patton 2011 ) of each point forecast compared to the respective actual value. For each formulation and each forecast horizon, we calculate the average MSE and QLIKE to build the ratio of the forecast losses for each extended HEAVY specification (DAP and m-DAP) and each standard model (GARCH, ARFIMA, HAR) to the loss of the benchmark one. A ratio lower than the unity indicates the forecasting superiority of the extended models relative to the benchmark one. A ratio higher than the unity indicates the forecasting superiority of the benchmark model relative to the standard ones. The lowest ratio means the lowest forecast losses, that is the model with the best forecasting performance. Based on the MSE calculations, we further apply the test for the pairwise comparison of competing models (here the benchmark specification vs. the DAP extensions) suggested by Harvey et al. ( 1998 ), HLN thereafter. The HLN forecast encompassing test was introduced as a modification to the Diebold-Mariano test (Diebold and Mariano 1995 ) to account for the fact that models are nested (here the DAP nests the benchmark specification). HLN test whether the differences between the two formulations’ forecasts are statistically significant and the larger model’s forecast losses are lower than the nested model’s ones (see also Clark and McCracken 2001 ).

We apply the optimal predictor \(\left| {\mathbf {r}}_{t}\right| ^{\wedge \varvec{\delta }}\) (see also the optimal predictors derivation in Section B.3, Proposition 3 of the Supplementary Appendix) and calculate the out-of-sample forecasts. The results, presented in Tables  5 and  6 for Brazil’s Bovespa index (similar forecasting results for the other four indices available upon request), clearly show the preference for the macro-augmented extensions over the benchmark models across all time horizons. The m-DAP specification dominates the benchmark model with the lowest MSE and QLIKE (Table  5 ). For the returns equations (see Table  5 , Panel A), the m-DAP formulation dominates the alternative benchmark HEAVY- r with the lowest MSE and QLIKE in all forecasting periods and the five- and ten-day forecast losses slightly lower for the m-DAP specification with crisis dummies. In the realized measure equation (see Table  5 , Panel B), we obtain the best forecasting performance in the m-DAP specification without crisis dummies in most cases. Comparing the forecast losses of the macro-augmented models with and without crisis dummies, we observe that the differences are small, similarly to the slight differences of their in-sample fit (log-Likelihood scores in Tables  3 ,  4 , and 10 ). Given the HLN test, the Asymmetric Power formulations perform significantly better than the benchmark models. HLN test results (Table  6 ) reject the null hypothesis of equal forecasts in favor of the DAP models’ lower forecast losses at 5% significance level while the difference of the forecast losses between the m-DAP specifications with and without crisis dummies is not significant for both returns and realized measure (p-values \(>0.100\) ).

Overall, the extended specifications perform better than the benchmark HEAVY and standard models in the short- and long-term horizons, with the forecasts significantly closer to the actual values for the enriched formulations. Our enhanced in-sample estimations with asymmetries, leverage, and macro-effects have transferred their predictive superiority to the out-of-sample computations. Investors and risk managers should utilize our macro-informed framework’s short-term predictions. At the same time, policymakers can benefit from our superior longer-term forecasts to build reliable scenarios on future financial volatility given the important informational contribution of the daily macro-effects.

7 The indirect uncertainty effect

Following the estimation of the benchmark HEAVY system with asymmetries, power transformations, and macroeconomic effects, and its sensitivity to financial and health crises, we investigate the drastic influence of uncertainty on financial volatility. Over the decade following the global turmoil, which sharply sparked the interest in the role of uncertainty and the relevant research increasingly gained momentum following an accelerating pace, the most widespread metrics documented, or proxies used, have referred to macroeconomic, financial, and policy uncertainty. They all share a common and highly plausible stylized fact: their guiding significance with a detrimental impact on the health of the economy and financial markets, which is stage-contingent (dampening economic activity with higher magnitude in shakier times—see also our crisis sensitivity analysis in Sect.  5.4 ). Despite the rapidly growing EPU literature, it appears that the empirical work on the realized volatility dynamics driven by EPU is limited, with evidence still scant for the emerging world, in particular. Consequently, the present study fills a notable gap in the extant EPU literature. We elucidate whether EPU exerts considerable influence on the HEAVY volatility modeling framework and on specific parameters of the macro-augmented asymmetric power specification. Our work differs from the existing literature in the use of the daily EPU index as a daily realized volatility determinant in emerging stock markets, with major implications for macro-informed trading in financial markets and policymakers’ financial stability concerns and systemic risk oversight. Obviously, the particular EPU-volatility link has not yet been thoroughly assessed.

7.1 The indirect EPU impact on realized volatility

Against this backdrop, we have already highlighted the direct positive effect, in line with Pastor and Veronesi ( 2013 ), and the forecasting power of daily EPUvol on realized volatility within the m-DAP framework in Sects. 5 and 6 . In this Section, we extend our empirical analysis by focusing more specifically on the first volatility macro-determinant of the realized measure equation, that is the economic uncertainty impact on stock indices realized variance. In what follows, we prove the significant EPU effect on the Heavy, Arch, uncertainty, bonds, commodities, and infectious disease news impact on the stock market realized variance. The m-DAP realized volatility equation is estimated using eight restricted forms alternately to examine each EPU effect separately with the following interaction terms: (i)–(iii) \(\alpha _{RR}^{{\textit{EPU}}}\) , \(\gamma _{RR}^{{\textit{EPU}}}\) , and \( \gamma _{Rr}^{{\textit{EPU}}}\) , are the parameters of the lagged EPU multiplied by the lagged realized variance and the two asymmetric effects, capturing the EPU effect on the Heavy ( \(\alpha _{RR}\) and \(\gamma _{RR}\) ) and asymmetric Arch ( \(\gamma _{Rr}\) ) parameters, (iv)–(viii) \(\phi _{R}^{{\textit{EPU}}}\) , \(\lambda _{R}^{{\textit{EPU}}}\) , \(\zeta _{R}^{{\textit{EPU}}}\) , \(\vartheta _{R}^{{\textit{EPU}}}\) , and \(\eta _{R}^{{\textit{EPU}}}\) measure the EPU effect on EPUvol, financial uncertainty, bonds, commodities, and disease news proxies, respectively. The interaction terms are again calculated through the multiplication of the log-transformed EPU index level by the respective variable and included in Eq. ( 2 ) as follows:

where the superscript \(^{{\textit{EPU}}}\) denotes the coefficients of the EPU interaction terms.

The direct EPUvol effect is already apparent through the significant \(\phi _{R}\) estimated in the m-DAP-HEAVY- R equation (Sect.  5.2 , Table  3 , Panel B). Table  7 summarizes the indirect EPU effects on realized volatility of the five emerging stock indices. We present the uncertainty impact on each parameter given by the alternative restricted forms of Eq. ( 4 ), including each interaction term one by one. They are all estimated with highly significant positive signed coefficients, signifying the amplifying EPU impact on each parameter. Intriguingly, within the macro-enriched DAP specification, we demonstrate that higher economic policy uncertainty means a stronger influence of EPU volatility, financial uncertainty, credit conditions, commodity market benchmarks, and infectious disease news on the realized measure. It is noticeable that EPU absorbs a significant part of the Heavy, Arch, and Macro-effects. Within the uncertainty literature, the link between credit condition tightening and uncertainty has recently been investigated by Alessandri and Mumtaz ( 2019 ), who associate the rising financing costs for firms with credit market uncertainty, while the commodities-uncertainty relation is widely explored by Antonakakis et al. ( 2014 ), Aloui et al. ( 2016 ), and Fang et al. ( 2018 ) among others. Most notably, Antonakakis et al. ( 2017 ) focus on the oil prices-stock market volatility link. According to our review of the flourishing research on uncertainty effects, academics have not yet covered the EPU, credit, commodities, and disease macro-effects on intra-daily emerging markets’ financial volatility and the EPU amplifying role on the credit and production cost channel, alongside the pandemic news impact, as well, which is plainly visible here through the HEAVY framework.

7.2 The indirect EPU impact on realized volatility during crisis

Next, we combine the EPU with the crisis impact to estimate the uncertainty effect on each realized variance parameter during crisis periods, separately. The in-crisis EPU impact on emerging equity realized volatility dynamics is captured by the coefficients with the superscript \(^{{\textit{EPU}}\_{\textit{CR}}}\) in the following equation:

where \({\textit{CRISIS}}\) and \({\textit{CR}}={\textit{GFC}},{\textit{COVID}}\) . Each EPU interaction term of Eq. ( 4 ) is multiplied with the crisis slope dummies applied in Eq. ( 3 ) in order to identify the indirect EPU effect during crises.

Table  8 reports the crisis impact on the EPU interaction terms as estimated through restricted forms of Eq. ( 5 ) by including each crisis-EPU term separately. Footnote 5 Similar to our crisis analysis (Table  4 ), we observe that the EPU interaction terms are significantly inflated during the 2008 financial turmoil (Panel A) in most cases. The Heavy, Arch, and EPUvol effects are augmented through the uncertainty (level) channel in the GFC period for all indices included, and the Chinese market as well, contrary to the EPUvol impact whose EPU interaction term is estimated insignificant and excluded in the whole sample for China (see Table  7 , \(\phi _{Rr}^{{\textit{EPU}}}\) insignificant for the Chinese index). US financial uncertainty, credit, and commodity conditions are also intensified for the models where they are incorporated apart from the Korean case. The indirect EPU effect on infectious disease news is important only for India in the GFC period while, during COVID (Panel B), it is significant for Brazil, India, and Korea. Furthermore, in the COVID period, most Heavy, credit, and commodity factors do not receive a statistically significant EPU impact, whereas Arch asymmetries and EPUvol interaction terms escalate the respective effect across all markets.

All in all, our contribution to the EPU literature consists of the new empirical evidence we provide on the positive link between daily EPU and emerging markets realized volatility and the US EPU volatility spillovers across emerging economies which are sensitive to crisis periods and higher EPU levels. Within the HEAVY framework, we firstly demonstrate the US EPUvol destabilizing impact on emerging stock markets with financial volatility investigated in a daily frequency. Secondly, we show that the leverage and heavy effects on realized variance are considerably magnified in financial and health crisis events and under higher prevailing uncertainty conditions. Thirdly, and most interestingly from an economic perspective, the increased VIX and volatility in credit conditions (or higher credit risk pricing in cases where the Moody’s corporate default spreads are applied), the rising prices in commodities, the disease news overflow, all three phenomena associated with economic downturns, exacerbate realized volatility to a degree intensified by elevated US EPU and crisis turbulence. Finally, we complement the literature on EPU spillovers (see, for example, Gabauer and Gupta 2018 ; Balli et al. 2017 , and Klößner and Sekkel 2014 ) by providing evidence of the daily uncertainty spillover effects from the US to emerging stock markets’ intra-daily volatility. We have demonstrated that policy uncertainty in a specific country is not confined to the country’s borders but is propagated across the whole world immediately (only the first EPU lag is examined in this study).

8 Policy implications discussion

Nowadays, our results should urge policymakers to consider and closely investigate the side effects of US policy uncertainty generated in recent years mostly by Trump’s controversial rhetoric and administration for the whole developing world. Overall, we demonstrate that emerging financial markets in Asia and the Americas are destabilized by higher policy uncertainty in the US economy directly (EPU volatility) and indirectly (EPU log-level), besides US financial uncertainty, global commodity and credit market conditions, and the infectious disease news impact, as well. The macro-effects on index volatility are significantly inflated by elevated EPU levels and the detrimental impact of crisis events, both financial and health emergencies. Turning to the policy implications of the macro-augmented high-frequency volatility model, our findings suggest that policymakers and authorities supervising and regulating the financial system should take into account reliable volatility forecasts in designing macro- and micro-prudential policy responses. Regulators could consider the macro-informed financial volatility forecasts of the m-DAP-HEAVY model across the whole risk management process of the financial system (identification of risk sources, assessment of the nature of risk factors, risk measurement, and risk mitigation) and the financial stability oversight tools, such as early warning systems, macro stress tests on financial institutions, and bank capital frameworks.

For example, based on our sensitivity analysis with the crisis magnifying impact and the EPU spillover effect on volatility macro-determinants, early warning systems for emerging economies should consider the US uncertainty channel and past global turmoil periods in identifying forward-looking signals which could imply a future market crash. Further, the macro stress test scenario inputs, which include, among others, stock market volatility predictions for the financial institutions’ trading books, should consider macro-informed volatility estimates to account for the macro-effects on financial markets. Economic uncertainty in one major country has been shown to play a decisive role across multiple regions’ equities. Accordingly, it is essential for supervisory authorities to add the US uncertainty factor in banks’ stress tests while facing the US policy turbulence. Moreover, complying with the capital and risk frameworks set by supervisors (Basel committee and central banks), financial institutions measure their trading portfolio’s market risk through internal models of daily Value-at-Risk (VaR) in order to estimate the potential trading losses over a pre-defined holding period for a given confidence level and define the corresponding capital charges. The most important input in the VaR calculation is the one-day volatility forecast of each risk factor relevant to the financial instruments under scope. Stock index price volatilities are widely used in the VaR computation of stock portfolios. Thus, reliable macro-informed volatility forecasts, provided by a macro-augmented volatility modeling framework, improve the VaR estimates considerably. Given that the market risk capital requirement is calculated on the trading portfolio’s total 99% VaR (absolute value, 60-day average) adjusted by the penalty of the backtesting exceptions (higher than 4 in the 250-day sample), supervisors should encourage banks to improve their market risk internal models with more accurate macro-informed volatility forecasts which better capture the loss distribution without inflating the capital charges.

Beyond our tangible results’ implications for policymakers, the volatility forecasts produced by the m-DAP-HEAVY model are directly applicable to a wide range of business finance operations. Alongside the well-established risk management practice of the trading VaR estimation, portfolio managers should rely on the proposed framework to predict future volatility in asset allocation and minimum-variance portfolio selection complying with their clients’ risk appetite. Risk-averse investors’ mandates specify low volatility boundaries on their portfolio positions, while risk lovers allow for higher volatilities on the risk-return trade-off of their investments. Accurate volatility predictions can also be used in a forward-looking performance evaluation context, through the risk-adjusted metrics, i.e. the Sharpe or the Treynor risk-adjusted return ratios (see, for example, Ben Ameur et al. 2018 ). Traders and risk managers focus on the volatility trajectory in derivatives pricing, volatility targeting strategies, and macro-informed trading decisions. Trading and hedging in financial markets depend on risk factors whose predicted volatilities are the main input of any pricing function applied. Lastly, financial chiefs consider volatility forecasts when they decide on investment projects or funding choices (bond and equity valuation defining the cost of capital) given that expected future cash-flow variation is a critical factor in business analytics.

9 Conclusions

Our study has examined the HEAVY model and its extension with leverage, power transformations, and macro-characteristics. For the realized measure, our empirical results favor the macro-augmented cross asymmetric power specification, where the lags of both powered variables—squared negative returns, and realized variance—move the dynamics of the power transformed conditional variance of the latter. Similarly, modeling the returns with a double asymmetric power process, we found that not only the powered realized measure asymmetry but the power transformed squared negative returns, as well, help to forecast the conditional variance of the latter. The macro-augmentation of the asymmetric power model ensures the superiority of our contribution, which can be implemented in several investment and risk management practices. We further demonstrated the forecasting dominance of the extended specifications over the benchmark HEAVY and standard volatility models through the out-of-sample forecasting across multiple short- and long-term horizons.

Moreover, we demarcate our study from previous literature by estimating the significant US uncertainty effect on the power of leverage (Heavy and Arch), and the macro-determinants of emerging markets realized variance. The US-led uncertainty spillovers shed light on new evidence for volatility modeling and macro-financial linkages literature. Our findings’ novelty is twofold: Given higher (lower) daily US policy uncertainty levels, mostly associated with economic downturns (upturns), (i) heavy and leverage effects become more (less) acute in realized variance modeling, and (ii) US EPU volatility, financial uncertainty, credit conditions, commodity market benchmarks, and disease news impact on emerging financial volatility increases (decreases). Similarly, financial and health crisis events magnify further the Heavy, Arch, and Macro parameters of the bivariate system and the EPU indirect impact on the volatility drivers, as well.

Our empirical findings on the nexus between low-frequency daily squared returns, high-frequency intra-daily realized measures, and daily macro-proxies provide a volatility forecasting framework with important implications for policymakers and market practitioners, from investors, risk and portfolio managers up to financial chiefs, leaving ample room for future research on further HEAVY model extensions. Therefore, policymakers and market players may use the more general framework to closely track and forecast financial volatility patterns in the process of devising stringent policies, enforcing the financial system’s regulations to preserve financial stability, deciding on asset allocation, hedging strategies, and investment projects. This US-led uncertainty spillover phenomenon, in particular, should be immediately recognized, monitored, and mitigated by regulators amid inconceivable fears stimulated by US politics, such as controversial policy initiatives on trade relations and the recent Covid-19 tragedy, among other critical issues. As part of future research, it would be interesting to extend our study to exchange rate market volatility and several other asset classes using alternative macro-proxies for each type of asset.

High-frequency-based-volatility (see, Shephard and Sheppard 2010 ).

This type of asymmetry was introduced by Glosten et al. ( 1993 ).

The log-transformed series are always positive because all series’ values are higher than one. Since the lower bound of our macro-regressors’ series is not one but zero, we, alternatively, included the regressors divided by 100 (EPU, VIX, MOVE, WTI) and 10,000 (GSCI). This resulted in similar estimated coefficients in terms of level and significance within the HEAVY framework (results available upon request).

Further research could consider an exponential HEAVY specification to address the non-negativity limitations.

The estimation results of the whole Eqs. ( 3 ), ( 4 ), and ( 5 ), when each EPU, crisis, and EPU under crisis effect, is included separately, are omitted due to space considerations. They are available upon request by the authors.

Aggarwal, R., Inclan, C., & Leal, R. (1999). Volatility in emerging stock markets. Journal of Financial and Quantitative Analysis , 34 , 33–55.

Article   Google Scholar  

Alessandri, P., & Mumtaz, H. (2019). Financial regimes and uncertainty shocks. Journal of Monetary Economics , 101 , 31–46.

Aloui, R., Gupta, R., & Miller, S. M. (2016). Uncertainty and crude oil returns. Energy Economics , 55 , 92–100.

Andreasson, P., Bekiros, S., Nguyen, D. K., & Uddin, G. S. (2016). Impact of speculation and economic uncertainty on commodity markets. International Review of Financial Analysis , 43 , 115–127.

Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001). The distribution of exchange rate volatility. Journal of the American Statistical Association , 96 , 42–55.

Antonakakis, N., Chatziantoniou, I., & Filis, G. (2013). Dynamic co-movements of stock market returns, implied volatility and policy uncertainty. Economics Letters , 120 , 87–92.

Antonakakis, N., Chatziantoniou, I., & Filis, G. (2014). Dynamic spillovers of oil price shocks and economic policy uncertainty. Energy Economics , 44 , 433–447.

Antonakakis, N., Chatziantoniou, I., & Filis, G. (2017). Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest. International Review of Financial Analysis , 50 , 1–26.

Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business and Economic Statistics , 27 , 417–427.

Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: a GARCH-MIDAS approach. Journal of Forecasting , 32 , 600–612.

Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics , 131 , 1593–1636.

Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C., & Viratyosin, T. (2020a). The unprecedented stock market impact of COVID-19. National Bureau of Economic Research Working paper No. 26945.

Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020b). Covid-induced economic uncertainty. National Bureau of Economic Research Working paper No. 26983.

Baker, S. R., Bloom, N., & Terry, S. J. (2020c). Using disasters to estimate the impact of uncertainty. National Bureau of Economic Research Working paper No. 27167.

Balli, F., Uddin, G. S., Mudassar, H., & Yoon, S. M. (2017). Cross-country determinants of economic policy uncertainty spillovers. Economics Letters , 156 , 179–183.

Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., & Shephard, N. (2008). Designing realized kernels to measure the ex-post variation of equity prices in the presence of noise. Econometrica , 76 , 1481–1536.

Barsky, R. B., & Kilian, L. (2004). Oil and the macroeconomy since the 1970s. Journal of Economic Perspectives , 18 , 115–134.

Barunik, J., Krehlik, T., & Vacha, L. (2016). Modeling and forecasting exchange rate volatility in time-frequency domain. European Journal of Operational Research , 251 , 329–340.

Bekaert, G., Hoerova, M., & Lo Duca, M. (2013). Risk, uncertainty and monetary policy. Journal of Monetary Economics , 60 , 771–788.

Ben Ameur, H., Jawadi, F., Cheffou, A. I., & Louhichi, W. (2018). Measurement errors in stock markets. Annals of Operations Research , 262 , 287–306.

Bernal, O., Gnabo, J. Y., & Guilmin, G. (2016). Economic policy uncertainty and risk spillovers in the Eurozone. Journal of International Money and Finance , 65 , 24–45.

Bernanke, B. S. (1983). Irreversibility, uncertainty, and cyclical investment. The Quarterly Journal of Economics , 98 , 85–106.

Bloom, N. (2014). Fluctuations in uncertainty. The Journal of Economic Perspectives , 28 , 153–175.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics , 31 , 307–327.

Bordo, M. D., Duca, J. V., & Koch, C. (2016). Economic policy uncertainty and the credit channel: Aggregate and bank level US evidence over several decades. Journal of Financial Stability , 26 , 90–106.

Boumparis, P., Milas, C., & Panagiotidis, T. (2017). Economic policy uncertainty and sovereign credit rating decisions: Panel quantile evidence for the Eurozone. Journal of International Money and Finance , 79 , 39–71.

Brooks, R. D., Faff, R. W., McKenzie, M. D., & Mitchell, H. (2000). A multi-country study of power ARCH models and national stock market returns. Journal of International Money and Finance , 19 , 377–397.

Buckman, S. R., Shapiro, A. H., Sudhof, M., & Wilson, D. J. (2020). News sentiment in the time of COVID-19. FRBSF Economic Letter, 2020-08.

Caggiano, G., Castelnuovo, E., & Figueres, J. M. (2017). Economic policy uncertainty and unemployment in the United States: A nonlinear approach. Economics Letters , 151 , 31–34.

Caggiano, G., Castelnuovo, E., & Kima, R. (2020). The global effects of COVID-19-induced uncertainty. Economics Letters , 194 , 109392.

Caliendo, F. N., Guo, N. L., & Smith, J. M. (2018). Policy uncertainty and bank bailouts. Journal of Financial Markets , 39 , 111–125.

Cano-Berlanga, S., & Giménez-Gómez, J. M. (2018). On Chinese stock markets: How have they evolved over time? Annals of Operations Research , 266 , 499–510.

Carriero, A., Clark, T. E., & Marcellino, M. (2018). Measuring uncertainty and its impact on the economy. The Review of Economics and Statistics , 100 , 799–815.

Cho, D., Han, H., & Lee, N. K. (2018). Carry trades and endogenous regime switches in exchange rate volatility. Journal of International Financial Markets, Institutions and Money , 58 , 255–268.

Christiansen, C., Schmeling, M., & Schrimpf, A. (2012). A comprehensive look at financial volatility prediction by economic variables. Journal of Applied Econometrics , 27 , 956–977.

Clark, T. E., & McCracken, M. W. (2001). Tests for equal forecast accuracy and encompassing for nested models. Journal of Econometrics , 105 , 85–110.

Colombo, V. (2013). Economic policy uncertainty in the US: Does it matter for the Euro area? Economics Letters , 121 , 39–42.

Connolly, R., Dubofsky, D., & Stivers, C. (2018). Macroeconomic uncertainty and the distant forward-rate slope. Journal of Empirical Finance , 48 , 140–161.

Conrad, C., & Karanasos, M. (2010). Negative volatility spillovers in the unrestricted ECCC-GARCH model. Econometric Theory , 26 , 838–862.

Conrad, C., & Loch, K. (2015). Anticipating long-term stock market volatility. Journal of Applied Econometrics , 30 , 1090–1114.

Corradi, V., Distaso, W., & Mele, A. (2013). Macroeconomic determinants of stock volatility and volatility premiums. Journal of Monetary Economics , 60 , 203–220.

Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics , 7 , 174–196.

De Santis, G. (1997). Stock returns and volatility in emerging financial markets. Journal of International Money and Finance , 16 , 561–579.

Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics , 13 , 253–263.

Google Scholar  

Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance , 1 , 83–106.

Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty . Princeton: Princeton University Press.

Book   Google Scholar  

Engle, R. F. (2002). New frontiers for ARCH models. Journal of Applied Econometrics , 17 , 425–446.

Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics , 95 , 776–797.

Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance , 48 , 1749–1778.

Engle, R. F., & Rangel, J. G. (2008). The spline-GARCH model for low-frequency volatility and its global macroeconomic causes. Review of Financial Studies , 21 , 1187–1222.

Estrella, A., & Hardouvelis, G. A. (1991). The term structure as a predictor of real economic activity. The Journal of Finance , 46 , 555–576.

Fang, L., Chen, B., Yu, H., & Qian, Y. (2018). The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH-MIDAS approach. Journal of Futures Markets , 38 , 413–422.

Francq, C., & Thieu, L. Q. (2019). QML inference for volatility models with covariates. Econometric Theory , 35 , 37–72.

Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters , 171 , 63–71.

Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance , 48 , 1779–1801.

Gulen, H., & Ion, M. (2015). Policy uncertainty and corporate investment. The Review of Financial Studies , 29 , 523–564.

Hamilton, J., & Lin, G. (1996). Stock market volatility and the business cycle. Journal of Applied Econometrics , 5 , 573–593.

Han, H. (2015). Asymptotic properties of GARCH-X processes. Journal of Financial Econometrics , 13 , 188–221.

Han, H., & Kristensen, D. (2014). Asymptotic theory for the QMLE in GARCH-X models with stationary and nonstationary covariates. Journal of Business and Economic Statistics , 32 , 416–429.

Hansen, P. R., Huang, Z., & Shek, H. (2012). Realized GARCH: A joint model for returns and realized measures of volatility. Journal of Applied Econometrics , 27 , 877–906.

Harvey, D. I., Leybourne, S. J., & Newbold, P. (1998). Tests for forecast encompassing. Journal of Business and Economic Statistics , 16 , 254–259.

He, C., & Teräsvirta, T. (1999). Statistical properties of the asymmetric power ARCH model. In R. F. Engle & H. White (Eds.), Cointegration, causality, and forecasting. Festschrift in Honour of Clive W.J. Granger (pp. 462–474). Oxford: Oxford University Press.

Heber, G., Lunde, A., Shephard, N., & Sheppard, K., (2009). Oxford-Man Institute’s (OMI’s) realized library, Version 0.3. Oxford-Man Institute: University of Oxford.

Jo, S., & Sekkel, R. (2019). Macroeconomic uncertainty through the lens of professional forecasters. Journal of Business and Economic Statistics , 37 , 436–446.

Jones, P. M., & Olson, E. (2013). The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model. Economics Letters , 118 , 33–37.

Jurado, K., Ludvigson, S. D., & Ng, S. (2015). Measuring uncertainty. American Economic Review , 105 , 1177–1216.

Karanasos, M., & Kim, J. (2006). A re-examination of the asymmetric power ARCH model. Journal of Empirical Finance , 13 , 113–128.

Kelly, B., Pastor, L., & Veronesi, P. (2016). The price of political uncertainty: Theory and evidence from the option market. The Journal of Finance , 71 , 2417–2480.

Klößner, S., & Sekkel, R. (2014). International spillovers of policy uncertainty. Economics Letters , 124 , 508–512.

Knight, F. H. (1921). Risk, uncertainty, and profit . Boston: Hart, Schaffner & Marx; Houghton Mifflin Company.

Kürüm, E., Weber, G. W., & Iyigun, C. (2018). Early warning on stock market bubbles via methods of optimization, clustering and inverse problems. Annals of Operations Research , 260 , 293–320.

Laurent, S. (2004). Analytical derivates of the APARCH model. Computational Economics , 24 , 51–57.

Li, X. M., Zhang, B., & Gao, R. (2015). Economic policy uncertainty shocks and stock-bond correlations: Evidence from the US market. Economics Letters , 132 , 91–96.

Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric Theory , 19 , 280–310.

Meligkotsidou, L., Panopoulou, E., Vrontos, I. D., & Vrontos, S. D. (2019). Quantile forecast combinations in realised volatility prediction. Journal of the Operational Research Society , 70 , 1720–1733.

Mittnik, S., Robinzonov, N., & Spindler, M. (2015). Stock market volatility: Identifying major drivers and the nature of their impact. Journal of Banking and Finance , 58 , 1–14.

Mumtaz, H., & Theodoridis, K. (2018). The changing transmission of uncertainty shocks in the U.S. Journal of Business and Economic Statistics , 36 , 239–252.

Nakatani, T., & Teräsvirta, T. (2009). Testing for volatility interactions in the constant conditional correlation GARCH model. Econometrics Journal , 12 , 147–163.

Pastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The Journal of Finance , 67 , 1219–1264.

Pastor, L., & Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics , 110 , 520–545.

Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics , 160 , 246–256.

Paye, B. S. (2012). ‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables. Journal of Financial Economics , 106 , 527–546.

Pedersen, R. S. (2017). Inference and testing on the boundary in extended constant conditional correlation GARCH models. Journal of Econometrics , 196 , 23–36.

Pedersen, R. S., & Rahbek, A. (2019). Testing GARCH-X type models. Econometric Theory , 35 , 1012–1047.

Schwert, G. W. (1989). Why does stock market volatility change over time? The Journal of Finance , 44 , 1115–1153.

Shephard, N., & Sheppard, K. (2010). Realising the future: Forecasting with high-frequency-based volatility (HEAVY) models. Journal of Applied Econometrics , 25 , 197–231.

Snower, D. J. (2020). The socioeconomics of pandemics policy. Global Economy and Development Brookings Institution, Working paper No. 138.

Tarassow, A. (2019). Forecasting US money growth using economic uncertainty measures and regularisation techniques. International Journal of Forecasting , 35 , 443–457.

Wang, J., Lu, X., He, F., & Ma, F. (2020). Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU? International Review of Financial Analysis , 72 , 101596.

Wisniewski, T. P., & Lambe, B. J. (2015). Does economic policy uncertainty drive CDS spreads? International Review of Financial Analysis , 42 , 447–458.

Xu, J. (1999). Modeling Shanghai stock market volatility. Annals of Operations Research , 87 , 141–152.

Download references

Author information

Authors and affiliations.

Department of Economics and Finance, Brunel University London, Kingston Lane, Uxbridge, UB8 3PH, UK

M. Karanasos & J. Hunter

School of Business and Economics, Loughborough University, Epinal Way, Loughborough, LE11 3TU, UK

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to S. Yfanti .

Ethics declarations

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of interest

The authors declare that they have no conflict of interest.

Availability of data and material

Data available on request from the authors. The data that support the findings of this study are derived from Thomson Reuters Datastream and http://www.policyuncertainty.com/ .

Code availability

OxMetrics 7 is used for the econometric estimations.

Authors’ contributions

Menelaos Karanasos: Methodology; Writing—review & editing. Stavroula Yfanti: Conceptualization; Data curation; Formal analysis; Methodology; Resources; Software; Validation; Visualization; Writing—original draft; Writing—review & editing. John Hunter: Methodology; Writing—review & editing.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 463 KB)

See Appendix Tables  9 and 10 .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Karanasos, M., Yfanti, S. & Hunter, J. Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises. Ann Oper Res 313 , 1077–1116 (2022). https://doi.org/10.1007/s10479-021-04042-y

Download citation

Accepted : 13 March 2021

Published : 21 April 2021

Issue Date : June 2022

DOI : https://doi.org/10.1007/s10479-021-04042-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Economic policy uncertainty
• Emerging markets
• Financial and health crises
• Macro-financial linkages
• Realized variance
• Uncertainty spillovers

JEL classification

• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 12 October 2020

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

• Haritha P H 1 &
• Abdul Rishad 2  

Financial Innovation volume  6 , Article number:  34 ( 2020 ) Cite this article

30k Accesses

39 Citations

Metrics details

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

Introduction

There has been growing academic attention in the past decade on investors’ sentiments and their potential impact on market performance. Investor sentiment is the expectation of market participants about the future cash flows (returns) and investment risk (De Long et al. 1990 ). Because traditional stock market theories comprehended market dynamics under the theoretical framework of the efficient market hypothesis (EMH) and random walk theory, they did not consider investor sentiment as an important aspect. However, they failed to explain the heterogeneous behaviour of investors in the capital market. Investors’ sentiment is a vital aspect of the capital market, as it contributes to frequent fluctuations in the stock price and thus creates uncertainty about future returns on investments. In the past few decades, there have been radical changes in the Indian financial environment, especially in the basic structure—for example, shifting from a savings-oriented economy to an investment-oriented economy. These changes have increased heterogeneity in the composition of participants and impacted investors’ risk-taking behaviour.

As per the EMH in classical financial theory, market participants exhibit rational risk aversion. Moreover, the information efficiency of the market does not allow participants to outperform the market (Fama 1965 ). The classical theory fails to explain the presence of systematic mispricing in the capital markets resulting from sentimental factors. Behavioural financial theories claim that irrational behaviour of noise traders and arbitrators causes a disparity in asset prices from their intrinsic (fundamental) values. Recent theoretical advances in behavioural finance and empirical evidence both have rejected the hypotheses of classical financial theory because of its assumption of rationality of agents in capital markets. In the previous decade, rational participants did not seem to have played a leading role in bringing the value of assets up to the then current value of anticipated cash flows (Baker and Wurgler 2007 ). Behavioural finance offers an alternative model that claims that economic phenomena can be better understood if the investors are accepted to not be entirely rational. In this context, the asset pricing not only includes the risk-related anticipated rates but also the impact of investor expectations on the returns. Behavioural finance explains the relationship between investment and the investor’s psychology. Investor behaviour is reflected in the stock prices, and market fluctuations, which ultimately shape the market, are themselves shaped by the psychology of the investors. Baker and Wurgler ( 2006 ) argued that market sentiment creates a tendency for investors to be optimistic or pessimistic while speculating prices instead of deciding on fundamental factors.

Previous studies sought to detect the predictability of sentiments as a systematic risk factor valued as per certain conditions in the market. Studies from developed economies like the USA are far ahead in understanding the sentiment-related market dynamics (Barberis et al. 1998 ; Lee et al. 2002 ; Neal and Wheatley 1998 ). Academic study of investor sentiment in developing economies with rapidly growing capital markets is still in infancy. Previous research has mainly focused on the influence of investors’ sentiment on investment returns, whereas the effect of sentiment on the conditional volatility structure of the market is less explored. Also, even among those studies that consider sentiment as a critical factor influencing the time-varying stock return, volatility and potential profitability relating to noise traders were the main aspects of focus. During the periods of high sentiment and low sentiment, noise traders act differently to keep their positions secure. During the high sentiment episodes, their participation and trading is more aggressive compared to that during a low sentiment episode. This is caused by naive and unaware noise traders’ misjudgement of potential risk. Past academic studies about emerging economies have not explored such factors in-depth. The present study is an attempt to address the above-mentioned issues by using a market-oriented sentiment index. We developed an investors’ sentiment index by using multiple sentiment yardsticks mentioned by Baker and Wurgler ( 2006 ). Considering the investors’ sentiments’ contribution to volatility in emerging markets, the current study aimed to establish new empirical evidence to add a more comparative dimension to the existing literature. The findings can help market participants to understand the role of investor sentiment in the determination of conditional volatility of the market and to take decisions to optimise the portfolio.

This study developed the aggregate sentiment index (ASI) from market-oriented sentimental factors such as trading volume, put-call ratio, advance-decline ratio, market turnover, share turnover and number of initial public offers (IPOs) in the period. The use of a constructed sentiment index under the GARCH framework to estimate the association between stock market volatility and investor sentiment makes this study different from existing studies. The findings indicate the persistence of volatility in market indices. Such persistent connection between the sentiment index and stock volatility suggests that investor sentiment is one of the most crucial determinants of Indian stock market volatility.

Theoretical background

According to the conventional theory of ‘market noise’ proposed by the Black ( 1986 ), noise traders operate on noisy signals in financial markets and balance both the systematic and non-systematic risk of an asset. According to this theory, noise makes markets inefficient to some extent and prevents investors from benefitting from inefficiencies. The significance of sentimental factors in asset pricing theories is substantiated by empirical literature from developed economies. The question of how irrational beliefs held by investors affect the market through asset pricing and expected returns is explained in behavioural finance theories. The theoretical model developed by De Long et al. ( 1990 ) explained this phenomenon as ‘Some investors, denominated noise traders, were subject to sentiment – a belief about future cash flows and risks of securities not supported by economic fundamentals of the underlying asset(s) – while other investors were rational arbitrageurs, free of sentiment. The irrational beliefs were caused by noise, interpreted by the irrational traders as information, thus the term noise traders.’

Theoretically, noise is part of irrational behaviour; the irrational traders consider noise as information. Interestingly, proponents of an efficient market claimed that noise traders were exploited by rational arbitrageurs who drove prices towards fundamental equilibrium values. Thus, noise was a reaction of noise traders to the activities of rational arbitrageurs that caused overpricing or underpricing of stocks during periods of high and low sentiment (Lemmon and Portniaguina 2006 ; Baker and Wurgler 2006 ). Researchers have been unable to satisfactorily explain the interaction between rational and irrational investors. The continuing debate on this issue significantly contributes to the literature but concentrates mainly on the role of noise traders in anticipated asset yields and volatility of return. It is not understood how the market reacts to noise, which is caused by a large number of small events. This behaviour can be observed among investors from advanced economies because they believe that systematic risk and return anomaly is associated with irrational investment behaviour (Brown and Cliff 2004 ; Qiu and Welch 2006 ; Lemmon and Portniaguina 2006 ). With this theoretical background, our study examines the role of irrational feelings of investors and their impact on the volatility of the Indian capital market.

Literature review

The development of behavioural finance theories triggered a discussion on the impact of investor sentiment on asset returns in the integrated stock market. According to theoretical and empirical research, investor sentiment strongly influences stock prices with inevitable consequences on portfolio selection and asset management, as psychological differences of heterogeneous investors have implications on the pricing of assets in the market. The influence of investor’s sentiment in asset price volatility is widely described as a combination of investors’ reaction to the current market situation and unjustified expectation of the future cash flows (Baker and Wurgler 2006 , 2007 ).

As a psychological factor, it is not easy to estimate investors’ sentiment because of their subjective and qualitative nature. However, different proxies have been used to measure sentiment. These indicators of the sentiment index are classified as indirect and direct measures. In direct measures, researchers measure the individual investor sentiment via surveys and polling techniques. They are highly sampling-dependent, and the chances of sampling errors are high. Moreover, they may not be able to give a broad picture of the prevailing sentiment. Indirect measures use market-determined sentiment proxies, such as trading volume, turnover volatility ratio, put-call ratio, advance-decline ratio, market turnover and share turnover for measuring the same. They posit that investors’ sentiment are reflected in the structure and breadth of the market and understanding these dynamics helps to capture the irrational aspects of the market. The consistent and theoretically comprehensible nature of the sentiment index has led to its wide adoption (Baker and Wurgler 2006 ; Brown and Cliff 2004 ; Chen et al. 1993 ; Clarke and Statman 1998 ; DeBondt and Thaler 1985 ; Elton et al. 1998 ; Fisher and Statman 2000 ; Lee et al. 2002 ; Neal and Wheatley 1998 ; Sias et al. 2001 ). According to Zhou ( 2018 ), investor sentiment indicates the distance of the asset’s value from its economic bases. This can be measured from different sources, such as official documents, media reports and market surveys. Mushinada and Veluri ( 2018 ) used trading volume and return volatility for understanding the relationship between sentiments and returns. Their findings showed that post-investment analysis was essential to correct errors in previous behavioural estimations. Market participants’ behaviour is heterogeneous because of the risk-return expectation, and it creates noise in the market. These findings contradict with the premises of the efficient market hypothesis that postulate that markets turn information efficient when investors behave rationally.

In the past few decades, empirical studies across the globe have investigated the connection between investors’ sentiment and stock returns for understanding and substantiating theories of market inefficiency (Brown and Cliff 2004 ; Fisher and Statman 2000 ). Chi et al. ( 2012 ) examined the impact of investor sentiment on stock returns and volatility by using mutual fund flows as an investor sentiment proxy in the Chinese stock market. They found that investor sentiment has a great impact on stock returns. The relationship between stock market volatility and investor sentiment has also been reported as statistically significant. Supporting these findings, Zhou and Yang ( 2019 ) stated that the construction of a theoretical model of stochastic investor sentiment influences investor crowdedness and also affects asset prices. Their result indicated that optimistic (pessimistic) expectations of investors can move asset prices above (below) the basic value. By examining the long-term association between investor sentiment in the stock and bond market, Fang et al. ( 2018 ) showed that the index of investor sentiment is positively associated with market volatility. Contradicting the fundamental tenets of the efficient market hypothesis, Shiller ( 1981 ) argued that investors are not completely rational, which could affect market prices aside from fundamental variables. Wang et al. ( 2006 ) noted that the sensitivity of investor sentiments to the information flow affected both market return and volatility. Chiu et al. ( 2018 ) found a positive relationship between investor sentiment, market volatility and macroeconomic variables. Jiang et al. ( 2019 ) constructed the fund manager’s sentiment index as a predictor of aggregate stock market returns. They found that when managers had a high level of sentiment, it caused a reduction in overall income surprises from total investment. Li ( 2014 ) pointed out that the sentiment index has strong predictive power for Chinese stock market returns. Retail investors’ attention will help to mitigate the crash risk, as the retail investors’ attention will not allow any irrational or noise traders to overrun the rational market participants (Wen et al. 2019 ).

Verma and Verma ( 2007 ) studied the role of retail and noise traders in price volatility to yield similar results. Verma and Soydemir’s ( 2009 ) empirical examination of the rational and irrational investors’ impact on market prices also supported the previous finding. They discovered that individual and institutional investors’ feelings influenced the market. Further evidence shows that the response of the market to volatility is not homogeneous; it is heterogeneous depending on the variations in shareholder sentiment. These findings are validated by Gupta ( 2019 ) who found that sentiments of fund managers are a stronger predictor than the returns, when it comes to forecasting volatility. Yang and Copeland ( 2014 ) found that the investor sentiment index has a long-term and short-term asymmetrical impact on volatility. They concluded that bearish sentiment is associated with lower returns than bullish sentiment, which accelerates market return. This shows that the bullish feeling has positive effects on short-term volatility, whereas in the long-term, it has a negative effect on volatility. These findings agree with the findings discussed by Qiang and Shue-e ( 2009 ), namely that positive and negative sentiment create different impacts on stock price variation. Baker et al. ( 2012 ) constructed investor sentiment indicators of six nations but because of the disintegration of different markets owing to the heterogeneous behaviour of investors across the globe, the indicators were not viable. Other studies have reported that investors’ sentiments are driven by overall funding patterns irrespective of the investor being individual or institutional (Baker and Wurgler 2000 ; Henderson et al. 2006 ). Investors’ sentiment has a mutual relationship with the expected return of public bonds and the expected return from the stock market (Bekaert et al. 2010 ).

In the context of the Indian stock market, Sehgal et al. ( 2009 ) discussed the fundamental aspects of investor sentiment and its relationship with market performance. They identified several factors that might act (individually and together) as indicators of market behaviour and investor sentiments’ influence on market behaviour. The authors used macroeconomic factors such as real GDP, corporate profits, inflation, interest rates and liquidity in the economy and market-based factors such as the put-call ratio, advance-decline ratio, earnings surprises, the price to earnings ratio and price to book value as potential factors to explain the underlying investor sentiment at the aggregate market level. They also suggested the development of a sentiment index based on these macroeconomic and market indicators. Using some of these indicators, Dash and Mahakud ( 2013 ) examined the explanatory power of an index of investor sentiment on aggregate returns. They found a significant relationship between the investor sentiment index and stock returns across industries in the Indian stock market. Rahman and Shamsuddin ( 2019 ) studied the excess price ratio and its influence on investor sentiment and found that the price to earnings ratio increased with a rise in investors’ sentiment. Kumari and Mahakud ( 2016 ) and Chandra and Thenmozhi ( 2013 ) studied the impact of investor sentiment in the Indian capital market. They found a positive relationship between investor sentiment and market volatility. Verma and Verma ( 2007 ) showed that investor sentiment has a positive impact on asset return, but it makes an adverse impact on individual and institutional investors owing to market volatility. Aggarwal and Mohanty ( 2018 ) studied the impact of the investor sentiment index on the Indian stock market and found that there is a positive relationship between stock returns and investor sentiments. However, most of these studies focused on the general effect of investors’ sentiment on stock returns. Such an approach restricts our understanding of the phenomenon of investors’ sentiment and its influence on market dynamics to a single dimension. In the present study, we explored the role of investor sentiment in determining excess market returns and volatility.

Data and variables

Being a qualitative factor, it is not easy to quantify the market behaviour of investors. Past studies have used multiple ways to measure investors’ sentiment. Some studies have relied on media reports, events and other publicly available documents to collect information on investor behaviour, and other studies have conducted surveys among investors for the same. Some other researchers have used market-based indicators such as price movements and trading activities for constructing sentiment indexes. A few researchers have used single variables as an indicator of investor sentiment. For instance, Mushinada and Veluri ( 2018 ) used trading volume as an indicator of investor sentiment. Using a single variable may not be sufficient to explain market sentiments because there are multiple factors that cause variation in these single variable proxies. Latest studies have constructed the sentiment index by using multiple market-based indicators that directly reflect the participants’ behaviour. Following Baker et al. ( 2012 ), this study employed multiple market-based indicators for constructing the sentiment index for the period from January 2000 to December 2016. We used the monthly average closing price of the NIFTY 50 (Nifty) stock index to measure market volatility and return. The diversified market representation of the Nifty index over the other benchmark BSE SENSEX (Sensex) motivated us to select the former. The study used monthly data because of the scarcity of high-frequency data on market-related indicators. The data was collected from the official websites and various reports of the National Stock Exchange, Reserve Bank of India and Securities and Exchange Board of India.

The study employed Bollerslev ( 1986 ) generalized autoregressive conditional heteroskedastic model (GARCH) to measure volatility using the conditional variance equation and to capture the dynamics of volatility clustering. This helped us to examine how the investors’ sentiment reacts to market volatility. This model helps verify whether the investors’ shocks are persistent or not. For the serial correlation, this study used the autoregressive conditional heteroskedasticity-Lagrange multiplier (ARCH-LM) of Engle and Ng ( 1993 ), autoregressive conditional heteroskedasticity (ARCH) test of Engle ( 1982 ) and Mcleod and Ll ( 1983 ) tests for the estimation of models. We also employed the Granger causality test to check the direction of causality between sentiment and market volatility.

Construction of investors’ sentiment index

The present study adopted the framework developed by Baker et al. ( 2012 ) to construct the investors’ sentiment index. It considered six variables: trading volume, put-call ratio, advance-decline ratio, market turnover, share turnover and the number of IPOs. The number of IPOs was defined as the total number of IPOs during the period. Baker et al. ( 2012 ) argued that firms try to procure more capital when the market value of the firm is high and repurchase their shares when the market value is low. The intention is to take advantage of the market sentiment until it reaches the fundamental value. In a bullish market, new issue of shares will transfer wealth from new shareholders to the company or to the existing shareholders. This market timing hypothesis suggests that higher (lower) value or number of IPOs means that the market sentiment is bullish (bearish) (Baker and Wurgler 2006 ). The number of IPOs reflects the market pulse; hence, they can be considered as an important component of the sentiment index.

The share turnover ratio is one of the conventional yardsticks for measuring the liquidity position, which reflects the active participation of traders and investors in the market. It is the ratio of the total value of shares traded during the period to the average market capitalization for the period. Turnover is vital in gauging investors’ sentiment in the market. Irrational investors actively participate in the market when they are optimistic and accelerate the volume of turnover (Baker and Stein 2004 ). Theoretically, the relationship between market returns and turnover is expected to be negative (Jones 2001 ). The presence of high turnover ensures liquidity and reduces the chances of abnormal returns.

Market turnover (MT) is the ratio of trading volume to the number of shares listed on the stock exchange. Market sentiment can be sensed from the turnover of the market because turnover will be low in bearish markets and high in bullish markets (Karpoff 1987 ). Small turnover is usually preceded by a price decline, whereas high turnover is associated with an increase in price (Ying 1966 ). Thus, the turnover information is a significant component of measuring the sentiment of market participants.

The advances and declines ratio (ADR) is a market-breadth indicator that analyses the proportion between the number of advancing shares and declining shares. The increasing (decreasing) trends in the ADR confirm the upward (downward) trend in the market (Brown and Cliff 2004 ). Generally, the ADR ratio is expected to be positive because investors’ sentiment makes the market active. Thus, the ADR ratio helps to recognise the recent trend and can be used as an indicator of market performance.

The put-call ratio (PCR) is another indicator to measure the dynamics of the secondary market. This sentiment indicator is measured as the ratio between transactions on all the put options and the call options on Nifty. A higher (lower) ratio indicates a bullish (bearish) sentiment in the market. Incorporating PCR to measure the aggregate sentiment index yields accurate results because it reflects the expectations of market participants. When market participants expect a bearish trend, they try to shield their positions. When trade volumes of put options are higher relative to the trade quantity of call options, the ratio will go up (Brown and Cliff 2004 ; Finter and Ruenzi 2012 ; Wang et al. 2006 ). This derivative market proxy is considered as an indicator of a bullish trend because the bearish market PCR will be small (Brown and Cliff 2004 ).

The trading volume (TV) is a key variable for constructing the sentiment index. It is measured as the monthly average of the Nifty daily trade volume. Frequent trades in an active market increase the volume and create liquidity in the market. Therefore, researchers have used market turnover as a proxy for investor sentiment (Qiang and Shue-e 2009 ; Zhu 2012 ; Li 2014 ; Chuang et al. 2010 ). The present study considered TV as one of the indicators of market sentiment.

Macroeconomic factors that are often flashed in the media tend to influence investor sentiment quite significantly. Factors like the levels of inflation, corporate debt, economic growth rate and foreign exchange rate and reserves tend to affect the behaviour of market participants to a certain extent. Therefore, this study used variables such as the exchange rate, Wholesale Price Index (WPI), Index of Industrial Production (IIP), Net Foreign Institutional Investment (FII) and Term Spread (TS) to measure the intensity of aggregate investor sentiment on market volatility.

Unit root tests

Ensuring the stationarity of the variables is necessary for consistent estimators. This study used the augmented Dickey-Fuller test (ADF) (Dickey and Fuller 1981 ) to analyse the presence of unit roots in the time series properties of each variable. Table  1 shows the results of unit root analysis using the ADF test. Unit root tests were run with the linear trend and at levels and intercept. The result shows that all variables expect MT are stationary at level. MT was converted to stationarity by taking the first difference.

• Principal component analysis

Principal component analysis (PCA) is a multivariate method in which several interconnected quantitative dependent variables describing the observations are analysed. PCA aims to find and extract the most significant information from the data by compressing the size and simplifying the data without losing the important information (Abdi and Williams 2010 ). It consists of several steps for conducting the linear transformations of a large number of correlated variables to obtain a comparatively few unrelated elements. In this way, information is clustered together into narrow sets and multicollinearity is eliminated. The principal goal of PCA is to summarize the indicator data through a common set of variables as efficiently as possible.

First, the six orthogonal sentiment proxies and their first lags were used as factor loadings to calculate the raw sentiment index. The study started with estimating the initial principal component of the six indicators and their lags, which gave a first-stage index with 12 loading factors, namely the six proxies and their lags. Then, we calculated the correlation between the initial index and the current and lagged values of the indicators. Finally, we estimated the sentiment as the first principal component of the correlation matrix of six variables, which were the respective proxy’s lead or lag. We chose whichever had a higher correlation with the first-stage index to rescale the coefficients so that the index had unit variance (Table 2 ). This process yielded a parsimonious index.

Investors’ sentiment and stock market volatility

Following the theoretical and empirical models proposed by Baker and Wurgler ( 2007 ), Brown and Cliff ( 2004 ) and Baker et al. ( 2012 ), this study used market-related indicators for the construction of the investor sentiment index in the initial stage. This study used six indirect proxies to create the sentiment index by considering the first principal component and the lagged components of the variable. The first principal component explains the sample’s variance. Researchers have argued that certain proxies take longer periods to reflect the investors’ sentiment. Therefore, the present study followed the approach of Baker and Wurgler ( 2006 ) and Ding et al. ( 2017 ) to reflect the investors’ sentiment accurately and to assess the PCA with levels as well as their lags to find the main factors.

The GARCH (1,1) model was used to estimate the impact of sentiment on market volatility and stock returns. The GARCH model helps analyse the volatility characteristics of the datasets, especially for financial data, as it has the unique characteristics of heteroscedasticity and volatility clustering (Fig.  1 ). The specific character of financial time series data limits the use of conventional econometrics models to estimate the parameters. The GARCH model helps to capture volatility clustering and to manage issues of heteroskedasticity.

The rational aggregate sentiment index

Stock market volatility can be estimated in two ways: with the help of market-determined option prices or by time series modelling. Non-availability of option prices led us to choose the time-series method. There are multiple indices available to measure the dynamics of the Indian capital market. Among them, Nifty, which consists of 50 companies from different sectors, and Sensex, which covers 30 companies, are prominent. Inclusion of diversified sectors and wider market coverage (market capitalisation) motivated us to select Nifty as the indicator for measuring market volatility. The indicator of market returns and the aggregate sentiment index showed volatility clustering (Fig.  2 ), and the heteroskedastic behaviour was confirmed through the ARCH-LM test. This satisfied the prerequisites for estimating the GARCH model.

Investors’ sentiment and stock index return

The GARCH ( p, q ) model, introduced by Engle ( 1982 ) and Bollerslev ( 1986 ), can be expressed as follows:

where r t is the log Nifty return (the positive value of r t indicates a bullish trend in the market, and the negative value shows a bearish trend in the market). It is calculated by.

\( {r}_t=\frac{P_1-{P}_0}{P_0} \) , where P 0 and P 1 represent the price at time t-1 and t. γ is the coefficient of the lagged value of the Nifty return ( r t  − 1 ).  c 0 is the constant of the mean equation; ω is the constant in the variance equations; and  ε t is the error term.  I t  − 1 represents the information available to the market participants. \( {\varepsilon}_{t-1}^2 \) is the ARCH term and \( {\sigma}_{t-1}^2 \) is the GARCH term that explains the instantaneous variance at time t − 1. α +β > 1β ≥ 0, which shows the persistence of volatility. A value close to 1 indicates the persistence of volatility and indicates a low level of mean reversion in the system. By increasing the number of the ARCH and GARCH terms, the model can be generalized to a GARCH (p,q) model. For a well-specified GARCH model, ω > 0, α > 0 and  β  ≥ 0 should be satisfied.

We modified the basic GARCH model by incorporating a sentiment variable in the equation,

where δ represents the coefficient of the sentiment index.

Empirical results

The estimated result of the GARCH (1,1) model is presented in Table  3 . The coefficients of the ARCH (α) and GARCH terms (β) are statistically significant and different from zero. In addition, the sum of α + β is close to unity. This indicates the high persistence of volatility, that is, the mean reversal process is very slow because of the persistent shocks. The result of the ARCH-LM test indicates the absence of further ARCH effects, which means the model captures the ARCH effects. The statistically significant coefficient of Q and Q 2 at the 20th lag indicates the absence of further autocorrelation in the model.

Sentiment is a crucial element that directly influences market behaviour. The conventional capital asset pricing model theory states that investors should be rewarded according to their risk-taking behaviour. However, the impact of sentiment on market volatility may cause market uncertainty and lead to less returns. If the market participants fail to earn a market risk premium for their expected volatility, they will move away from the market, which further causes volatility in the market. This vicious circle may cause a bearish trend and languid growth and development of the market. The conditional volatility graph shows that the impact of negative sentiment is higher than that of positive sentiment. This indicates that when sentiments are positive, investors actively participate in the market with the expectation of higher returns. However, this causes more speculative activities in the markets and may cause overvaluation of scrips. In contrast, during the dominance of negative sentiments, investors move away from the market because of the negative expectation of market returns. Therefore, it can be theorised that during positive sentiment, companies explore the opportunity to enter the market through IPOs. Similarly, dividend declaration, bonus issue and a rights issue also trigger positive sentiments.

Conditional volatility

The conditional variance graph from the GARCH (1,1) model shows the dynamics of market volatility of the Nifty returns (Fig.  3 ). Up to May 2008, volatility was high, though it can be deemed as moderate when compared to that during the subprime crisis period. During this period, volatility increased exponentially, and this trend continued up to February 2010. Later, the volatility reduced substantially.

• Granger causality test

The Granger causality test examines the direction of cause among different series (Granger 1969 ). A time series x t Granger-causes another time series y t if series y t can be predicted with better accuracy by using the past values of x t rather than by not doing so. This study examined the causal relationship between the sentiment index (S ent ) and stock market return. Tests between the aggregate sentiment index and stock returns were modelled for understanding the leading and the lagging variables. We found that investors’ sentiment leads to volatility of the market returns. However, volatility in the returns does not cause sentiment (Table  4 ).

De Long et al. ( 1990 ) pointed out that noise traders’ pressure in a market with a strong bullish sentiment on the price to move beyond the fundamental value causes a drop in the expected return. However, if bullish noise traders dominate the market, it causes a rapid upward movement in the market prices because of the upsurge in demand for the high-risk scrips. The expected level of market risk will be higher, creating a ‘hold more’ effect because of the expectation of higher returns. The intensity of sentiments on stock returns closely depends on the effect that dominates the market expectation. The unidirectional causality of sentiments to volatility indicates that the price-pressure effect (noise traders’ pressure on prices reduces the expected return) dominates the market and that noise traders benefit during episodes of a high sentiment index. This way, sentiment leads to volatility. However, once the noise traders start making profit, their expectation on return and risk will increase. Thus, it may not create a reverse causality in a developing market because of information inefficiency. In another way, it can be explained that when investors’ irrational sentiment is positive, their expectation on return is also positive. This may lead to speculative activities on their part to exploit the situation, exciting them to invest more. This leads to volatility in the market. On the other hand, market uncertainty causes withdrawal of market makers and encourages investors to stay inactive because of the uncertain expectation on the return in a risky market. Moreover, in such a situation, investors are always concerned about fundamentally induced equilibrium prices that give the fair value of assets. Following the arguments of Wen et al. ( 2019 ), retail investors should be more attentive in collecting information to minimise their information asymmetry for managing their potential risk.

This research provides a comprehensive examination of the impact of investor sentiment on stock market volatility. The study constructed a sentiment index by using a linear combination of different-market oriented proxies weighted using principal component analysis. The study found an asymmetrical relationship when the sentiment index was decomposed into positive and negative sentiment. The positive sentiment index has a positive effect on excess market return, but the intensity of negative sentiment is less on negative returns. These results imply that when investors are more optimistic about the market generating excess returns, their extreme optimism leads to more speculative activities that tempt them to invest even more. The study also found persistency of market volatility and the sentiment index, which shows the contemporaneous impact on sentiment and excess market returns. The findings reveal that investors consider the market as weak-efficient. This shows that the efficient market hypothesis may not be sufficient in explaining the market behaviour of emerging markets like India. The results indicate the scope for arbitration in the Indian market and thus invalidate the explanation of efficient market volatility in India. This further indicates a deviation from a random walk, but it is difficult to predict the volatility of the market sufficiently to produce excess returns.

The results help to understand the role of non-fundamental factors in driving the Indian equity market away from a fundamentally oriented equilibrium and in influencing the risk-return perception. They also show that sentiment is relatively correlated with unexpected stock returns, and the correlation differs significantly over time. This contradicts with the traditional capital market theories and supports the behavioural theories on capital markets. Proper examination of the market sentiment helps investors and fund managers decide their entry and exit points for investment. By taking the investor sentiment into account as a significant determinant of stock market volatility in asset price models, investors can enhance their portfolio performance. The results can also help policymakers’ efforts to stabilize stock market volatility and uncertainty in order to protect investors’ wealth and attract more investors. Therefore, future research should aim to develop investors’ sentiments from available high-frequency data by incorporating additional comprehensive investor sentiment factors to reflect real-time information.

Availability of data and materials

Not applicable.

Abbreviations

Indian Aggregate Sentiment Index

Gross domestic product

Price to earnings ratio

Generalized autoregressive conditional heteroskedastic model

Autoregressive conditional heteroscedasticity-Lagrange multiplier

Put-call ratio

Initial public offer

Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

Article   Google Scholar  

Aggarwal D, Mohanty P (2018) Do Indian stock market sentiments impact contemporaneous returns? S Asian J Bus Stud 7(3):332–346

Baker M, Stein JC (2004) Market liquidity as a sentiment indicator. J Financ Mark 7(3):271–299

Baker M, Wurgler J (2000) The equity shares in new issues and aggregate stock returns. J Financ 55:2219–2257

Baker M, Wurgler J (2006) Investor sentiment and cross-section of stock returns. J Financ 61(4):1645–1980

Baker M, Wurgler J (2007) Investor sentiment in the stock market. J Econ Perspect 21:129–152

Baker M, Wurgler J, Yuan Y (2012) Global, local, and contagious investor sentiment. J Financ Econ 104(2):272–287

Barberis N, Shleifer A, Vishny R (1998) A model of investor sentiment. J Financ Econ 49(3):307–343

Bekaert G, Baele L, Inghelbrecht K (2010) The determinants of stock and bond return comovements. Rev Financ Stud 23:2374–2428

Black F (1986) Noise. J Financ 41(2):529–543

Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31:307–327

Brown GW, Cliff MT (2004) Investor sentiment and the near-term stock market. J Empir Financ 11(1):1–27

Chandra A, Thenmozhi M (2013) Investor sentiment, volatility and stock return comovements. Volatility and Stock Return Comovements

Google Scholar  

Chen N, Kan R, Miller M (1993) Are the discounts on closed-end funds a sentiment? index? J Financ 48:795–800

Chi L, Zhuang X, Song D (2012) Investor sentiment in the Chinese stock market: an empirical analysis. Appl Econ Lett 19(4):345–348

Chiu CWJ, Harris RD, Stoja E, Chin M (2018) Financial market volatility, macroeconomic fundamentals and investor sentiment. J Bank Financ 92:130–145

Chuang W-J, Ouyang L-Y, Lo W-C (2010) The impact of investor sentiment on excess returns: a Taiwan market cases. Int J Inf Manag Sci 21:13–28

Clarke RG, Statman M (1998) Bullish or bearish? Financ Anal J 54:63–72

Dash SR, Mahakud J (2013) Investor sentiment and stock returns: do industries matter? Margin J Appl Econ Res 7:315–349

De Long JB, Shleifer A, Summers LH, Waldmann RJ (1990) Noise trader risk in financial markets. J Polit Econ 98(4):703–738

DeBondt WF, Thaler R (1985) Does the stock market overreact? J Finance XL(3):793–805

Dickey DA, Fuller WA (1981) Likelihood ratio statistics for autoregressive time-series with a unit-root. Econometrica 49:1057–1072

Ding Z, Liu Z, Zhang Y, Long R (2017) The contagion effect of international crude oil price fluctuations on Chinese stock market investor sentiment. Appl Energy 187:27–36

Elton EJ, Gruber MJ, Busse JA (1998) Do investors care about sentiment? J Bus 71:477–500

Engle RF (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007

Engle RF, Ng VK (1993) Measuring and testing the impact of news on volatility. J Financ 48:1749–1778

Fama EF (1965) The Behavior of Stock-Market Prices. J Bus 38(1):34–105

Fang L, Yu H, Huang Y (2018) The role of investor sentiment in the long-term correlation between US stock and bond markets. Int Rev Econ Financ 58:127–139

Finter P, Ruenzi S (2012) The impact of investor sentiment on the German stock market. Z Betriebswirt 82(2):133–163

Fisher KL, Statman M (2000) Investor sentiment and stock returns. Financ Anal J 56:16–23

Granger CWJ (1969) Inves~ga~g causal relations by econometric models and cross-spectral models. Econometrica 37:424–438

Gupta R (2019) Manager sentiment and stock market volatility. Journal of Manag Inf Decis Sci 22(1):11–21

Henderson BJ, Jegdeesh N, Weisbach MS (2006) World markets for raising new capital. J Financ Econ 82:63–101

Jiang F, Lee J, Martin X, Zhou G (2019) Manager sentiment and stock returns. J Financ Econ 132(1):126–149

Jones C (2001) A century of stock market liquidity and trading costs. Columbia University Mimeo, New York

Karpoff JM (1987) The relation between price changes and trading volume: a survey. J Financ Quant Anal 22(1):109–126

Kumari J, Mahakud J (2016) Investor sentiment and stock market volatility: evidence from India. J Asia Pac Bus 17(2):173–202

Lee WY, Jiang CX, Indro DC (2002) Stock market volatility, excess returns, and the role of investor sentiments. J Bank Financ 26:2277–2299

Lemmon M, Portniaguina E (2006) Consumer confidence and asset prices: some empirical evidence. Rev Financ Stud 19(4):1499–1529

Li BH (2014) Does investor sentiment predict stock returns? The evidence from the Chinese stock market. J Syst Sci Complex 27:130–143

Mcleod AI, Ll WK (1983) Diagnostic checking ARMA time series models using squared residual autocorrelations. J Time Ser Anal 4:269–273

Mushinada VNC, Veluri VSS (2018) Investors overconfidence behaviour at the Bombay Stock Exchange. Int J Manag Financ 14(5):613–632

Neal R, Wheatley SM (1998) Do measures of investor sentiment predict returns? J Financ Quant Anal 33(04):523–547

Qiang Z, Shue-e Y (2009) Noise trading, investor sentiment volatility, and stock returns. Syst Eng Theory Pract 29(3):40–47

Qiu L, Welch I (2006) Investor sentiment measures. In: Schmeling M (ed) Investor sentiment, herd-like behavior and stock returns http://ssrn.com/abstract=589641

Rahman ML, Shamsuddin A (2019) Investor sentiment and the price-earnings ratio in the G7 stock markets. Pac Basin Financ J 55:46–62

Sehgal S, Sood GS, Rajput N (2009) Investor sentiment in India: a survey. Vision: J Bus Perspect 13(2):13–23

Shiller RJ (1981) Do stock prices move too much to be justified by subsequent changes in dividends? Am Econ Rev 71(3):421–436

Sias RW, Starks LT, Tinic SM (2001) Is noise trader risk priced? J Financ Res 24:311–329

Verma R, Soydemir G (2009) The impact of individual and institutional investor sentiment on the market price of risk. Q Rev Econ Finance 49(3):1129–1145

Verma R, Verma P (2007) Noise trading and stock market volatility. J Multinatl Financ Manag 17:231–243

Wang YH, Keswani A, Taylor SJ (2006) The relationships between sentiment, returns and volatility. Int J Forecasting 22:109–123

Wen F, Xu L, Ouyang G, Kou G (2019) Retail investor attention and stock price crash risk: Evidence from China. Int Rev Financ Anal 65:101376

Yang Y, Copeland L (2014) The effects of sentiment on market return and volatility and the cross-sectional risk premium of sentiment-affected volatility, Cardiff economics working papers, no. E2014/12. Cardiff Business School, Cardiff

Ying CC (1966) Stock market prices and volumes of sales. Econometrica 34:676–685

Zhou G (2018) Measuring investor sentiment. Ann Rev Financ Econ 10:239–259

Zhou L, Yang C (2019) Stochastic investor sentiment, crowdedness and deviation of asset prices from fundamentals. Econ Model 79:130–140

Zhu X (2012) Investor sentiment and volatility of stock index an empirical analysis from the perspective of behavioural finance. Adv Appl Econ Finance 3(4):627–629

Download references

Acknowledgements

Author information, authors and affiliations.

Central University of Kerala, Kasaragod, India

Haritha P H

Central University of Himachal Pradesh, Dharamsala, Himachal Pradesh, India

Abdul Rishad

You can also search for this author in PubMed   Google Scholar

Contributions

The authors analyze the role of irrational investor sentiment in determining Indian stock market volatility using monthly information from India’s National Stock Exchange between June 2000 and December 2016. Sentiment index with the assistance of principal component analysis was developed using market-related implicit indices. Further, this sentiment index was modelled in the GARCH and Granger Causality framework to analyses its contribution to volatility. The results show that irrational sentiment significantly causes excess market volatility. Moreover, the study reveals that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns. The findings reveal that investors consider the market as weak-efficient. This shows that the efficient market hypothesis may not be sufficient in explaining the market behaviour of emerging markets like India. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Haritha P H .

Ethics declarations

Competing interests, additional information, publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

P H, H., Rishad, A. An empirical examination of investor sentiment and stock market volatility: evidence from India. Financ Innov 6 , 34 (2020). https://doi.org/10.1186/s40854-020-00198-x

Download citation

Received : 29 August 2019

Accepted : 18 August 2020

Published : 12 October 2020

DOI : https://doi.org/10.1186/s40854-020-00198-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Investor sentiment
• Stock market volatility

• Share full article

Advertisement

Supported by

A Turbulent Month Shows Markets Are Fickle. So Be Patient.

With the political conventions behind us and a rate cut expected soon, the market has been rallying, yet our columnist says it’s safer to play a longer game.

By Jeff Sommer

Jeff Sommer writes Strategies , a weekly column on markets, finance and the economy.

The stock market not only has recovered from its tantrum of early August, it is behaving as though that downturn never happened.

Stocks have been rallying joyfully. With the political conventions behind us and the Federal Reserve expected to trim interest rates next month, many Wall Street strategists have reined in their nervousness about a possible recession and are betting instead on good times to come.

There are compelling reasons for the bullishness. Inflation has been subdued, and data on retail sales and jobless claims suggested that the economy remains in good health. Corporate earning s — which fuel the market — have been outstanding. Executives in earnings calls with Wall Street analysts say they are cautiously optimistic, both about the economy and their companies’ abilities to continue reaping profits.

At their annual conclave at Jackson Hole in Wyoming on Friday, Fed officials signaled that they would begin cutting interest rates at their next formal policymaking session in September. The benchmark federal funds rate has stood at 5.25 to 5.5 percent since July 2023. Lower rates are typically ambrosia for market traders.

What’s more, as the year goes on, the political calendar may become more favorable for the stock market, which has been largely indifferent to the twists in the presidential race so far. Historically, regardless of who wins or loses, the market has tended to rally once the outcome of an election seems a sure thing.

In essence, there are plausible arguments for why the market will continue to head higher. Yet I’m not jumping onto this bandwagon — or embracing any strong view on where stocks are going.

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .