Stock Volatility Research Articles

Stock market volatility: a systematic review

PurposeThe 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/approachThis 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.FindingsThe 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/valueThis 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.

A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading

The financial market, as a complex nonlinear dynamic system frequently influenced by various factors, such as international investment capital, is very challenging to build trading strategies from the obtained market information. Deep Reinforcement Learning (DRL) combines the perceptual capability of deep learning and the control decision making capability of reinforcement learning to learn the mapping between financial market states and trading decisions by interacting with the environment. In this paper, an enhanced stock trading strategy, denominated efficient deep State-Action-Reward-State-Action (SARSA), is presented to tackle the algorithmic trading problem of determining optimal trading positions in the daily trading activities of the stock market. This algorithm is recognized for its properties of stable learning and convergence, attributes of critical significance within the financial domain, where stability assumes paramount importance, and excessive risk-taking should be averted. Furthermore, a novel deep network architecture called Bidirectional Long Short-Term Memory (BiLSTM)-Attention is introduced to address the challenge of accurately presenting the complex and volatile stock market. The BiLSTM-Attention greatly enhances the network’s capacity to recognize key features and patterns in stock market data, allowing agents to focus on the most relevant aspects of the data. Evaluations on DJI, SP500, GE, IXIC datasets from January 1, 2008 to December 31, 2022 show that our efficient deep SARSA algorithm outperforms a wide range of traditional strategies (B&H, S&H, MR, TF) and DRL-based strategies (TDQN, DQN-Vanilla). For example, on the IXIC dataset, the efficient deep SARSA strategy achieves an attractive Cumulative Return (CR) of 582.17% and a Sharpe Ratio (ShR) of 1.86, outperforming all other methods. These experimental results prove the performance of our method in enhancing stock trading strategies.

New ESG rating drivers in the cross‐section of European stock returns

AbstractWe assess the performance of two quantitative signals based on ESG scores across a large, multi‐national cross‐section of European stock returns. We test whether the cost of equity capital is more influenced by the upward momentum (measured over time) of the ESG scores of the firms issuing stocks or by their stability (identified as the volatility of the scores over time), measured around a changing mean level. We find that short‐term ESG momentum over 1 month has a significant impact on the cross‐section of stock returns, lowering the anticipated cost of capital and leading to positive average abnormal returns. This suggests that short‐term ESG momentum may represent a novel, priced systematic risk factor. Furthermore, we find strong evidence that an ESG volatility spread strategy which buys low ESG score volatility stocks and sells high volatility ones, generates a substantial alpha and affects the ex‐ante cost of capital. Both quantitative ESG signals result in portfolio sorting and long‐short strategies that enhance the overall sustainability profile of the issuing firms without compromising the raw average of their ESG scores.

The Fractional Soliton Wave Propagation of Non-Linear Volatility and Option Pricing Systems with a Sensitive Demonstration

In this study, we explore a fractional non-linear coupled option pricing and volatility system. The model under consideration can be viewed as a fractional non-linear coupled wave alternative to the Black–Scholes option pricing governing system, introducing a leveraging effect where stock volatility corresponds to stock returns. Employing the inverse scattering transformation, we find that the Cauchy problem for this model is insolvable. Consequently, we utilize the Φ6-expansion algorithm to generate generalized novel solitonic analytical wave structures within the system. We present graphical representations in contour, 3D, and 2D formats to illustrate how the system’s behavior responds to the propagation of pulses, enabling us to predict suitable parameter values that align with the data. Finally, a conclusion is given.

Enhancing predictive modeling for Indian banking stock trends: A fusion of BERT and attention-based BiLSTM approach

In the field of finance, deep learning techniques have been extensively researched for predicting stock prices. In this research, we propose a novel approach for predicting stock price movements using a combination of reviews and historical price data for SBI and HDFC stocks. As market volatility is influenced by numerous factors, it is crucial to consider it while predicting stock prices. To capture the interactions between the price and text data effectively, we create a fusion mix and utilize a hybrid information mixing module, designed using BERT and BiLSTM, to extract the multimodal interactions between the time series and semantic features. The proposed model, the hybrid information mixing module, is based on a multilayer perceptron and achieves high accuracy in predicting price fluctuations in highly volatile stock markets. Future research can extend this approach to include additional data sources and explore other deep learning techniques for better performance.

Stock Prediction Using Machine Learning Methods

The volatility and non-linear nature of the financial stock markets make it incredibly difficult to estimate stock market returns efficiently.. Investors need rapid access to precise information while trading stocks to make intelligent selections. Programable prediction approaches have demonstrated to be increasingly successful in forecasting stock prices with the introduction of artificial intelligence and better computing capacity. However, several variables impact the decision- making process as a stock market trades multiple stocks. Furthermore, it is impossible to forecast the behavior of stock prices. All of these elements make stock price prediction, both vital and tricky. This drives research into the most accurate prediction model that creates the fewest mistakes in its projections. This research studies machine learning approaches and algorithms in an effort to increase the accuracy of stock price prediction. Keywords— Machine Learning, Linear Regression, LSTM, SVM, Decision Tree, Random Forest

Impact before and after the Covid-19 pandemic on LQ45 stock volatility

The purpose of this study was to find out the difference between LQ45 stocks before and after the COVID-19 pandemic. The period used in this study is 2019-2020 using data from 45 companies whose shares are included in the LQ45 group listed on the Indonesia Stock Exchange. The data in this study were processed using IBM SPSS version 25 with a different test model (compare means paired samples t-test). It is found if there is a difference in the situation before and after the COVID-19 pandemic in the volatility of the price of the LQ45 shares listed on the Indonesia Stock Exchange. In general, there was a decrease of -6.14% after the incident in 2020 compared to before the COVID-19 pandemic situation occurred in 2019. There were 13 companies or reached 28, there were as many as 32 companies, or as much as 71.11 percent whose share value decreased. Issuers with the core business of national television, mining, pulp, distributor of heavy equipment, communication equipment, construction, some banking, spinning and weaving, and animal feed can be a choice for future investment.

Pandemics and Stock Price Volatility: A Sectoral Analysis

In this paper, we assess the impacts of the five most recent pandemics on the volatility of stock prices across forty-nine sectors of the economy in the United States. These five most recent pandemics are the 1957–1958 Asian flu, the 1977 Russian flu, SARS-CoV-1, swine flu and COVID-19. Applying the GJR-GARCH model, we find that pandemics other than COVID-19 have heterogeneous impacts on the volatility of stock returns. The results of our analysis indicate that COVID-19 has increased the volatility of stock returns in all sectors. Similarly, stocks in more than seventy percent of sectors in our study declined during the ongoing pandemic, perhaps reflecting the severity of the pandemic. In addition, our results on sectors such as healthcare and natural gas diverge from other literature. The mixed results on SARS-CoV-1 are partially explained by the fact it emerged at a time when stock valuations were particularly pessimistic. In the case of Russian flu, it was relatively short-lived and limited in spread relative to other pandemics in our study.

Impact of social Media and Google on stock Markets During a Pandemic: The Case of an Airline

The outbreak of the pandemic has dealt a tangible blow to the global economy, in particular by causing the collapse of stock markets. Most countries have taken measures to contain the coronavirus related to the restriction of human mobility. One of the main victims of such actions were airlines. In order to examine the direct and indirect effects of the pandemic, we investigated the long- and short-term effects on airline stock price volatility of the spread of coronavirus, social media attention to it, the vaccines against coronavirus and restrictive measures in relation to the pandemic. The ARDL model with MG and PMG estimates was applied after the preliminary stability tests for airlines in developed and developing countries. We analyzed the period of the greatest anti-COVID restrictions from 23 March 2020 to 23 March 2021. We reached the following conclusions. Firstly, the increase in the number of cases and deaths from COVID-19 was accompanied by a short-term increase in the volatility of airline stock prices. Secondly, Twitter’s increased focus on COVID-19-related restrictive measures and vaccines against it was accompanied by a short-term increase in airline stock price volatility. Thirdly, the increasing attention at Google on airline restrictions has been accompanied by the long-term effects of rising stock volatility. Our results demonstrate that with the spread of the Internet and social media, the impact of the pandemic on stock markets occurs not only through direct effects on the determinants of Solow’s economic growth model, but also through indirect effects of social media and the Internet on investor behavior through the formation of fear and hysteria in them.

Modeling and forecasting listed tourism firms’ risk in China using a trend asymmetric GARCH-MIDAS model

This study employs the multivariate trend asymmetric GARCH-MIDAS (TAGM) model, an extension of the GARCH-MIDAS model, to explore the potential asymmetric impact of uncertainty shocks, including oil and infectious disease shocks, on the long-term volatility of China’s listed tourism firms. Furthermore, we test the out-of-sample forecasting accuracy of uncertainty shocks to China’s listed tourism firms’ risk, which is measured by the volatility of tourism stocks after the outbreak of coronavirus disease 2019 (COVID-19). The results show that uncertainty shocks have a significant asymmetric effect on the long-run volatility of tourism stocks. The included uncertainty shocks improved accuracy in forecasting China’s listed tourism firms’ risk after the pandemic outbreak. The empirical results have important implications for tourism investment strategies in unstable environments.

Do implied volatilities of stock and commodity markets affect conventional & shariah indices differently? An evidence by OVX, GVZ and VIX

The current study aims to investigate how index returns of conventional and shariah indices of the USA, Europe, and Asia are affected by changes in oil prices, gold prices, VIX, gold-VIX, and oil-VIX. In our investigation, we used the S&P 500, S&P Europe 350, S&P Pan Asia, and their relevant shariah counterparts for the USA, Europe, and Asia. To examine how the explanatory factors affect the overall distribution of the explained variables, we used OLS and quantile regression. For the time frame prior to Covid-19, we discover that all volatility indices—OVX, GVZ, and VIX—influence returns of all indices simultaneously, and that all variables—aside from the spot price of oil—have a greater impact during the bear phase according to QR findings. Further, Volatility indices have a greater impact on volatility of index returns during the Covid-19 period. This is largely because the Covid-19 outbreak had a rapid impact on economies all around the world, and the only thing that affected financial markets consistently was high volatility. This is further supported by the findings of BEKK, which demonstrate that volatility extends across all markets and originates from commodities like gold, oil, gold-VIX, and VIX. Evidence for this can be seen in the fact that during the COVID-19 period, stock prices reacted more favorably to oil price volatility than to oil spot prices, which even went negative on April 20, 2020. Because of this, market stability can be promoted by reducing volatility through the prompt dissemination of crucial information, even while governments have little direct control over the prices of significant commodities like gold and crude oil.

Market beta coefficient and enterprise risk management: A literature review

One of the significant factors in the valuation of publicly listed firms is their market beta coefficient, commonly utilised in the capital asset pricing model (CAPM) as a proxy for stock volatility directly affecting market value. This article’s primary purpose is to explore the theoretical basis for future empirical research into the relationship between the market beta coefficient and enterprise risk management (ERM). The author explores academic literature about various researched variables affecting the market beta coefficient in the context of the neoclassical capital asset pricing model, which was founded on the premise of an efficient market hypothesis.

The Influence of Market Dynamics on Retail Investor Attention

Retail investors have become increasingly active in global markets over the past several years. However, the factors that drive retail investors to focus on particular stocks are unclear. Using a sample of UK FTSE 100 stocks, this paper analyses whether stock volatility, liquidity, returns, and trading volume have the power to attract the attention of UK retail investors, measured using the Google Search Volume Index. Overall, this paper finds positive relationships between three of the dynamics (volatility, returns, and daily trading volume) and increased retail investor attention. Greater stock illiquidity also coincides with an increase in the Google Search Volume Index, although this may be due to liquidity-impacting events. When conditioning on stocks by quartiles of market capitalisation, I find that the effects of returns and trading volume are greater in magnitude for the top 25% of stocks.

Can ETFs mitigate stock Co-movement? An analysis of an emerging market

This research paper aims to examine the impact of exchange-traded funds (ETFs) on stock movements in Iran, particularly during periods of stability and turbulence. The study utilizes a sample of 38 active funds listed on the Tehran Stock Exchange that invest in stocks, covering the period from 2015Q4 to 2023Q2. The GMM panel regression method is used to analyze the data. The results indicate that ETFs in Iran decrease co-movement during stable markets, but increase it during turbulent times. This may have significant implications for ETF investors and market regulators, as higher stock volatility reduces the diversification benefits that ETFs offer. Market regulators should be vigilant of elevated levels of stock co-movement during periods of turmoil, particularly in emerging economies where financial markets are more vulnerable.

Dynamic relationship between volume and volatility in the Chinese stock market: evidence from the MS-VAR model

Since market uncertainty, or volatility, serves as a crucial gauge for assessing the traits of market fluctuations, the link between stock market volume and price continues to be a focal point of interest in finance. This study examines the dynamic, nonlinear correlations between Chinese stock volatility, trading volume, and return using a hybrid approach that combines the Markov-switching regime with the vector autoregressive model (MS-VAR). The empirical findings are as follows: (1) The Chinese stock market can be divided into three regional systems: steady downward, steady upward, and high volatility. The three states have similar frequencies of occurrence, and their corresponding stable probabilities are not high, indicating that the Chinese stock market is unstable. (2) Asymmetric dynamic relationships exist between market volatility, investment return, and trading volume. For different regimes, while the effect of trading volume on volatility and return appears to be insignificant, the impacts of volatility and return on trading volume are considerably strong. (3) A regime-dependent, contemporaneous correlation between volatility and return is observed, which also reflects the behavior of the Chinese stock market “chasing up and down”. However, a positive contemporaneous correlation always exists between volatility and trading volumes in different regimes, indicating that uncertainty in the Chinese stock market is closely related to information inflow.

Determinants of Stock Performance for Selected Asia-Pacific Economic Cooperation Countries (APEC)

Asia-Pacific Economic Cooperation (APEC) held its 27th summit in 2020 and emphasized facilitating trade and investment in the Asia-Pacific region. This creates trading opportunities for investors in the stock market, allowing for potential financial growth and economic development. However, it becomes a challenge for investors to participate in the stock market due to APEC consisting of multiple countries with different risks and opportunities. The main objective is to find, analyse and determine the independent variables that can potentially affect the stock performance of all selected APEC countries in a significant manner. The independent variables included in the study are stock volatility, inflation rate, real interest rate and market capitalization. The study uses underpinning theories to support the independent variables, including Dow Theory, Cost-Push Inflation Theory, Liquidity Preference Theory and Free-Float Methodology. A static panel analysis takes place following the Random Walk Model with the adjusted formula to fit the study. The results suggest that all four independent variables have achieved significant probability values for all selected APEC countries. The significant outcomes may be the result of integration and interdependence among APEC economies, which has indeed created opportunities for investors to gain an advantage over the stock market.

Implied volatility surfaces: a comprehensive analysis using half a billion option prices

AbstractThis study delves into the critical aspect of accurately estimating single stock volatility surfaces, a task indispensable for option pricing, risk management, and empirical asset pricing. Utilizing a comprehensive dataset consisting of half a billion daily price observations for options on 499 US individual stocks and the S&P 500, the research investigates the accuracy of diverse methods for constructing volatility surfaces. The comparative evaluation of the three-dimensional kernel smoother by OptionMetrics (IvyDB US file and data reference manual, version 5.2, Rev. 01/27/2022, Computer software manual, New York, 2022), the semi-parametric spline by Figlewski (in: Robert F. Engle (ed) Estimating the implied risk neutral density. Volatility and time series econometrics: Essays in honor, Oxford University Press, Oxford, 2008), and a refined one-dimensional kernel smoother reveals the distinct superiority of the latter. This method consistently outperforms its counterparts across all moneyness, maturity, and liquidity categories, with markedly lower error metrics. The study further uncovers significant distortions in the extraction of Bakshi et al. (Rev Financ Stud 16:101–143, 2003) moments and skewness spanning induced by the noise-infused three-dimensional kernel smoother, which could potentially mislead derivative pricing and trading decisions. The findings offer valuable insights to traders, risk managers, investors, and researchers, suggesting a robust, one-size-fits-all method for crafting more accurate and less noisy volatility predictions. The research advances our understanding of option-implied information, its extraction, and broader implications for financial markets.

The extra value of online investor sentiment measures on forecasting stock return volatility: A large-scale longitudinal evaluation based on Chinese stock market

Numerous studies have purported to show that using online investor sentiment measures can enhance the accuracy of forecasting stock return volatility. However, many of these studies did not validate their findings with out-of-sample data, had insufficient validation samples, or failed to incorporate appropriate controls in their predictive models. None of these studies compared the predictive power of online sentiment measures with commonly used technical sentiment measures, nor did they investigate whether online sentiment measures offer any additional predictive value for volatility forecasting when controlling for technical measures. Using a large stock sample in China A-shares, we contribute to examining the additional predictive power of online sentiment measures for predicting stock volatility for different forecast horizons and different time windows, while controlling for technical measures. Methodologically, through using rolling origins and moving windows evaluation framework, we consider the benefits of using global forecasting models compared to local stock-specific models, and the robustness of our appraisal of the use of sentiment measures. We found that technical sentiment measures generally perform better than online sentiments. When controlling for technical measures, online sentiment measures have extra predictive value only when the forecasting horizon is long (e.g., one week or month), but the magnitudes of the improvement are relatively limited.

Multidimensional connectedness among the volatility of global financial markets around the Russian-Ukrainian conflict

This study investigates the impact of the Russo–Ukrainian war-induced uncertainty on multidimensional connectedness measures across the volatility of global stock and commodity markets using time-, frequency-, and quantile-based approaches. The results show that the war-induced uncertainty significantly increases the connectedness among the volatilities of global financial markets, notably in the short term, reflecting its impact on the decisions of traders. Unlike the impact of the Russo-Ukrainian war, the impact of COVID-19 is stronger and more apparent in the short, medium, and long terms. Considering various quantiles, the war considerably affects volatility connectedness at the middle quantile, whereas its impact becomes insignificant at the upper quantiles. Further analysis shows that a large increase in the uncertainty induced by the Russo-Ukrainian war can exert a significant and positive impact on the total volatility connectedness index. These findings are useful for the decision-making of traders and investors and can help policymakers formulate resourceful guidelines to deal with the adverse impact of war-induced uncertainty on financial markets.

Outward foreign direct greenfield investments and firms predicted long-term stock volatility levels and connectedness. Evidence from China

The paper offers an initial effort to unfold the effect of outward foreign direct greenfield investments (OFDGIs) on firms' long-term stock return volatility levels and connectedness. Within a sample of Chinese firms, in a GARCH-MIDAS model, we offer evidence that OFDGIs investments will reduce firms' long-term stock return volatility. We also introduced a measure of firms' stock return volatility connectedness and studied OFDGIs' effect on firms' dependence on other firms' risks. Implications for theory and practice are discussed.