Stock Market Volatility Research Articles

Did COVID-19 challenge the volatility of the sustainable stock market? An examination of Asian market

The coronavirus outbreak at the end of 2019 hit many aspects of the financial sector, especially the stock market. This research examines the impacts of the COVID-19 pandemic, exchange rates, gold price, and the Brent oil price on the volatility of the Sustainable Stock Market in Asia. A quantitative research method is applied using average monthly panel data from March 2020 to April 2022, covering the COVID-19 pandemic period. We employed panel regressions and applied Feasible Generalized Least Square (FGLS) in the analysis, which also serves as a robustness check. This study contributes to the literature by examining the variables significantly impacting sustainable investing, particularly in the sustainable stock market. Empirical results find that COVID-19, gold price and the exchange rate have negatively affected sustainable stock market volatility, while the Brent oil price has a positive impact on the volatility of the sustainable stock market. This study's recommendations infer that both investors and managers should consider the increase of COVID-19 cases and frequency-varying exchange rates to the USD on the Asian sustainable stock market volatility.

International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models

This paper aims to forecast the volatility of Chinese stock market under the effects of international crude oil shocks. Eight individual models, including multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) and bidirectional gated recurrent unit (BiGRU) are constructed. The realized volatilities of the CSI 300 index and ten primary sector indices are taken as explained variables, respectively. Four oil shock indicators and the autoregressive terms of the realized volatilities are taken as explanatory variables. The SHAP method is used to analyze their effects on the stock indices. Based on eight individual models, four kinds of combination models, i.e., a mean combination (Mean), a median combination (Median), a trimmed mean combination (Trimmed Mean), and two discount mean squared forecasting error combinations (DMSPE (1) and DMSPE (0.9)) are proposed. We compare forecasting performance between combination and individual ones. Empirical results show that the effects of international crude oil shocks on Chinese stock market are significant and have strong predictability. The effects on the energy, industry, optional consumption, and public sectors are greater than those on the CSI 300 and other sectors. Most of the combination models can effectively improve forecasting accuracy. In addition, by changing the benchmark model, the lengths of the rolling window, and the historical lengths of oil shock indicators, we find that most of the combination models are robust in volatility forecasting. This study is of guiding significance for individual and institutional investors to understand the operating mechanism of Chinese stock markets.

Behavioral Finance Analysis of the Spillover Effect of Major Health Emergencies on Shipping and China's Investment Market

Cross-market risk conduction is an important risk source in the global investment market. In recent years, the global market risk linkage cause-d by major public health emergencies has attracted much attention. This paper selects the COVID-19 epidemic as an example, and uses the shipping market as a representative indicator of the global economy to try to analyze the complete path of the epidemic impact from the global economy to the Chinese stock market. From the perspective of behavioral finance, it analyzes the role of investor sentiment in this risk linkage. The empirical results show that the transmission of epidemic risk between Chinese stock markets deviates from the global economic fluctuations but has regularity, and the momentum effect brought by investors ' overconfidence and overreaction makes the stock market reaction short-term and volatility amplification. Risk aversion and limited attention make the market volatility caused by the deterioration of the epidemic greater than the mitigation. This paper creatively conducts a full-path study on the transmission of epidemic risk between macro-economic and stock markets, and uses behavioral finance theory to quantitatively verify the impact of investor sentiment, which provides a theoretical basis for cross-market risk transmission research.

Investment in Commodities as Hedging and Safe-Haven Tools during the Periods of Stock Market Volatility

Investment in Commodities as Hedging and Safe-Haven Tools during the Periods of Stock Market Volatility

Forecasting US Stock Market Volatility: Evidence from ESG and CPU indices

This study investigates the predictability of U.S. stock market volatility using environmental, social, and governance (ESG) and climate policy uncertainty (CPU) indices based on the Markov-regime GARCH-MIDAS model. Our out-of-sample results show that ESG and CPU information is useful to forecast the U.S. stock market volatility. Notably, the ESG index proved to be a more powerful predictor for volatility estimation. Lastly, we ensure the reliability of the study's results through various robustness checks, including the model confidence set (MCS) method. These results suggest that investors and policymakers should consider carefully the impact of ESG and CPU risk on financial decision making and policy realms.

Impact Of Stock Market Volatility on Household Financial Assets

Impact Of Stock Market Volatility on Household Financial Assets

Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products

This study investigates the role of investor attention to the price of petroleum products (APPP) in forecasting Chinese stock market volatility. In the in-sample analysis, we find that a higher APPP index can lead to higher stock market volatility in the next month. The out-of-sample results show that the APPP index has strong predictive power for Chinese stock market volatility. Moreover, the volatility predictability of the APPP index is stronger than that of all commonly used economic and economic policy uncertainty predictors. Furthermore, when compared with the commonly used economic and economic policy uncertainty predictors, we find that the APPP index contains more valuable information for predicting stock market volatility than traditional predictors. When performing two robustness tests, alternative forecasting windows and lags of RV, the results are still robust. Further analysis indicates that the forecasting power of the APPP index for stock market volatility concentrates on the low-volatility regime.

"Investor attention fluctuation and stock market volatility: Evidence from China".

This paper examines the linkage between Chinese stock market volatility and investor attention fluctuation. In Heterogeneous autoregressive (HAR) model, first, we analyzed the linkage between both decomposed and undecomposed stock market realized volatility and investor attention fluctuations across full-sample and two-year moving window sub-samples. Second, we compare the predictive power of four models in short-, medium-, and long-term volatility forecasting. Empirical results show large positive attention fluctuation amplified Chinese stock market volatility after the outbreak of COVID-19, and negative small attention fluctuation significantly stabilized stock market volatility before COVID-19, and the impact dwindled in after COVID-19. The model incorporating decomposed realized volatility and decomposed attention fluctuation performs better in volatility Forecasting. This research underscores a shift in the dynamics between stock market volatility and investor attention fluctuations, and investor attention fluctuation improves the volatility forecasting accuracy of the Chinese stock market.

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.

Stock market volatility and economic policy uncertainty: New insight into a dynamic threshold mixed-frequency model

This study re-examines the relationship between U.S. stock market volatility and economic policy uncertainty (EPU) using the mixed-frequency dynamic threshold model. The empirical results exhibit several findings. The EPU has a threshold effect that is time-varying. Moreover, combining the dynamic threshold with the Markov-regime Mix-frequency model (MS-MIDAS), we find that this new model can significantly improve the predictive performance in a statistical view compared to other competing models (including the benchmark model). Our findings can provide new insight into volatility forecasting.

The Relationship Between Social Media Data and Stock Market Volatility

In recent years, the influence of social media data on the stock market has been deeply concerned and studied by academics and professionals, because the information reflected in these data has become increasingly important in contemporary society. By offering a complete synthesis of current research, evaluating the contributions made by earlier studies, and highlighting knowledge gaps warranting additional inquiry, this literature review seeks to examine the complicated link between social media data and stock market volatility. In order to accomplish this, the paper delves into a wide range of topics, such as where to get social media data, how to gauge stock market volatility, and what kinds of approaches have been taken to examine the possible connections between the two. Some research suggests that social media sentiment may forecast market patterns, while other research suggests a lower effect, as shown by our study, which concludes that there is either no relationship or a positive one between these factors. More study should focus on perfecting sentiment analysis methods, delving into nonlinear correlations, exploring variations in social media platforms, and addressing ethical problems in data utilization. We hope that these efforts will help us better comprehend the relationship between social media data and stock market volatility, allowing us to create more precise forecasting instruments.

Banking Risk Assessment During the COVID-19 Crisis

The COVID-19 outbreak has had a profound impact on the global banking industry and poses a variety of risks. Through an analysis of the global banking industry, this paper examines in detail the impact of COVID-19 on liquidity risk, credit risk, market risk, and systemic risk. The study finds that liquidity risk is mainly related to the recent Fed's interest rate hike, which, overlaid with the impact of the epidemic on the economy, has exacerbated banks' liquidity risk. Credit risk is mainly related to the increase in non-performing loan ratio and banks' credit management while studying Credit Suisse which plunged due to credit management. Market risk is mainly reflected in changes in interest rates and stock market volatility, which in turn contributed to a series of large bank losses and the bankruptcy of Silicon Valley Bank. Factors such as climate change and the development of digital currencies pose the systemic risk to banks, and the role of Basel III in insulating banks against systemic risk is significant. Based on these findings, the authors recommend that governments and regulators should take steps to address these risks and provide additional policy support.

The Impact of Monetary Policy on the U.S. Stock Market since the COVID-19 Pandemic

Inflation in 2021 and 2022 grew much faster than the Federal Reserve expected. The Fed downplayed inflation in 2021 and then increased the federal funds rate by 500 basis points between March 2022 and May 2023. This paper investigates how this unprecedented tightening has impacted the stock market. To do so, it estimates a fully specified multi-factor model that measures the exposure of 53 assets to monetary policy surprises over the 1994 to 2019 period. It then uses the monetary policy betas to gauge investors’ beliefs about monetary policy between 2020 and 2023. The results indicate that changing perceptions about monetary policy multiplied uncertainty and stock market volatility.

Music sentiment and the stock market in Vietnam

PurposeBy extending Edmans et al.’s (2021) music sentiment measures to the Vietnam market, the authors aim to investigate the impacts of music sentiment on stock market returns and volatility.Design/methodology/approachThe authors adopted Edmans et al.’s (2021) music-based sentiment to proxy for investor mood. The current study uses linear regression analysis.FindingsThe authors find that music sentiment is significantly and positively related to both stock returns and stock market volatility. The authors also show that music sentiment has a contagious effect: Global music sentiment and those in the United States, France and Hong Kong are significant drivers of the Vietnamese stock market. The authors also examine the effect on different industry returns and find that returns on stocks of firms in the communication services, consumer discretionary, consumer staples, energy, financials, healthcare, real-estate, information technology and utility sectors are significantly related to music sentiment. In addition to valence, the authors find that other Spotify audio features can be used to quantify music sentiment.Originality/valueThis study contributes to the behavioral finance literature that focuses on investor sentiment. The authors address this topic in Vietnam using high-frequency data.

Investment Behavior of Foreign Institutional Investors and Implied Volatility Dynamics: An Empirical Study on the Indian Equity Derivatives Market

The aim of this study is to examine the association between the capital flows of foreign institutional investors (FIIs) in the equity derivatives market in India and the implied volatility of options. Previous studies on FIIs and realized volatility in the equity market provide the basis for this study. Covering a period of ten years (2012–2021), this study established the importance of FII capital flows in explaining the implied volatility of options. The Granger causality test confirms the unidirectional flow of causality between FII and implied volatility (VIX) in the Indian stock market. The vector autoregression model developed in the study confirms the dynamic relationship between implied volatility and the investment behavior of foreign institutional investors (FIIs). The outcome of this study will help options traders to understand the mispricing of options because of FII’s buying pressure on implied volatility. The results will also help policymakers understand how institutional investors influence option pricing so that appropriate decisions can be made.

Frequency spillover effects between natural gas market, uncertainty, and stock market: new evidence from China.

This study investigates the correlation between the natural gas market, uncertainty, and the stock market in China. The research uses the BK model and considers both high- and low-frequency bands. According to the dynamic spillover indices and connectedness network, natural gas market, uncertainty, and stock market spillover influences are concentrated in the high-frequency band within 12 trading weeks and minor in the low-frequency band. The natural gas market receives information in the return model. In contrast, the uncertainty of the financial and energy market is the primary transmitter and substantially impacts the natural gas market return. In the volatility model, the natural gas market remains the information receiver but is affected by financial market uncertainty and stock market volatility. During the financial crisis and the COVID-19 pandemic, results indicate that financial market uncertainty, energy market uncertainty, and stock market volatility exerted a substantial influence on the natural gas market.

Volatility and Liquidity Comparison of Indonesian and Singapore Stock Market in COVID-19 Mobility Restrictions Era

The COVID-19 case found at the end of December 2019 became a pandemic in March 2020. The research aimed to see and understand the differences in the performance of the Indonesian and Singapore stock indices represented by the Indeks Harga Saham Indonesia (IHSG) and Straits Times Index (STI) before and after the implementation of community mobility restrictions (Pembatasan Sosial Berskala Besar (PSBB) in Indonesia and Circuit Breaker in Singapore). The stock index data were stock index prices at closing and stock trading volume. The stock index performance was measured by its volatility and liquidity. Meanwhile, data volatility with heteroscedasticity symptoms was measured using the GARCH (1,1) model. Meanwhile, the standard deviation was used to measure homoscedastic data. The results show differences in return volatility and stock index liquidity before and after restrictions on community mobility. The return volatility of the IHSG and STI is higher before the community mobility restrictions compared to the period after. IHSG experiences liquidity after PSBB I and before PSBB II. The conclusion that can be drawn from these results is that liquidity in Indonesia does not improve when PSBB I is implemented, but liquidity improves in PSBB II. Meanwhile, STI’s liquidity is higher in the period after the implementation of Circuit Breaker. These results indicate that implementing the Circuit Breaker helps to improve the stock index’s performance in Singapore because volatility decreases when the policy is implemented. The policy also reduced the liquidity of the Singapore stock index.

Central Bank Credibility’s Effect on Stock Exchange Returns’ Volatility: Evidence from OECD Countries

Central bank characteristics are important determinants of stock market returns and their volatility. While the literature has examined the effects of transparency and independence, no research has been conducted so far on the effect of central bank credibility on stock market returns’ volatility. A panel regression using financial and macroeconomic data from 45 OECD member countries over the period of 1998–2022 tested the hypothesis that central bank credibility determines stock exchange returns’ volatility. The results indicated that credibility reduces stock returns’ volatility, remaining robust and statistically significant across models. Economic growth also decreases stock market volatility, while money-market interest rates’ volatility, the stock market’s turnover ratio, and economic/financial crises act as amplifying factors of stock market volatility. All variables, except for economic growth, exhibit unidirectional causality, leading to changes in stock market volatility.

The impact of investor protection on stock market volatility

PurposeThis study aims to advance knowledge on the direct impact of the investor’s protection level on the stock market volatility, that is, whether investor’s protection is an important stock market volatility determinant.Design/methodology/approachA panel data was estimated using a sample of 48 countries, from 2006 to 2018, totalizing 31,808 observations. To measure stock market volatility and the investor protection level, a generalized autoregressive conditional heteroskedasticity model and the World Bank Doing Business investor protection index were used, respectively.FindingsThe results evidence that the protection of investors’ rights reduces the stock market volatility. This result indicates that a high level of investor protection, which is the result of a better quality of laws and policies in place that protect investor’s rights, promotes the country as a “safe haven.”Practical implicationsThe relationship that the authors intend to analyze becomes important, given that investor protection will give outsiders guarantees on the materialization of their investments. This study contributes important knowledge for investors and for the establishment of government policies as a way of attracting investment.Originality/valueAlthough there have been a few studies addressing this relationship, to the knowledge, none of them directly analyses the influence of investor protection on the stock market volatility.