Stock Volatility Research Articles

Pandemic or panic? A firm-level study on the psychological and industrial impacts of COVID-19 on the Chinese stock market

This study presents a thorough investigation of the relationship between the coronavirus disease 2019 (COVID-19) and daily stock price changes. We use several types of COVID-19 patients as indicators for exploring whether stock prices are significantly affected by COVID-19’s impact. In addition, using the Chinese stock market as an example, we are particularly interested in the psychological and industrial impacts of COVID-19 on the financial market. This study makes two contributions to the literature. First, from a theoretical perspective, it shows a novel quantitative relationship between the psychological response to the pandemic and stock prices. In addition, it depicts the mechanism of the shock to the stock market by pointing out the specific functional expression of the impulse reaction. To our knowledge, this is the first theoretical calculation of the impulse of a shock to the financial market. Second, this study empirically estimates the marginal effect of the COVID-19 pandemic on fluctuations in stock market returns. By controlling for stock fundamentals, this study also estimates diverse industrial responses to pandemic stock volatility. We confirm that the COVID-19 pandemic has caused panic in the stock market, which not only depresses stock prices but also inflates volatility in daily returns. Regarding the impulse of the shock, we identify the cumulative level of the pandemic variables as well as their incremental differences. As shown by our empirical results, the terms for these differences will eventually dominate the marginal effect, which confirms the fading impulse of the shock. Finally, this study highlights some important policy implications of stock market volatility and returns to work in the industry.

Risk-Return Trade-off and Volatility Characteristics in the Indian Stock Market

The study examines the volatility characteristics of Indian stock markets and their tradeoff between the risk and return. It finds a positive but insignificant association between the risk and returns during the subsample (the pre-COVID and COVID pandemic outbreak) and whole sample periods. The study also shows that the weak form of Indian stock markets is not sustainable. Consistent with the GARCH literature, persistent and asymmetric effects are evidenced, and the magnitude of the negative shocks has a larger immediate impact than the positive shocks. These results would help measure the volatility in the Indian stock markets and provide investors and regulators with necessary information about the market efficiency, persistency (long-memory process) and asymmetric effects.

Dynamic and Static Volatility Interruptions: Evidence from the Korean Stock Markets

We conducted a comprehensive analysis on the sequential introductions of dynamic and static volatility interruptions (VIs) in the Korean stock markets. The Korea Exchange introduced VIs to improve price formation, and to limit risk to investors from brief periods of abnormal volatility for individual stocks. We found that dynamic VI is effective in price stabilization and discovery, while the effect of static VI is limited. The static VI functions similarly to the pre-existing price-limit system; this accounts for its limited incremental benefit.

Policy Announcement, Investor Attention, and Stock Volatility: Evidence From the New Energy Vehicle Industry.

New energy vehicle (NEV) policies have greatly promoted the growth of the NEV industry in China, while also attracting a lot of investor attention. Using Chinese NEV concept stocks and related industrial policies, including purchase tax incentives (PTI) and promotion and application (P&A) policies, issued from 2011 to 2020 as the research setting, this paper adopts a panel data model to examine the impact of policy announcement on the volatility of NEV concept stocks, as well as the mediating role of investor attention in transmitting the impact. We find that NEV P&A policies have a significant and positive impact on NEV concept stock volatility, while PTI policies do not have a significant impact. Moreover, investor attention plays a partial mediating role in transmitting the impact of P&A policies on NEV stock market by increasing the stock volatility risk. Furthermore, there is heterogeneous effect of equity ownership in the relationship between policy announcement and investor attention on the volatility of NEV concept stocks; non-state-owned firms are more sensitive to the NEV P&A policies than state-owned firms. By analyzing the relationship between policy announcement and concept stock volatility, this paper enriches the research on NEV concept stocks and provides policy implications for the NEV industry.

Do global factors drive the interconnectedness among green, Islamic and conventional financial markets?

PurposeThis study aims to examine the connectedness among green, Islamic and conventional financial markets from December 2008 to May 2021. Moreover, the impact of global factors on the connectedness of given financial markets is also observed.Design/methodology/approachThis study first employed the time-varying parameter vector autoregressions (TVP-VAR) technique to explore the connectedness of markets. Second, This study utilized the wavelet coherence analysis to test the time-frequency impact of global factors in terms of implied volatilities of stock, oil, gold, currency and bond on the connectedness across financial markets.FindingsThis study finds Islamic stocks, sustainability index and S&P500 composite index are the net transmitters, whereas Sukuk, commodity index, bond market, clean energy and green bonds are the net recipient of spillovers. Time-varying features of green, Islamic and conventional financial markets are evident in system-wide connectedness. This study further evidenced that global factors drive the connectedness of financial markets, particularly during stressful times.Practical implicationsThe findings of this study furnish significant implications for policymakers, regulatory authorities, investors, financial market participants and portfolio managers in terms of carefully assessing the unique characteristics offered by each financial market in terms of risk mitigation and diversifying the portfolios.Originality/valueUsing a portfolio of green, Islamic and conventional financial markets, the uniqueness of this study lies in the examination of the connectedness of these markets by deploying the TVP-VAR technique. In addition, wavelet analysis offers a significant contribution in terms of global factors driving the connectedness of green, Islamic and conventional markets.

Bearish conditions and volatility persistence during COVID-19 can microchip stocks weather the storm?

PurposeThe purpose of this article is to examine the impact of panic and hysteria news on the volatility of microchip stocks during Covid-19.Design/methodology/approachThe authors use the P-GARCH (1,1) and random effects regression to model/examine the impact of Covid-19 panic and hysteria news on the overall microchip sector and individual firms. They further utilize the SVAR model to examine volatility spill-over from the microchip sector to the automobile and main technology sectors. Their time frame ranges from 6th January 2020 to 30th June 2021 to capture the effects of both waves of Covid-19.Findings The study results firstly reveal that Covid-19 panic and hysteria news have tremendous potential to model the volatility of microchip sector stock thus confirming the information discovery hypothesis. The authors secondly demonstrate the influence of Covid-19 cases, deaths and policy stringency on stock returns of individual microchip companies in different countries. Finally the authors confirm the presence of volatility spill-over from the microchip sector to other technology sectors.Research limitations/implicationsThe authors provide evidence to support the profundity of bad news in predicting stock behavior. The study results depict how Covid-19 has affected microchip stocks so that policy initiatives can be taken to protect the industry. The presence of volatility spill-over signifies the importance of diversifying portfolios by mixing technology and non-technology stocks.Originality/valueThe research strand on Covid-19 and individual sectoral stocks has received limited scholarly attention despite unparallel effects of the pandemic on different sectors.

Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions

Existing studies rely on exogenous drivers to improve the accuracy of the volatility forecasting of the least polluting fossil fuels, natural gas. However, the academic literature lacks a comprehensive analysis on the factors driving the volatility of clean energy stock indices, although the transition to renewable energy sources has resulted in the emergence of the clean energy sector as a fast-growing sector very appealing to environmentally responsible investors. This paper examines the ability of five uncertainty indices and seven global economic conditions to predict the realized volatility of clean energy stock and natural gas markets. Using daily return data of four exchange-traded funds to track the performance of the global clean energy stock market and natural gas prices, we construct the monthly realized volatility and apply various methods, including shrinkage methods. We find that both uncertainty indices and global economic conditions successfully predict clean energy realized volatility. The shrinkage methods consistently outperform dimensionality reduction methods and combination forecast methods for clean energy and natural gas. Whether clean energy ETFs or natural gas are used, the predictive information extracted from global economic conditions outperforms uncertainty indices using shrinkage methods. This suggests that real economic activities rather than text-based measures of uncertainty should be considered when investors and policy-makers analyze the volatilities of clean energy and natural gas. We further investigate the forecasting power of uncertainty indices and economic conditions in terms of variable selection, market conditions, and seasonality effects.

Individual investors' dispersion in beliefs and stock returns

AbstractWe construct a measure of dispersion in beliefs among individual investors. We find that dispersion in beliefs negatively predicts future cross‐sectional stock returns, and it is positively related to trading volume and stock volatility. We also find that illiquidity does not affect the significance of dispersion in beliefs in predicting future stock return, and that the negative disagreement‐return relation is significant under high‐sentiment periods but becomes insignificant under low‐sentiment periods. Moreover, investor characteristics affect their dispersion in beliefs even when controlling firm fundamentals. In particular, stocks with more wealthy, younger, and male investors tend to have higher dispersion in beliefs, and stocks with more experienced investors have lower dispersion in beliefs.

Does syndicate structure create value for initial public offerings? An empirical investigation for the Indian market

PurposeThis paper aims to investigate the impact of the underwriters’ syndicate size (SS) and its structure on underpricing (UP), oversubscription rate, liquidity and volatility. The authors use a database of 185 initial public offers (IPOs) issued in India during the period 2012–2019.Design/methodology/approachThe authors have used ordinary least squares regression and stepwise regression on cross-sectional data to construct the regression model for the dependent variables under consideration, namely, UP, subscription rate (SUB), listing day volatility and listing day liquidity.FindingsThe authors find that larger syndicates reduce UP. The authors also find strong evidence of a larger subscription rate for IPOs managed by larger syndicates, suggesting that larger syndicates generate more information in the market. Looking into the composition of investment banks in the syndicate, the authors find that syndicates comprising more lead managers and comanagers attract a higher subscription from potential investors. More book running lead managers and nonmanaging syndicate members help increase liquidity and reduce the volatility of IPO stocks on listing day. Additionally, the authors find that larger firms with reputed lead managers establish larger syndicates while venture-affiliated IPO firms prefer a smaller syndicate.Practical implicationsThe findings would interest issuing firms, investors, intermediaries and policymakers engaged in formulating syndicates for better management of IPOs.Originality/valueThe study extends the present literature on IPO syndicates, particularly in the Indian context as an emerging economy. The study extended the present understanding of SS and composition, creating value for the issuers.

Institutional investor attention and stock market volatility and liquidity: international evidence

ABSTRACT In this paper, we examine the influence of the daily institutional investor attention to particular stocks on stock volatility and liquidity. The institutional investor attention is measured from the number of times that users of Bloomberg terminal, who are mostly institutional investors, search for or read articles on a specific stock. Relying on a large international dataset of approximately a million daily observations over the period 2011–2020 from nine countries (Canada, France, Germany, Japan, Russia, South Korea, Switzerland, the UK, and the US), we find that this recent measure of institutional investor attention has a strong positive effect on stock volatility and liquidity. Confirmed by a battery of robustness tests, our findings suggest that this continuous barometer of attention by institutional investors can be used by financial practitioners to predict future stock volatility and liquidity.

The CoRisk-Index: a data-mining approach to identify industry-specific risk perceptions related to Covid-19

The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of corporate risk perceptions related to Covid-19. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of the US workforce. We construct two measures—the number of ‘corona’ words in each report and the average text negativity of the sentences mentioning corona in each industry—that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment rates across industries and with an established market volatility measure, and it preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides highly granular data on different dimensions of the crisis and the concerns of individual industries. The index presented here helps researchers and decision makers to measure risk perceptions of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macroeconomic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard.

Sleeping with the enemy: The perils of having the government on(the)board

We study the causal effect of unsought political connections on firm value. To address concerns of potential endogeneity and sample-selection bias we exploit the nationalization of Argentina's pension system, a unique natural experiment yielding exogenous variation in new political connections. We find unsought political connections to have a large negative effect on the value of newly connected firms. Yet this result only materializes when, in addition to becoming a shareholder, the government also obtains the right to appoint directors. Decreased stock liquidity or higher stock volatility do not explain this result, suggesting a channel that decreases expected cash flows to shareholders.

COVID-19 pandemic and risk dynamics of financial markets in G7 countries

PurposeThe purpose of this study is twofold: to examine the effects of the COVID-19 pandemic on the risk dynamics of stock and bond markets in G7 countries; and to examine if the stock-bond risk dynamics can be linked to government measures to contain the pandemic.Design/methodology/approachTo examine the pandemic impact on the risk dynamics of the bond and stock markets, this study chooses G7 countries for their efficient financial market properties. This study uses standard generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) and exponential GARCH (1,1) models to determine the most volatile and sensitive market, most persistent market during the crisis and the leverage effect between stock and bond markets. This study then uses a panel study to investigate whether this volatility in stock and bond markets is affected by the COVID-19 cases and various government responses (fiscal stimulus packages, monetary policy, emergency investment in health care and vaccine investment).FindingsThe findings of the study confirm that the bad news of the pandemic is causing higher volatility than good news for all seven stock markets. Canadian stock and bond markets are the most volatile, and Italian bond and stock markets are the most sensitive G7 countries. Japan has shown the highest persistence, and the stock market exhibits higher leverage than the bond market. Fiscal stimulus packages are helping to reduce bond market volatility, but none of these measures are effective in the stock market.Research limitations/implicationsThe pandemic is still spreading, and the rate at which it spreads wildly will always pose a limitation to any attempt to examine its full effect.Practical implicationsInvestigation of market volatility will help policymakers and market players formulate the best strategies to overcome and exit the crisis and plan post-pandemic solutions. It provides valuable insights for investors to rebalance their portfolios during highly volatile markets while preserving their risk appetite and investment objectives.Originality/valueThe paper provides evidence on the impact of the pandemic-induced crisis and the respective government responses on the volatility of competing capital markets (stock and bond) in countries that are considered most efficient in reflecting news.

Investor sentiment and stock volatility: New evidence

This study investigates the predictability of sentiment measure on stock realized volatility. We propose a new investor sentiment index (NISI) based on the partial least squares method. This sentiment index outperforms many existing sentiment indicators in three aspects. First, in-sample result shows that the NISI has greater predictive power relative to the others. Most sentiment indicators show predictability in the non-crisis period only while the NISI is also effective in the crisis period. Furthermore, the NISI exhibits more prominent superiority in longer horizons forecasting. Second, further analysis indicates that the NISI has robust predictability before and after the Chinese stock market turbulence periods while the others not. Importantly, the NISI is still effective significantly after considering leverage effect while most of the others not. Finally, out-of-sample analysis demonstrates that the NISI is more powerful than other sentiment measures. This result is reproducible in different robustness checks.

Is the effect of dividend policy on the volatility of stock prices stable? An empirical study on European countries

ABSTRACT We scrutinise the impact of dividend policy on stock price volatility by examining the relationship between volatility and three dividend policy indicators, dividend yield, dividend payout, and stock repurchases, for 1,221 firms in eleven developed and emerging countries in Europe during the 2003–2017 period. We employ fixed-effects panel data analysis and we check for possible effects of the 2008–2009 global financial crisis on the relationship under investigation. Our results reveal an inverse relationship in a comprehensive and comparative setting. The results are robust for sub-groups of countries, i.e., developed and emerging, and sub-sample periods, i.e., pre- (2003–2007) and post- (2010–2017) crisis.

THE NONLINEAR ASYMMETRIC RELATIONSHIP AMONG IMPLIED VOLATILITY INDICES OF INDIAN STOCK MARKET, GOLD, AND OIL: EVIDENCE FROM NARDL MODEL

The paper aims to examine the nonlinear asymmetric relationship among the implied volatility indices of the Indian stock market, gold, and oil for the period from 2nd March 2009 to 29th October 2021. Nonlinear Autoregressive Distributed Lag (NARDL) model results provide evidence of asymmetric nonlinear relationship among the selected variables in the short-run and the long-run. The positive and negative shocks to gold and oil implied volatility indices have a positive and significant influence on the implied volatility of the Indian stock market. The expected volatility of gold has a short-term symmetric impact on expected stock market volatility in the short run. Whereas, the implied volatility of oil has a long-run asymmetric impact on the implied volatility of the stock market. Increasing volatility in oil prices can be viewed as a signal for the starting point for the volatility of the Indian stock markets. In the long run, positive shocks to gold volatility have more impact on the expected volatility of the Indian stock market than the negative. This indicates that investors are shifting their investments from gold to stocks for higher returns when the gold prices are fluctuating.

Effects of Government's Policy in Stock Price: A Case of Nepse

The purpose of this study is to find out the relationship of government's policy on the price movement of Nepal stock exchange (NEPSE). This study followed a case study research design, because it offers a deeper perspective and clearer understanding of the stock price movement of Nepalese joint venture banks. The sample size of this study consists of five joint venture commercial Banks, economic analysis and survey reports conducted by central bank of Nepal (Nepal Rastra Bank).The judgmental sampling method has been applied for selection of joint venture banks. The study was totally based on secondary data. in order to make proper analysis descriptive and inferential statistics were used using SPSS software version 26. The finding of this study revealed that the GDP and import are inversely associated with stock price movement and CRR, export, interest rate and inflation are positively associated with stock price movement. Further, it is found that the macroeconomic variables are key factors to determine the Nepalese stock price movement. More importantly, stock market has been found to respond significantly to changes in the government policy. It is recommended that CRR, EXPORT, INTEREST RATE and INFLATION are major factors which largely affect the stock price movement of NEPSE. GDP and IMPORT are not compliance with the stock price movement as they produce negative association with the stocks volatility.

Predictability of the Realised Volatility of International Stock Markets Amid Uncertainty Related to Infectious Diseases

In the context of the great turmoil in the financial markets caused by the COVID-19 pandemic, the predictability of daily infectious diseases-related uncertainty (EMVID) for international stock markets volatilities is examined using heterogeneous autoregressive realised variance (HAR-RV) models. A recursive estimation approach in the short-, medium- and long-run out-of-sample predictability is considered and the main findings show that the EMVID index plays a significant role in forecasting the volatility of international stock markets. Furthermore, the results suggest that the most vulnerable stock markets to EMVID are those in Singapore, Portugal and The Netherlands. The implications of these results for investors and portfolio managers amid high levels of uncertainty resulting from infectious diseases are discussed.

Economic Policy Uncertainty Shocks and Chinese Stock Market Volatility: An Empirical Analysis with SVAR

This paper proposes a framework for examining the interaction between stock market volatilities and economic uncertainty shocks, aiming to understand better the influence of economic uncertainty shocks on the Chinese stock market. The major empirical results include the followings. First, the economic policy uncertainty shocks push the Chinese stock volatility up, increasing the market risk. A 1‐standard‐deviation shock of economic policy uncertainty will enhance the stock volatility of the two composite indices by approximately 7% in 12 months. Second, the stock volatility reacted more intensely to fiscal and monetary economic policy uncertainty shocks, with a 1‐standard‐deviation shock that can enhance the stock volatility of the two composite indices by more than 10% in 12 months. Third, different stock indices exhibit different patterns of cumulative impulse responses, and the reaction of the volatility of the SSE real estate index to economic policy uncertainty shocks is more significantly intense than other indices. Besides, we have proved the robustness of empirical results by reestimating the models with a lag order of 2. Overall, our research results can provide policy and managerial insights for the sustainable development of the Chinese stock market and beyond.

Effects of investor sentiment on stock volatility: new evidences from multi-source data in China’s green stock markets

The effect of investor sentiment on stock volatility is a highly attractive research question in both the academic field and the real financial industry. With the proposal of China's "dual carbon" target, green stocks have gradually become an essential branch of Chinese stock markets. Focusing on 106 stocks from the new energy, environmental protection, and carbon–neutral sectors, we construct two investor sentiment proxies using Internet text and stock trading data, respectively. The Internet sentiment is based on posts from Eastmoney Guba, and the trading sentiment comes from a variety of trading indicators. In addition, we divide the realized volatility into continuous and jump parts, and then investigate the effects of investor sentiment on different types of volatilities. Our empirical findings show that both sentiment indices impose significant positive impacts on realized, continuous, and jump volatilities, where trading sentiment is the main factor. We further explore the mediating effect of information asymmetry, measured by the volume-synchronized probability of informed trading (VPIN), on the path of investor sentiment affecting stock volatility. It is evidenced that investor sentiments are positively correlated with the VPIN, and they can affect volatilities through the VPIN. We then divide the total sample around the coronavirus disease 2019 (COVID-19) pandemic. The empirical results reveal that the market volatility after the COVID-19 pandemic is more susceptible to investor sentiments, especially to Internet sentiment. Our study is of great significance for maintaining the stability of green stock markets and reducing market volatility.