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The study aims to explore the relation between dividend policy of any company and its stock volatility.

Companies listed on six GCC stock markets are used in the analysis and the data ranges from 2006 to 2020. Fixed effect and random effect panel data analysis is used to explore the association between stock volatility and the dividend policies.

A significant negative relation is observed between dividend payout and stock volatility. Also, significant negative relation between stock volatility and equity is found, whereas insignificant positive relation is observed between asset growth and stock volatility.

The data of all listed companies on six GCC markets were not available.

The question of raising dividend or maintaining at the current level is of utmost importance for the managers of any company before making any investment decisions. Also, the investors look at the dividend announcements as a sort of signal about the future prospect of the company. A stable or fluctuating dividends may be preferred by the investors that ultimately changes the stock price of any company.

The relationship between dividend policy and the volatility of stock price is explored for emerging GCC markets which is the major significance of this paper which will have many social impacts on various stakeholders of any company including investors, regulators and employees, etc.

To the best of the authors’ knowledge, no study for GCC markets is done to establish a relation between stock volatility and the dividend policies which is needed by the academicians to further explore the behavior of these markets.

The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times.

The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries.

The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

This paper explored the new features of emerging stock markets, in order to point out the most associated indicators of increasing stock return volatility, which may lead to instability of emerging markets. The study covers a sample of five geographical areas of emerging economies, including Mexico, Korea, South Africa, Turkey, and Malaysia. It used the backward multiple‐regression technique to examine the relationship between monthly changes of stock price indices as dependent variable and the associated predicting local as well as international variables, which represent possible causes of increasing price volatility and initiating crises in emerging stock markets. The study covered monthly data for a period of forty‐eight months from January 1997 to December 2000. The study revealed that stock trading volume and currency exchange rate respectively represent the highest positive correlation to the emerging stock price changes; thus represent the most predicting variables of increasing price volatility. International stock price index, deposit interest rate, and bond trading volume were moderate predicting variables for emerging stock price volatility. While changes in inflation rate showed the least positive correlation to stock price volatility, thus represents the least predicting variable.

Focusing on the spillover effects between the CSI 500 stock index futures market and its underlying spot market during April to September 2015, the purpose of this paper is to explore whether Chinese stock index futures should be responsible for the 2015 stock market crash.

Using both linear and non-linear econometric models, this paper empirically examines the mean spillover and the volatility spillover between the CSI 500 stock index futures market and the underlying spot market.

The results showed the following: the CSI 500 stock index futures market has significant one-way mean spillover effect on its spot market. The volatility in CSI 500 stock index futures market also has a significant positive spillover effect on its spot stock market, and the mean value of dynamic correlation coefficient between the two market volatility is 0.4848. The spillover effect of the CSI 500 stock index futures market on the underlying spot market is significantly asymmetric, characterized by relatively moderate and slow during the period of the markets rising, yet violent and rapid during the period of the markets falling. The findings suggest that although the stock index futures itself was not the “culprit” of Chinese stock market crash in 2015, its existence indeed accelerated and exacerbated the stock market’s decline under the imperfect trading system.

Different from the existing literature mainly focusing on CSI 300 stock index futures, this paper empirically examines the impact of the introduction of CSI 500 stock index futures on 2015 Chinese stock market crash for the first time.

This study aims to investigate the moderating influence of board composition on the relationship between capital structure and stock price volatility in Ghanaian listed firms.

Utilizing data from 20 Ghanaian listed firms spanning 2011–2021, the study employs the generalized method of moments (GMM) technique for analysis.

The results indicate that both capital structure and board composition significantly influence stock price volatility in Ghanaian listed firms. Specifically, a higher debt-to-equity ratio leads to higher stock price volatility, while a better board composition reduces stock price volatility. The study further establishes a negative and significant moderating effect of board composition on the relationship between capital structure and stock price volatility, implying that firms with a well-structured board experience a more pronounced reduction in stock price volatility as they adjust their capital structure.

The findings offer practical insights for regulators, shareholders, managers and creditors on enhancing corporate governance practices and optimizing capital structure decisions to mitigate financial risk and improve firm value.

This paper contributes to the existing literature by presenting novel insights into how board composition influences the link between capital structure and stock price volatility from a developing country perspective, making it a pioneering effort in the Ghanaian context.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

The purpose of this study is to examine how the volatility interruption (VI) mechanisms affect idiosyncratic volatilities in Korean stock markets.

Collecting the South Korea Stock Market (KOSPI) data from June 15, 2015 to March 31, 2019, we collect each residual,  εi,t, from three different estimated models: capital asset pricing model (CAPM), FF3 and FF5. To estimate the conditional idiosyncratic volatility, the authors employ two conditional time-varying measurements: GARCH and TGARCH.

The results show that the conditional idiosyncratic volatility increases when stock prices reach the upper and lower static limits, indicating the implementation of adopting static VI mechanism neither stabilize market conditions nor reduce excess volatility along with the existence of price limits.

Although market regulators and policymakers improve market conditions with the advanced VI mechanism, the empirical results show the adverse effect of the mechanism. Not allowing investors to earn above average returns without accepting above average risks makes Korean stock markets inefficient along with advanced VI mechanisms.

Clean energy stocks are exhibiting signs of increasing volatility reflecting the varied and conflicting strategies employed by nations to pursue energy security objectives. In this regard, this paper aims to examine the response of NASDAQ clean energy stock returns volatility to the influences of external energy security elements including oil price, natural gas price, coal price, carbon price and green information technology stock price.

The paper uses symmetric and asymmetric generalised autoregressive conditional heteroskedasticity models (GARCH and TGARCH, respectively), which incorporate external energy security elements as exogenous variables, to estimate volatility models for clean energy stock returns.

Although, prices of oil, coal and natural gas are negatively associated with NASDAQ clean energy returns volatility, only the effect of natural gas price is significant. While carbon price affects NASDAQ clean energy returns volatility positively, green information technology price affects the volatility negatively. These results are robust to exponential GARCH and lead-and-lag robust ordinary least-squares as alternative estimation methods.

The study lumps the effects of all other external and internal factors, including internal energy security elements, in the autoregressive conditional heteroscedasticity (ARCH) term to predict NASDAQ clean energy returns conditional variance. GARCH method does not disentangle individual roles of the factors captured in the ARCH term in predicting volatility.

Results documented imply that natural gas appears a closer substitute for renewable energy sources than crude oil and coal, such that its price rise is perceived as good news in the NASDAQ clean energy financial market, while a fall is considered bad news. Furthermore, both an increase in carbon price and a decrease in green information technology stock performance are perceived as negative shocks.

In assessing risks associated with clean energy stocks, investors and fund managers should carefully consider the effects of external energy security elements.

To the best of the author’s knowledge, the paper is the first to identify external energy security elements and examine their effects on clean energy stock volatility.

Broker-dealer leverage volatility increases during booms and crisis periods, but its impact on stock prices is relatively unexplored. This paper aims to investigate whether broker-dealer leverage volatility is a key driver for stock prices.

This paper collects the US quarterly data of broker-dealer book leverage and three leading stock market indicators (S&P 500, DJIA and Nasdaq) for the period of 1967–2018. The research uses a multivariate GARCH-in-mean VAR to examine the impact of leverage volatility on each of the stock market indicators. A split-sample analysis (pre-1990 and post-1990) has also been performed to show the robustness of the result.

The research finds that broker-dealer leverage volatility does not have any significant impact on stock prices.

Broker-dealers are important financial intermediaries, and there is a huge literature exploring the relationship between their leverage and asset prices. But, the relationship between broker-dealer leverage volatility and asset prices is not explored yet. This study fills the gap and provides the first evidence that broker-dealer leverage volatility does not play any major role in the theory of stock pricing. The research proposes that the stock holding decisions of the investors should depend only on the first moment of leverage and not on the second moment of leverage. The study concludes that high broker-dealer leverage volatility is not a sinister signal for the US stock market.

This study aims to investigate which stock characteristics and corporate governance variables affect stock price overreaction and volatility during the COVID-19 pandemic period.

A set of stock characteristics and corporate governance variables which may affect price overreaction and volatility were identified following a review of the literature. A dummy variable was created for the cross-sectional analysis to take into account the unique sector effect in the consumer staples sector. Out of sample analysis was conducted to confirm the robustness of the main results.

The empirical results consistently show that size, dividend and trading volume determine the stock price reactions when the market is in turmoil during the pandemic period. Board size and average board tenure exhibit moderate effects on reducing the stock price reactions, but the effects become insignificant while controlling for the firm characteristics in the regressions. The results remain robust when tested out of the sample. More interestingly, a consumer staples sector effect is identified and tested. The test results show that the consumer staples sector effect mitigates the stock price reactions.

The results have practical implications for investors who aim to manage desired levels of risk in their portfolios during the pandemic. The results also provide meaningful insights to stock market speculators regarding pandemic-related speculation opportunities.

This study makes a meaningful connection between the irrational stock market anomalies and the COVID-19 pandemic.