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This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.

This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.

The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.

The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.

Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.

Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.

Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1^st April 2010 to 21^st June 2023.

Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.

Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.

By examining the impact of the day of the week during the COVID-19 pandemic and the subsequent economic recession, it is possible to provide insights into market behaviour during volatile times that can be furnished to investors and policymakers for informed decisions.

This study investigates the day-of-the-week effect on the Colombo Stock Exchange (CSE), with particular emphasis on the variations in this effect during the COVID-19 pandemic and the subsequent economic crisis.

The study applies the Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model, allowing for the evaluation of asymmetric responses to positive and negative shocks. The data span from January 2006 to December 2022 and are segmented into different periods: the entire sample, war and post-war periods, the COVID-19 pandemic and the economic crisis period, each reflecting distinct market conditions.

The study uncovers a significant day-of-the-week effect on the CSE. Mondays and Tuesdays typically show a negative effect, while Thursdays and Fridays display a positive impact. However, this pattern shifts notably during the COVID-19 pandemic, with all weekdays exhibiting significant positive impact, and varies further across different waves of the pandemic. The economic crisis period also shows unique weekday effects, particularly before and after an important political event.

This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.

The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.

The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.

These results have crucial implications for investors regarding the benefits of international portfolio diversification.

Purpose: This chapter examines the effect of COVID-19 on the stock market volatility (SMV) in the Colombo Stock Exchange (CSE), Sri Lanka.

Need for the Study: The study is necessary to understand investor behaviour, market efficiency, and risk management strategies during a global crisis.

Methodology: Utilising daily All Share Price Index (ASPI) data from 2 January 2018 to 31 August 2021, the data are divided into subsamples corresponding to the pre-pandemic period, the pandemic period, and distinct waves of the pandemic. The impact of the pandemic is investigated using the Mann–Whitney U test, the Kruskal–Wallis test, and the Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model.

Findings: The pandemic considerably affected CSE – the Mann–Whitney U test produced different market returns during the pre-COVID and COVID eras. The Kruskal–Wallis test improved performance during COVID-19 but did not continue to do so across COVID-19 waves. The EGARCH model detected increased volatility and risk during the first wave, but the second and third waves outperformed the first. COVID-19 had a minimal overall effect on CSE market results. GARCH and Autoregressive Conditional Heteroskedasticity (ARCH) models identified long-term variance memory and volatility clustering. The News Impact Curve (NIC) showed that negative news had a more significant impact on market return volatility than positive news, even if the asymmetric term was not statistically significant.

Practical Implications: This study offers significant insight into how Sri Lanka’s SMV is affected by COVID-19. The findings help create efficient mitigation strategies to mitigate the negative consequences of future events.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

This paper aims to test for volatility spillovers among Islamic stock markets with the exogenous impact of geopolitical risk (GPR) to check the risk transmission among Saudi Arabia, Malaysia, Indonesia and Turkey. Researchers test for both the symmetric and asymmetric risk transmission.

For the symmetric response of volatility, the study uses simple generalized autoregressive conditional heteroscedastic (GARCH) and for the asymmetric response of volatility with the exogenous impact of GPR, the exponential GARCH models have been adopted.

The results suggest spillover effects exist from Turkey to Saudi Arabia, Indonesia to Malaysia and Saudi Arabia and Malaysia to Indonesia. The findings of volatility spillover from GPR to sample countries suggest that only Malaysia and Indonesia experience volatility spillovers from GPR.

The present study is limited to the context of four countries and Islamic equities; the study contributes to the literature on volatility spillover, Islamic finance, GPR and asset pricing.

This study contributes to individual, institutional investors’ policymakers’ knowledge in determining security prices, trading plans, investment hedging and policy regulation.

The extant literature disregards the GPR index to examine the volatility spillover effects among Islamic stock markets, which allow researchers to justify the mechanism of risk transmission due to GPR across the Islamic stock market.

To the best of the authors’ knowledge, this is the first research of its type to look at volatility spillover and GPR transmission in Islamic stock markets.

In this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.

Our study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.

We discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.

We combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).

This study examines the determinants of idiosyncratic risk (IR) or unsystematic risk. The study also examines the determinants of IR by dividing the firms into different categories: beta-based firms, liquid and illiquid firms and financially constrained (FC) and unconstrained (FUC) firms.

The fixed effects static panel data model specifications are formulated based on Hausman (1978) test for BRICS (Brazil, Russia, India, China, and South Africa) member countries over the period 2000–2019. Moreover, the t-test is applied to see whether the returns of different types of portfolios are significantly different.

The portfolio analysis results show that, on average, high IR firms tend to be small in size, highly leveraged, have low competitiveness, low profitability, less dividend yield and low returns for all the sampled countries. The sample paired t-test also confirms that a significant difference exists between extreme portfolios: small and large size and low IR and high IR portfolios. The panel regression results show that firm size, market power, price-to-earnings ratio, return on equity (ROE) and dividend yield negatively relates to IR. Yet, both leverage and liquidity are positively related to IR. However, the sign of momentum returns is mostly positive for the entire sample. The coefficient values for high-beta, FC and illiquid firms are more significant and large than the firms' counterparts for all BRICS member countries. These results support the hypothesis of an under-diversified portfolio and suggest that the above-mentioned firm-specific variables are the significant determinants of unsystematic risk.

The securities exchange commission, as the supervisor of the public limited companies, needs to increase its role in investor protection related to the uncertainty of investment in the capital market. Accordingly, in making investment decisions in a stock exchange, investors can use the information that captures unsystematic risk for investment decision-making.

This study is the first to explore the determinants of IR in top emerging countries. Second, none of the existing studies has focused on the determinants of the IR based on different categories of firms.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.