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US dairy markets have become increasingly volatile, which presents a challenge for dairy farmers and industry participants to manage risk, disturbing the stability of the industry. However, the study of dairy volatility is limited. This article investigates the return comovement and price volatility of four major US dairy commodities: butter, cheese, Class III milk, and dry whey. We also investigate the COVID-19 pandemic's impact on dairy volatility. Our results show that dairy commodities returns and volatilities are positively correlated, but only cheese and Class III milk correlate strongly. The volatilities of butter, cheese, and Class III milk are impacted comparably by return shocks and past volatility. However, dry whey volatility is predominantly driven by past volatility. Using multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) models, we demonstrate that return comovements are time-varying and volatilities are interdependent among dairy commodities. Volatility spillover effects are observed among dairy commodities, especially between dry whey and cheese. The COVID-19 pandemic amplifies dairy volatilities and spillover effects, with a more substantial impact on cheese and milk. Interestingly, dry whey experiences the least impact and performs better during the pandemic. Our results aid market participants in risk management and inform policymakers' decision-making processes. [ABSTRACT FROM AUTHOR]

This study explores the resilience of the Indian stock market in the face of global shocks in the post-pandemic era, focusing on its volatility dynamics and interconnections with international indices. Through a combination of Vector Autoregression (VAR), DCC-GARCH, and wavelet analysis, we analysed the time-varying relationships between the National Stock Exchange (NSE) of India and major global indices, including those from the U.S., Europe, Asia-Pacific, Hong Kong and Japan. Time series data of the selected indices have been collected for the period 1 January 2021 to 30 September 2024. Results reveal that while the NSE demonstrates resilience through rapid adjustments following shocks, it remains vulnerable to substantial spillover effects from markets such as the S&P 500 and European indices. Wavelet coherence analysis identifies periods of high correlation, particularly during major economic events, indicating that regional and global factors can periodically compromise market stability. Moreover, the DCC-GARCH results show a persistent but fluctuating correlation with specific markets, reflecting a connected and adaptive nature of the Indian market that is influenced by regional dynamics. This study emphasises the importance of strategic risk management. It highlights critical periods and indices that policymakers and investors should monitor closely to understand the economic resilience of the Indian financial market better. Further research could explore sector-specific impacts and the role of macroeconomic factors in shaping market responses. [ABSTRACT FROM AUTHOR]

We investigate the effects of non‐synchronous trading on volatility spillover for the G‐7 equity markets during the Eurozone sovereign debt crisis (ESDC) and the Covid‐19 pandemic crisis. For data synchronisation we utilise ΜΑ(1) adjusted return series to estimate the Baba‐Engle‐Kraft‐Kroner (BEKK) and the dynamic conditional correlation (DCC) models. We also consider the use of realised kernels as explanatory variables in the variance equation. In this set up, the contagion effects during crises periods are more perceptible, as the spikes are easier to interpret. We also check the robustness of our main results by applying, wavelet coherence analysis to G‐7 major equity indices with realised kernels, as well as local Gaussian correlations (LGC). Our findings suggest the empirical significance of the synchronisation effects for the US and the other G‐7 equity markets. We also conclude that realised kernels is an effective tool for mitigating non‐synchronous effects. These results underline the significance of quantifying the synchronisation effects in equity markets as well as international portfolio diversification strategies. [ABSTRACT FROM AUTHOR]

Finding suitable safe haven opportunities to protect emerging investments, such as cryptocurrencies, from external factors, such as Geopolitical risk, is a major concern for investors. Recognizing safe havens for these assets can help investors and traders manage risk, stabilize their portfolios, diversify their investments, and preserve capital against Geopolitical risk. To find the safe havens for cryptocurrencies regarding Geopolitical risk, this study proposes an approach to identifying the most suitable safe havens for cryptocurrencies highly affected by Geopolitical risk. First, the study identifies the cryptocurrencies that are more influenced by Geopolitical risk than others; by this, the assets that require hedging are discovered. Then, a new method, quantile-on-quantile regression, is employed to test the hedging ability of multiple assets from different markets. Once the outcomes of the quantile-on-quantile regression are cleared, the hedge effectiveness index by dynamic conditional correlation GARCH is calculated to validate the results. Both methods yield similar results, suggesting that the Forex market and stock indexes are the most suitable options as safe havens for cryptocurrencies. The study also finds that assets from the energy sector of the commodity market, such as Crude Oil and Natural Gas, are the weakest safe haven options. [ABSTRACT FROM AUTHOR]

This study employed the dynamic conditional correlation algorithm and incorporated the temporal dynamics of spillover effect to enhance the Multivariate Stochastic Volatility (MSV) model. Consequently, a DGC-t-MSV model (multiple stochastic volatility model of dynamic correlation coefficient with Granger causality test) was constructed to simulate and examine the volatility spillover effects between China's carbon market and the traditional energy market. The findings reveal the following: (1) A significant spillover effect in price volatility exists between China's carbon and traditional energy markets, with a notably fluctuating spillover index. The traditional energy market in China exerts a stronger unidirectional volatility spillover effect on the carbon market. Price fluctuations in the traditional energy market impact carbon market prices through mechanisms such as cost transmission and market expectations. (2) In the initial stages, the dynamic correlation between China's carbon and traditional energy markets showed an overall downward trend, underscoring the positive influence of policy incentives and technological advancements on the growth of alternative energy. A mutual weakening effect exists between the carbon and traditional energy markets. (3) Price fluctuations in China's carbon and traditional energy markets display a high degree of interdependence and short-term persistence, with evidence of a long memory and significant inertia in these price movements. Integration of the DGC-t-MSV model with the Bayesian approach and the Markov Chain Monte Carlo (MCMC) method and the introduction of a time-varying factor enabled the efficient measurement of the volatility spillover effect between China's carbon and traditional energy markets. [ABSTRACT FROM AUTHOR]

This study investigates risk transmission in the US energy instrument market to determine if certain factors, such as crude oil and natural gas, influence this market and whether stock or energy investment portfolios track their behavior. To investigate volatility spillover, the VAR-based connectedness approach is applied. This approach facilitates the measurement of interdependence across a network of variables, providing insights into aggregate, directional, and net interdependence. The use of the time-varying parameter vector autoregression (TVP-VAR) approach, as developed by Antonakakis and Gabauer, avoids the problems associated with selecting rolling window sizes and the resultant loss of observations during estimations. The analysis revealed a distinction between alternative and traditional ETFs, with lower interdependence observed among the volatility of alternative energy ETFs. While most energy ETFs transmit risk within the systems analyzed, some act as risk receivers, though their net receiving/transmitting character fluctuates. The results of this study are significant for investment portfolio managers. [ABSTRACT FROM AUTHOR]

This study examines the spillover dynamics and interconnectedness amongst sustainability indices, green bond markets, and oil price shocks. Using data from June 2013 to February 2023, this study employs spillover index methodology to analyze the transmission of shocks and volatility. The findings reveal that there is time-varying connectedness between all variables, which evidences a significant boost during times of crisis and extreme circumstances. Furthermore, the North American and US sustainability indices, along with the SP500 Environmental, Social, and Governance (SP_ESG) index, act as significant transmitters of shocks and volatility to other markets under different market conditions. Conversely, the Asia-Pacific, emerging market, and European sustainability indices, along with the green bond index, have emerged as net volatility receivers. Additionally, the study highlights the role of oil risk shocks as a transmitter of volatility under normal and bullish market statuses, while becoming a receiver in the lower quantile. These results have noteworthy implications for understanding the interdependencies and risks within financial markets and provide insights for investors, policymakers, and market participants. [ABSTRACT FROM AUTHOR]

This research seeks to offer a thorough investigation of the return and volatility spillover dynamics between DSEX and five financial assets from Gold, currency rates, energy, and stock indexes. Previous studies only used one econometric model to estimate the results, but this work used three well-known models for cross validation and also used portfolio weights and an optimal hedge to control risk. The DCC-GARCH technique is used to examine volatility spillovers, and the Quantile VAR and TVP-VAR frameworks are used to examine return spillovers. Surprisingly, three separate methodologies all provided equal results and arrived at the same conclusion. This study's findings corroborate the premise that these six components frequently exhibit considerable co-movement in futures trading. This paper illustrates that investors can greatly benefit by altering their portfolios based on the dynamic weights and hedge ratios, even if there is a trade-off amongst the degree of risk mitigation and portfolio revenue. [ABSTRACT FROM AUTHOR]

The study examines the relationship and linkages between spot and future prices of European Union Emission Trading Systems (EU-ETS) during 2019–2021. Dynamic conditional correlation specification of multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model was employed to examine the time-varying correlation between spot and future prices. Also, vector autoregression mean model and MGARCH-Baba-Engle-Kroner-Kraft volatility model was jointly estimated to model the spillover between EU ETS spot and future prices in the first and second moments. Lastly, we utilize the variance-covariance matrix of joint mean-variance model estimation to derive the optimal conditional hedge ratio as well as the hedge effectiveness of EU ETS future contracts. Our findings reveal a high conditional correlation and significant spillover between carbon spot and future markets in EU. Further, our study uncovers a high degree of hedge effectiveness for EU ETS future contracts. This is possibly the first study that examines the linkages between EU ETS spot and future prices pertaining to the recent transition stage of phase III and the initial stage of ongoing phase IV of the ETS market. Our findings pinpoint to ETS markets becoming more complete and in turn offering optimal hedging avenues. JEL Codes: G15, C58, Q38 [ABSTRACT FROM AUTHOR]

The objective of this research is to examine the dynamic volatility spillover effects that occurred during two significant worldwide crises—the COVID-19 pandemic and the Russia-Ukraine war—between the Indian stock market and six growing global stock markets: Brazil, Argentina, Indonesia, South Africa, Saudi Arabia, and Taiwan. The data for each index has been taken for 10 years from 2014-2024 and has been divided into three parts: before COVID-19 and Russia-Ukraine war (Nov 2014-March 2020), during COVID-19 (Mar 2020-Dec 2021), and during Russia-Ukraine war (Feb 2022-Sep 2024). The Granger causality test and DCC-GARCH model are used to analyse the time-varying correlations. The results reveal significant spillover effects, with Brazil and South Africa consistently influencing India, particularly during periods of heightened uncertainty. Brazil's continuous volatility spillover highlights its crucial role in influencing India's market behaviour during crises, even though markets like Argentina and Taiwan showed weaker connections. The study provides valuable insights for portfolio managers and policymakers to optimize global diversification strategies amid emerging market volatility and crisis-driven spillovers.

In a rapidly globalising world, economic boundaries are dissolving as stakeholders seek broader opportunities. Corporations are now multinational, and investors are increasingly turning to global stock markets to maximise gains. Volatility serves as a crucial benchmark, guiding investment decisions in this interconnected landscape. The current study looks at the time-varying spillover effects of the returns of the indices from January 6, 2020, until March 15, 2024, of the five economies of the world, namely, the S&P (United States), SSE (China), Nikkei (Japan), DAX (Germany), and Nifty (India). The conditional correlations and volatility spillovers are measured using the DCC-GARCH model and the Diebold and Yilmaz method. The study concludes that the transmission of information between the indices occurs in the long run except between Germany and China. Further, Germany and the US are net transmitters of volatility spillover, while China, Japan, and India are net receivers. The total spillover among the indices of these five economies is 39.37%.

This research delves into the interconnectedness and volatility dynamics within sectoral indices amid the COVID-19 pandemic, to determine the most appropriate model for elucidating these dynamics. It addresses the burgeoning interest among investors in comprehending sectoral dynamics within India's stock markets. Leveraging five years of daily closing price data (April 2017 to December 2021) sourced from the National Stock Exchange's official website for 16 sectoral indices and the Nifty50 index, the study employs the Diebold-Yilmaz Spillover 2012 framework alongside four GARCH models (E-GARCH, T-GARCH, FIE-GARCH, and FE-GARCH). Empirical analysis underscores that the mid-small financial sector is the riskiest sector among others, advocating for the inclusion of the pharmaceutical sector to mitigate portfolio risks. Furthermore, the research underscores the persistence of volatility across sectors and identifies the TGARCH model as the appropriate model to capture the volatility for most sectoral indices. [ABSTRACT FROM AUTHOR]

A rise in the yield of financial market assets could lead to variations in the returns of other assets over time due to arbitrage conditions. Consequently, this phenomenon may trigger spillover effects or cointegration among the volatilities of assets within financial markets. The aim of this study is to investigate spillover effects among American, European, Russian, and Turkish stock markets during the COVID-19 pandemic and the Russia-Ukraine war. Employing the diagonal BEKK-GARCH model from 2020 to 2023, the volatility transmissions within stock returns is examined. The results reveal significant GARCH effects alongside modest ARCH effects. Notably, during the COVID-19 period, the European market exerted the most significant influence on other markets, whereas during the war period, the US market dominated, and Turkish markets displaying the least impact for two periods. Furthermore, the findings indicate that the lagged cross-volatility persistence is lower during the Russia-Ukraine war period compared to the COVID-19 period. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be short‐lived, indicated by a relatively low DCC‐GARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCC‐GARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through real‐world examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the short‐lived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context. [ABSTRACT FROM AUTHOR]

The global food market's escalating volatility has led to a complex network of uncertainty and risk transmission across different grain markets. This study utilizes the Time-Varying Parameter Vector Autoregression (TVP-VAR)-Connectedness approach to analyze the price transmission and volatility dynamics of key grains, including wheat, maize, rice, barley, peanut, soybean, and soybean meal, and their dynamic spillover directions, intensity, and network. By integrating the TVP-VAR-Connectedness model, this research captures the time-varying variability and interconnected nature of global grain price movements. The main findings reveal significant spillover effects, particularly in corn prices, with prices of soybean dominating other grains while prices of peanut and corn experience higher external spillover effects from other grains. The conclusions drawn underscore the imperative for policymakers to consider a holistic perspective of all types of grains when addressing global food security, with this study providing valuable insights for risk management in the grain sector at both global level and country level. [ABSTRACT FROM AUTHOR]

The objective of this article is to examine the volatility spillover effect between the foreign exchange market and the stock market of Brazil, Russia, India, China and South Africa (BRICS) countries along with Japan as the developed country in the region, affecting the BRICS countries. Generalized Autoregressive Conditionally Heteroscedastic (GARCH) (1,1) method is used to study the volatility between the stock market and the foreign exchange market in selected countries, and asymmetric model, that is, Exponential Generalized Autoregressive Conditional Heteroscedasticity—EGARCH (1,1) is also used to investigate the presence of leverage effects in both stock market and foreign exchange market in selected countries. GARCH findings suggest a two-way volatility spillover between the stock market and foreign exchange markets for India, China and South Africa. In BRICS countries, volatility spillover from the currency market to the stock market is seen as more evident and robust as compared to spillover from the stock market to the currency market. A positive asymmetry in spillover is also observed from the foreign exchange market to the stock market. The findings of the study may provide valuable information to investors for decision-making in international portfolio investment and also for economic policymakers for their financial stability perspective. [ABSTRACT FROM AUTHOR]

This paper explores the influence of the risk–return relationship and volatility spillover on stock market returns of emerging economies, with a particular focus on the BRICS countries. This research is undertaken in a context where discussions on de-dollarization and the expansion of BRICS membership are gaining momentum, making it a novel and distinct exercise compared to prior studies. Utilizing econometric techniques to investigate daily market returns from 1 April 2008 to 31 March 2023, a period that witnessed major events like the global financial crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict, linear and non-linear models like ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, are employed to assess stock return volatility behaviour, assuming a Gaussian distribution of error terms. The diagnostic test confirms that the distribution is non-normal, stationary, and heteroscedastic. The key findings indicate a lack of the risk–return relationship across all BRICS stock markets, except for South Africa; a more pronounced effect of unpleasant news over pleasant news; a slow mean-reverting process in volatility; the EGARCH model is the best fit model as evidenced by a higher log likelihood and lower Akaike information criterion and Schwardz information criterion parameters; and finally, the presence of significant bidirectional and unidirectional spillover effects in the majority of instances. These findings are valuable for investors, regulators, and policymakers in enhancing returns and mitigating risk through portfolio diversification and informed decision making. [ABSTRACT FROM AUTHOR]

The objective of this research is to examine the dynamic volatility spillover effects that occurred during two significant worldwide crises--the COVID-19 pandemic and the Russia-Ukraine war--between the Indian stock market and six growing global stock markets: Brazil, Argentina, Indonesia, South Africa, Saudi Arabia, and Taiwan. The data for each index has been taken for 10 years from 2014-2024 and has been divided into three parts: before COVID-19 and Russia-Ukraine war (Nov 2014-March 2020), during COVID-19 (Mar 2020-Dec 2021), and during Russia-Ukraine war (Feb 2022-Sep 2024). The Granger causality test and DCC-GARCH model are used to analyse the time-varying correlations. The results reveal significant spillover effects, with Brazil and South Africa consistently influencing India, particularly during periods of heightened uncertainty. Brazil's continuous volatility spillover highlights its crucial role in influencing India's market behaviour during crises, even though markets like Argentina and Taiwan showed weaker connections. The study provides valuable insights for portfolio managers and policymakers to optimize global diversification strategies amid emerging market volatility and crisis-driven spillovers. [ABSTRACT FROM AUTHOR]

This study investigates the return and volatility spillovers between BRICS bonds and oil price volatility in the pre-COVID and during COVID periods by employing the bivariate EGARCH model, cross-quantilogram approach, and wavelet coherence framework. The findings are mixed when comparing the volatility transmissions with various nations and in two periods since variations in return and volatility spillovers between oil prices and bond market returns have co-moved through time. In particular, they increased during the COVID-19 outbreak, which supports the idea that financial market integration intensified during the crisis. Additionally, oil prices have a positive impact on bond markets in Russia and South Africa in both periods, while there are weak and negative relationships in China, India, and Brazil in pre-COVID and pandemic periods. This information has consequences for risk management and portfolio decisions for investors with a substantial position in the BRICS bonds market. [ABSTRACT FROM AUTHOR]

Based on daily data from 2013 to 2022, this study examines the spillover effects of volatility between cloud stocks and other asset classes (global stocks, treasury bonds, gold and crude oil) using the VAR connectedness approach. The results show that there is a significant spillover effect from global stocks and crude oil markets to the cloud stock market. The spillover effects become stronger whenever there are shocks such as economic crisis, turbulence in the international financial markets, COVID‐19 and global inflation. However, nearly 91% of the variations of cloud stocks come from within, suggesting that the diversification/hedging value of cloud stocks is potentially high. [ABSTRACT FROM AUTHOR]

Agricultural commodities price volatilities experienced an increase in the period of 2006 2008 and since then, the shocks from the global crises have been affecting these markets, as the Covid-19 pandemic period. Many studies have evaluated volatility spillovers around agricultural markets by focusing on crises cycles. However, few of these studies focus on emerging markets. This study examines the impacts of the Covid 19 pandemic on Brazilian agricultural price volatility. This study also considers the USD/BRL exchange rate and crude oil prices. We examine the volatility spillover effects and dynamic connectedness among the markets. A TVP-VAR model was applied, considering the specifications proposed by Antonakakis et al. (2020). The results indicate an increase in volatility connectedness after the Covid-19 outbreak, where volatility transmission affected all markets domestically. These effects were still significant after the Russia Ukraine conflict and dissipated from mid-2022 onwards. Overall, the exchange rate and soybean were the largest net transmitters during the pre- and post-Covid-19 pandemic, and corn was a net receiver. Crude oil had a significant transmission effect after a short period after the Covid-19 outbreak and the Russia-Ukraine war. Additionally, wheat was a significant volatility receiver after the Russia-Ukraine conflict and rice was a net transmitter during the Covid-19 pandemic. These findings corroborate that the crises cycles also affect Brazil but highlight that in the context of an emerging market, the exchange rate is more important in explaining agricultural price dynamics than crude oil.

This study investigates the volatility spillover effect of the COVID-19 pandemic on investor sentiment for the Johannesburg Stock Exchange (JSE). The study analyses how new cases and deaths affected the sentiment of participants in the stock market during the pandemic. It uses the South African Volatility Index (SAVI) as the main measure of market sentiment and the returns of the JSE main index. Daily data on the pandemic from 03/01/2020 to 19/03/2023 was obtained from the World Health Organisation (WHO) while the rest of the financial data was obtained from Yahoo Finance. The methods used are the Baba, Engle, Kraft and Kroner (BEKK) and dynamic conditional correlation (DCC) multivariate GARCH with the mean equations as a Vector Auto-Regressive (VAR) system. The results show that the pandemic had a spillover effect on investor sentiment. This spillover was asymmetric implying that negative news had more effect than positive news. Furthermore, new cases had more spillover effects on investor sentiment than new deaths recorded. The study recommends that investors should trade cautiously during pandemics considering the increased volatility and that policymakers need to minimise contagion effect from the virus to financial markets by calming down the markets or even halting trading temporarily.

The prices that govern the output of producers in specific industries hold great significance, particularly in the context of the renewable energy industry, which is profoundly influenced by the dynamics of critical mineral prices. Oil prices also affect mineral transportation and cost-competitive consumer choices, altering the renewable energy sector. This study examines the global transmission of shocks and volatility spillover effects of essential mineral and oil prices in renewable energy production covering the monthly data from January 1990 to June 2023. We use partial cross-quantilogram (PCQ) and rolling window-based recursive cross-quantilogram (RCQ) methods to address the dataset's non-normal and fat-tailed nature. The PCQ analysis shows that aluminum and copper price shock transmission and volatility spillover effects boost cumulative and decomposed renewable energy generation, particularly solar and wind power, in bullish market conditions with long-term memory. Nickel prices decline in bullish markets with long memory. Interestingly, oil price shock transmission and spillover effects benefit solar and wind energy generation over long time horizons. Thus, this study emphasizes the importance of leveraging mineral prices by determining favorable values and implementing market operations to promote environmental sustainability and sustainable development in the face of oil-based industrial expansion.

AbstractThis study empirically investigates the volatility spillover among the sectors of emerging markets, that is, India and China and developed markets, that is, the United Kingdom (UK) and the United States (US). Focusing on financial services, auto, oil and gas, Information Technology (IT), healthcare and real estate sectors, the research employs the BEKK GARCH and GO-GARCH models to analyze the daily data. Results reveal that the own market’s conditional volatility is primarily responsible for the volatility spillover in every sector. Further, the study also found evidence of major cross-market volatility spillover in the oil and gas, IT, healthcare and real estate sectors of emerging and developed markets. Specifically, the US IT sector dominated other markets’ IT sectors. The hedge ratio indicates that hedging between sectors of the emerging and developed markets is the cheapest, contrasting with the higher cost for hedging solely with the emerging or developed markets sectors. Investors are advised to monitor and rebalance their portfolios based on the volatility and dynamics of developed market sectors for optimum return. Additionally, the study found that the BEKK model is better for risk-return optimization.

Investor sentiment has the potential to serve as a predictive factor for cryptocurrency assets, yet its impact on volatility spillover across markets remains uncertain. This research investigates the influence of investor sentiment classification on the volatility spillover from stablecoins to conventional financial markets. Empirical findings reveal the role of stablecoins as net volatility receivers from financial assets during extreme fear sentiment conditions, suggesting that stablecoins may function as a potential safe-haven when investors experience significant fear, offering insights to investors and portfolio managers regarding the potential use of stablecoins as a risk mitigation tool within an investment portfolio.

This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew-t errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets.

The integration of commodities into stock exchanges marked a pivotal moment in the analysis of price dynamics. Commodities are essential for both daily sustenance and industrial processes and are separated into hard commodities, like metals, and soft commodities, such as agricultural produce. This paper provides a review of the relevant literature concerning the implications of commodity price volatility on commercial and financial landscapes, recognizing its profound impact on global economies. Drawing from Google Scholar and Science Direct, we analyze trends in academic publications until 2022, particularly focusing on the interplay between volatility spillover and ten different commodities, providing insights into the evolution of research paradigms over time. In a nutshell, the literature suggests that relationships between hard commodities are stronger since, in addition to being raw materials, they also serve as investment products. For the same reason, relationships between agricultural products appear to be relatively weaker. [ABSTRACT FROM AUTHOR]

Recently, Environmental, Social, and Governance (ESG) have been transitioned from optional to essential for sustainable economy and development not only in country scale but also in global scale. ESG becomes a rapid growth of indexing as the Index Industry Association (IIA) reported the ESG indexes number globally increased by 55 percent in 2022. Since global investors, also funds integrate ESG to make a decision for the portfolio investment. Regulators and policymakers also make an attention on the indexes. Therefore, many companies disclose ESG information and release ESG reports to present their participation and ESG performance. The indexes provide the average returns and risks of selected companies. They are early warning indicators for opportunities creating to avoid losses. Then, returns and volatilities would help investors to evaluate its performance. This issue is essential to analyze the connectedness across ESG indexes. It is also important to figure out the linkage of returns and risk spillovers from one market to others because the globally financial system. The time varying parameter - vector autoregressive (TVP-VAR) is employed to examine return and volatility connectedness of global ESG indexes in both advanced and emerging markets from 2020 to 2024. The linkage of return and volatility spillovers across markets would guide the investors for adjusting their portfolio. Also, the regulators would monitor the global financial stability. [ABSTRACT FROM AUTHOR]

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This study examines how the COVID-19 pandemic impacted stock market volatility and interconnectedness between India and other selected global economies. The analysis, using data from 2016 to 2024, reveals a substantial rise in volatility within both the Indian market and those of several other countries after the pandemic. Interestingly, the volatility transmission patterns also changed. While the Indian market's volatility significantly influenced Brazil, China, and Mexico throughout the entire period, the influence of the US market became negligible post-pandemic. In contrast, Russia exhibited a weak but statistically significant impact on India's volatility only after the pandemic. These findings highlight the lasting impact of the pandemic on global financial markets and emphasize the need for investors and policymakers to adapt. By understanding these new dynamics, investors can make more informed decisions, and policymakers can develop stronger risk management strategies and international coordination during periods of increased volatility. This study offers valuable insights for navigating the current financial landscape and the interconnectedness of emerging economies. [ABSTRACT FROM AUTHOR]

The present work has been designed to intensely investigate the capability of the commodity futures market in achieving the aim of price discovery. Further, the downside of the cash and futures market and transfer of the risk to other markets has also been studied using VaR, and Bivariate EGARCH. The findings of the work point that the metal commodity derivative market helps in the efficient discovery of price in the spot market except for nickel. But, in the case of the agricultural commodities, the spot is found to be leading and thus there is no price discovery except turmeric. On the other hand, the volatility spillover is bidirectional for both agri and metal commodities except copper, where volatility spills only from futures to spot. Further, the effect of negative shock informational bias differs from commodity to commodity, irrespective of metal or agriculture. [ABSTRACT FROM AUTHOR]