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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]

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 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 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]

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]

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]

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]

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]

A wide range of statistical and econometric models have been applied in the extant literature to compute and assess the volatility spillovers among renewable stock prices. This research adds to the body of knowledge by analyzing the dynamic asymmetric volatility spillover between major NASDAQ OMX Green Economy Indices, including solar, wind, geothermal, fuel cell, and developer/operator. The novelty of the research is that it distinguishes between positive and negative volatility spillovers in a time-varying fashion and conducts a connectedness network analysis. To do so, the study implements the Time-Varying Parameter Vector Autoregression (TVP-VAR) approach, as well as the connectedness network. The empirical investigation is based on high-frequency data between 18 October 2010, and 2 April 2022. The main findings may be summarized as follows. First, the analysis reveals a shift in the dominance of positive and negative volatility transmission during the study period, which represents compelling evidence of dynamic asymmetric spillover in the volatility transmission between renewable energy stocks. Second, the connectedness analysis indicates that the operator/developer and solar sectors are the net transmitters of both positive and negative volatility to the system. In contrast, the wind, geothermal and fuel cell sectors receive shocks from other renewable energy stocks. The asymmetric spillovers between the renewable energy stocks are confirmed using the block bootstrapping technique. Finally, the dynamic analysis reveals a substantial impact of the COVID-19 outbreak on the interdependence between renewable energy stocks. The findings above are robust to different lag orders and prediction ranges. [ABSTRACT FROM AUTHOR]

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]

To enhance economic cooperation in the Bohai Sea and Beijing-Tianjin-Hebei region, China introduced the Beijing-Tianjin-Hebei integration plan in 2014. Over the past decade, coordinated development has established a solid foundation, emphasizing economic and spatial structure optimization. The region's development has now achieved comprehensive, qualitative, and profound progress. Recognizing economic integration as crucial, this study analyzes the 2020-2023 stock data of listed firms in the region, utilizing the DY spillover index to examine sectoral volatility spillover effects, both statically and dynamically. Results reveal bidirectional, asymmetric spillovers, with heterogeneous spillover levels. Industry, raw materials, and information technology are key spillover sectors, significantly influencing others and inducing synchronization. Property and finance sectors are primarily recipients, influenced by others. Energy and communication services exhibit strong bidirectional spillovers. Volatility spillover strength is influenced by factors such as inter-sector correlation, market risk, macroeconomic changes. Studying these factors helps grasp volatility spillover patterns and understand sectoral volatility-factor relationships. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Practical implications: This study contributes to individual, institutional investors' policymakers' knowledge in determining security prices, trading plans, investment hedging and policy regulation. Social implications: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

The research examines the dynamic correlation between the volatility of the Bombay Stock Exchange (BSE) and the Small- and Medium-enterprises (SM Es) Index of BSE in India. It focuses on analysing the volatility spillover between SME and BSE by utilising the generalised autoregressive conditional heteroskedasticity (GARCH) model. The study extends its scope by examining the impact of the current pandemic, COVID-19, on the volatility of both SME and BSE by utilising the exponential GARCH and threshold GARCH models. Findings suggest that there is volatility spillover between the two indices. The results also highlight that the pandemic significantly affected the volatility of both indices. The findings of the study provide important implications for policymakers, portfolio managers, and investors, particularly when the Indian government has been emphasising on the growth and continuous support of the small and medium enterprises and increasing foreign investments in India's SMEs in recent years. [ABSTRACT FROM AUTHOR]

The study investigates the effects of the Russian-Ukrainian war, the COVID-19 pandemic, and exchange rate fluctuations on the average return, contagion dynamics, and persistence of risks associated with three staples in Türkiye: wheat, sunflower oil, and corn. The pandemic and the war disrupted the grain, oil seed, and fertilizer supply from Russia and Ukraine to Türkiye and several Middle Eastern and African countries, risking food insecurity. The specified VECM-Asymmetric BEKK-MGARCH model estimation uses data from January 2010 to May 2023. Results show that over time, short-term deviations move towards long-term equilibrium. The presence of reciprocal causality among wheat, corn, and sunflower oil prices underscores their mutual influence. The volatility pass-through between wheat, sunflower oil, and corn shows asymmetric transmission. Long-term uncertainty stemming from one market exacerbates that market's uncertainty but mitigates risks affecting other markets. The volatility can be reduced through domestic production expansion through comprehensive policies enhancing rural area development, assuring farmer access to inputs, and implementing market risk mitigating measures. Results also show that the effects of the pandemic and the Russia-Ukraine war have been amplified by exchange rate fluctuations. Market risk mitigation could involve the agricultural exchange to support an expanded number of licensed grain warehouses. In a broader scholarly context, this study stresses the interaction between global shocks, market uncertainty, and safeguarding the nation's food security.

Abstract This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies. Using the high-frequency trading data of exchanges, the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains. We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges. Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets. Finally, we identify the determinants of total connectedness in two types of volatility spillover, which can explain cryptocurrency or exchange interlinkage.

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.

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.

This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies. Using the high-frequency trading data of exchanges, the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains. We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges. Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets. Finally, we identify the determinants of total connectedness in two types of volatility spillover, which can explain cryptocurrency or exchange interlinkage. [ABSTRACT FROM AUTHOR]

Purpose: To estimate the volatility of exchange and stock markets and examine its spillover within and across the member countries of BRICS during COVID-19 and the conflict between Russia and Ukraine. Design/methodology/approach: The study utilizes the "dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH)" approach of Gabauer (2020). The volatility of the markets is calculated following the approach of Parkinson (1980). The sample dataset comprises the daily volatility of the stock and exchange markets for 35 months, from November 2019 to September 2022. Findings: The study confirms the existence of contagion effects among member countries. Volatility spillover between exchange and stock markets is low within the country but substantial across borders. Russian contribution increased significantly during the conflict with Ukraine, and other countries also witnessed a surge in the spillover index during the pandemic and war. Research limitations/implications: It adds to the body of literature by emphasizing the necessity of comprehending the economies' behavior and interdependence. Offers insightful information to decision-makers who must be more watchful regarding the financial crisis and its regional spillover. Originality/value: The study is the first to explore the contagion of volatility among the BRICS countries during the two biggest crisis periods of the decade. [ABSTRACT FROM AUTHOR]

Globalization of the world economy has ensured flexible exchange rate mechanisms are executed thereby creating interdependence between and within the stock, digital currency and foreign exchange markets. Unfortunately, in emerging African countries, few studies conducted on volatility spillovers failed to adequately establish the significance and pattern of volatility spillover effects between returns on Bitcoin, stock markets and exchange rates. Hence, the need for this study using the diagonal-BEKK approach. While Botswana had an inverse pattern of spillovers, Tunisia had a positive pattern. Bitcoin and stock prices both had volatility spillover effects between each other in South Africa. South Africa and Namibia were the only countries with significant volatility spillovers between stock prices and exchange rates. In countries like Kenya that had significant cross-volatility from the stock market to the exchange rate, news about the stock market stimulated reactions from investors that impacted volatility within the market. This volatility creates a multiplier effect on other economic circles of influence, depending on whether reactions are favourable to the market or unfavourable. When volatility in the Kenyan stock market rises, exchange rates in the next period experience less volatility, against the common theory that investors' actions that cause volatility in the stock market cause withdrawal of investments. [ABSTRACT FROM AUTHOR]

Price discovery and its related volatility spillover effect are significant indicators in commodity derivatives market to hedge the risk against sharp price fluctuations. This study empirically analyses the price discovery and volatility spillover using vector error correction model, Granger causality and bivariate exponential GARCH model (EGARCH) in Indian cardamom spot-futures markets. Daily time series data of spot market and near month futures contracts spanning the period from January 2009 to December 2020 have been used for the study. These price series data are based on the authenticated sources of Multi Commodity Exchange. The results of vector error correction model supported by Granger causality test indicates that spot market plays the role of leader as it is more efficient in reflecting the new information to prices. The results of bivariate EGARCH (1,1) model show that volatility spillover from cardamom spot market to futures market is dominant. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates (i) the return-volatility spillover between Bitcoin, Ethereum, Ripple, and Litecoin, (ii) the interdependence between cryptocurrencies' volatility and the US equity and bond markets' volatility, and (iii) the impact of the Covid-19 outbreak on the cryptocurrencies' return-volatility. A two-step estimation approach is considered where Univariate General Autoregressive Conditional Heteroskedastic models are estimated to model the volatility of the four cryptocurrencies then a Simultaneous Equation Model is estimated to model the interconnection between the cryptocurrency volatilities, the US equity and bond markets' volatility, and Covid-19 outbreak. We show that return-volatility spillovers exist among Bitcoin, Ethereum, and Litecoin while Ripple is the main transmitter of shocks. We find that the cryptocurrency market is detached from the US stock market but not from the US bond market. Finally, we show that a high economic and financial uncertainty in the US stock market due to pandemic outbreaks affects the price of Litecoin, Bitcoin, and Ethereum. However, shocks are short-lived. Our findings have practical implications; as the evidence of volatility spillovers among cryptocurrencies and their relative isolation from the majority of mainstream assets should be factored into the valuation and portfolio diversification strategies of investors. In crisis times such as those induced by Covid-19, investors who seek protection from downward movements in bond markets could benefit from taking a position in Ethereum. Policymakers can also rely on our findings to time their intervention to stabilize markets and control uncertainties inherent to stressful periods. [ABSTRACT FROM AUTHOR]

The carbon emission trading markets represent an emerging domain within China. The primary objective of this study is to explore whether carbon price volatility influences stock market volatility among companies subject to these emission trading regulations. Employing daily returns data from 293 publicly traded companies regulated by these emission trading markets, this study encompasses the national carbon market and eight pilot regional carbon markets spanning from August 2013 to October 2023. The results demonstrate that volatility in regional carbon prices positively impacts the stock volatility of companies in the corresponding emission trading region, indicating a volatility spillover effect. Moreover, this spillover effect is more pronounced in sectors marked by lesser carbon intensity than those with greater carbon intensity. The volatility transmission is more pronounced in coastal areas than in inland regions. However, no notable distinctions in volatility transmission are discerned between the periods before and throughout the COVID-19 pandemic. Vector autoregression analyses substantiate that lagged carbon price fluctuations possess limited predictive capacity for contemporaneous equity market volatility and vice versa. The robustness of these outcomes is fortified by applying the E-GARCH model, which accounts for the volatility clustering phenomenon. As the first investigation into the volatility spillover effect between China's emission trading market and corresponding stock markets, this study offers valuable insights into the investment strategies of retail investors, the formulation of carbon regulations by policymakers, and the carbon emission strategies of corporate managers. [ABSTRACT FROM AUTHOR]

This paper investigated exchange rate and stock price volatility connectedness and spillover in Brazil, Russia, India, China, and South Africa (BRICS) during pandemic-induced crises. We first extracted volatility using the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. Then volatility connectedness and spillover were investigated by using (Diebold and Yilmaz, International Journal of Forecasting, 28(1), 57–66, 2012) method. We find that exchange rate volatility and stock return volatilities are connected during pandemic-induced crises. The study also finds volatilities spillover among countries in the sample. Russia has strong volatility connectedness with India in these financial markets. The direction of volatility spillover is from Russia to India. Similarly, Brazil has strong volatility connectedness with South Africa and the direction volatility spillover is from Brazil to South Africa. Finally, China has a weak volatility connection with the remaining BRICS countries. Thus, the volatility transfer in these financial markets and across BRICS countries has economic implications. [ABSTRACT FROM AUTHOR]

This study is unique in examining the spillover and leverage effect of Smart Beta Exchange Traded Funds (SB ETFs) and their underlying indices and Cap-Weighted (CW) indices. We find unidirectional return spillover from most SB ETFs to CW indices and bidirectional return spillover between SB ETFs and SB indices using VAR/VECM model. Besides, we find that information is transmitted faster from SB ETFs to the indices than from indices to SB ETFs. Interestingly, we observe that innovations in SB ETFs explain 97% of variance in SB indices and 81% of variance in CW indices. Hasbrouck's information share of SB ETFs is highest (88%) followed by CW indices (5.6%). ARIMA-GARCH model shows that bidirectional volatility spillover exists between SB ETFs and the indices. ARIMA-EGARCH model provides evidence of leverage effect in SB ETFs, highlighting that volatility increases more after negative shocks than after positive shocks. Our study provides evidence of greater information transmission from SB ETFs to SB indices and to CW indices. [ABSTRACT FROM AUTHOR]

Abstract This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.

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.

This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco's financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets. [ABSTRACT FROM AUTHOR]

In the study, the multivariate VAR-EGARCH model created by Koutmos is used to evaluate the spread of return and volatility across China, Russia, and Brazil, which may one day become significant economic powers in the world economy (1996). The study uses 2,621 days of data from November 1, 2012, through November 11, 2022. The findings show that there is a return spread of approximately 13.4% from the Brazil market to the Chinese market and approximately 15% from the Brazil market to the Russian market. These ratios show that the spillover of returns from Brazil to the other two countries is high. In addition, volatility spillover from Brazil to Russia is approximately 4.5%, and from Brazil to China, it is approximately 1.5%. These results show that the volatility spillover is asymmetrical rather than symmetrical, as is the return spread from Brazil to the other two countries. Also, Brazil is giving volatility to China and Russia, and it is concluded that China is the one who takes volatility from Russia and Brazil. On the other hand, the markets with a high relative importance of asymmetry, which shows the effect of negative news compared to positive news, are Russia (3.21), Brazil (2.89) and China (2.58), respectively. [ABSTRACT FROM AUTHOR]

The study aims to investigate the impact of the stock market volatility spillover effect from major developed countries to emerging countries from the period 2014 to 2022 by using the GRACH model. For instance, Canada and Japan are the major contributors to G7 countries and whereas, India, Russia, Brazil, China and Turkey are the emerging countries in Asian and African subcontinents. It has been established that the stock market movement of Canada and Japan has substantially affected the stock markets of major emerging countries. However, Japan has major financial ties with all of the emerging countries except Russia. It is an astonishing fact that the insignificant coefficients of Japan and Canada with Russia indicate that the dynamics of Russian countries are a bit different than the rest of the world. The Russian economy is communist and generally perceived as a closed economy and the said does not have any major trade ties with the rest of the world. [ABSTRACT FROM AUTHOR]

This paper investigates the causal relationships and volatility spillovers between the BSE Bankex index and the stock prices of five major Indian banks (Axis Bank, HDFC Bank, ICICI Bank, Kotak Bank, and SBI). Daily data from January 2, 2018 to March 8, 2023 are used, and statistical techniques such as descriptive statistics, Unit Root test, Cointegration test, Ganger Causality test, OLS regression, and GARCH model are employed. The study finds bidirectional causal relationships between the bank stocks and BSE Bankex returns, suggesting that the movement of the bank stocks significantly affects the overall market returns and vice versa. The study also finds significant volatility spillovers between the bank stocks and BSE Bankex returns, implying that the shocks in the bank stocks affect the market returns and vice versa. The study's outcomes have practical implications for investors and policymakers. Investors can use the results to make informed investment decisions in the Indian stock market, while policymakers can use the findings to monitor the financial stability of the banking sector and design appropriate policy interventions to address any potential financial crises. Overall, the study's findings suggest that policymakers should proactively monitor and manage market risks to safeguard overall financial stability. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model. Design/methodology/approach: Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return. Findings: Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles. Originality/value: The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin's volatility on financial system stability. [ABSTRACT FROM AUTHOR]

Objective In financial literature, there are two well-explored characteristics of volatility. The first pertains to the asymmetric reactions of volatility to positive and negative news, while the second involves the presence of volatility spillover (contagion) between markets and various financial assets. The asymmetric behavior of volatility refers to empirical evidence that a negative return shock causes a greater increase in volatility than a positive return shock of the same size. Also, concerning the asymmetric impact of news on stock volatility, two hypotheses, namely the leverage effect and volatility contagion, have been postulated. Accordingly, this study aims to explore the leverage effect, dynamic conditional correlation, and volatility spillover among ten selected indices of the Tehran Stock Exchange. These ten industry indices collectively constitute over 75 percent of the overall Tehran Stock Exchange index. Methods In this study, the ARMA (1,1) form was utilized to construct the mean model. Then, the GJR-GARCH model was used to check the leverage effects. Finally, the DCC-GARCH (1,1) framework was employed, which helped to deeply analyze the dynamic linkages in volatility among selected indices of the Tehran Stock Exchange. The daily return data of industry indices, comprising a total of 1117 observations, was utilized during the period from March 25, 2018, to November 16, 2022. Results The result of the GJR coefficient, which was positive and significant for all return series - except for the Chemical and Oil Product Indexes-Indicates leverage effects exist. Also, the result of DCC (1,1) indicates the conditional correlation between all variables is positive and volatility spillover among them was strongly confirmed. Conclusion Financial markets, particularly the stock market, exhibit varied responses to positive and negative shocks, and these shocks impact the correlation between variables. For this reason, this research aimed to investigate the time frame during which the stock market underwent substantial fluctuations. This approach allowed for a more thorough and accurate examination of leverage effects, volatility spillover, and dynamic conditional correlation between returns. In the first half of 2019, despite the drop in the prices of commodities, oil, and the COVID-19 pandemic, the stock market experienced stunning growth, while investors were excited to buy regardless of the fundamental conditions of the companies. This enthusiasm to buy spread among stock market industries, but from the second half of 2019, the situation was completely reversed, and the market sold their shares at the slightest negative news. Hence, based on the results revealing the presence of leverage effect, dynamic conditional correlation, and volatility contagion, investors and portfolio managers can use these findings to mitigate risks and optimize their portfolios. [ABSTRACT FROM AUTHOR]

When facing volatility spillovers in energy markets, all players require risk mitigation strategies to insulate themselves from the same. To prevent energy markets from being strongly crashed by volatility spillovers, which even trigger financial crises, in this paper, we use network analysis as an aid to identify spillovers among the main nine energy markets. Specifically, we first measure the volatility spillovers among the main energy markets through a BEKK model. Based on this, influential markets are identified by using network analysis. The coal, wind and water energy markets should be paid close attention as they occupy vital roles in the volatility spillover network. Even though clean energy markets contribute more in terms of market stability, traditional energy markets are still important to ensure energy supply when experiencing extreme crashes caused by COVID-19. In this paper, we make the contributions to analysing volatility spillovers in multiple energy markets and identifying crucial energy markets in volatility spillover networks, then provide more market information that helps the government and policymakers effectively manage systemic risks caused by volatility spillovers. The effective risk management of crucial energy markets enhances economic recovery and stability, especially in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

The rapid expansion of renewable energy sources and their integration into the energy mix has generated scholarly interest in comprehending the interplay between renewable and conventional energy markets. This research aims to examine the (a)symmetric volatility spillover between the oil market and various regional renewable energy stock markets, namely the US, Europe and Asia. To achieve this objective, we employ the time-varying parameter vector autoregressive-based connectedness (TVP-VAR) approach, which allows analysing the interconnection and transmission of shocks between the different markets. Based on an analysis of daily data relative to the different regional renewable energy stock markets and international oil prices, the findings suggest the presence of a dynamic volatility connectedness between the green and brown energy stock markets. The extent of connectedness is contingent upon the specific regional renewable energy market under consideration. Moreover, the decomposition of the volatility series into good and bad volatility emphasizes an asymmetric pattern, which becomes more pronounced during periods of major events. On average, the oil market and the Asian renewable energy stock market are net receivers of volatility shocks. In contrast, the US and European renewable energy stock markets are net transmitters of shocks. Our findings provide investors with valuable insights for portfolio design and risk management decisions. [ABSTRACT FROM AUTHOR]

One of the factors affecting the efficiency of the pharmaceutical and food industry index in developing countries such as Iran, which has a high degree of uncertainty in macroeconomic variables, is the exchange rate (dollar). The pharmaceutical and food industry is considered one of the strategic industries in the country in terms of its direct relationship with people's health, therefore managers and policymakers have always paid attention to it. Various factors affect the food and pharmaceutical industries, one of the most important of which is exchange rate fluctuations. The main purpose of this study is to investigate the contagion of fluctuations between the return of the dollar and the return of the pharmaceutical and food industry index due to the importance of these two industries in the country's economy. In this study, first, ARMA (1,1) model was used to extract the residuals. Then, GJR-GARCH model was used to check the Leverage effects, and finally, The DECO-GARCH model was used to check the contagion of volatilities between the dollar return, pharmaceutical and food industry indices. Also, the data used in this research was extracted daily from the Bourseview.com website for the period of March 25, 2020, to November 4, 2023. The result of GJR coefficient, which is positive and significant for all return series leverage effects exist. AlsoThe results of DECO-GARCH model estimation indicate the existence of a spillover effect between the dollar return and the pharmaceutical and food industry indices. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to analyze the volatility dynamics and spillover, symmetric and asymmetric, among BRICS countries' stock markets. The paper employed dynamic conditional correlation and asymmetric generalized dynamic conditional correlation models to examine the bidirectional volatility spillover. The study preferred these sophisticated and flexible models as they have several advantages over other econometric models. The findings of the study indicate a long-term integration and a significant bidirectional spillover effect (both symmetric and asymmetric), suggesting a close relationship among the stock markets of BRICS countries. Consequently, diversifying one's portfolio between these markets would not yield substantial economic value for investors. Also, the study finds the same pattern of flow in asymmetric volatility spillover but at a different significance level. To the best of the authors' knowledge, this is the first study investigating the asymmetric volatility spillover effect among BRICS countries' stock indices using the DCC-MGARCH and AGDCC-MGARCH models. [ABSTRACT FROM AUTHOR]

This study investigates the volatility spillover between emerging South Asian equity markets and developed U.S. market. Specifically, the study examines the entire sample period and three sub-periods: pre-crisis, crisis, and post-Global Financial Crisis (GFC) periods in the emerging markets of Pakistan, India, and Sri Lanka, as well as the U.S. as developed stock market. The study analyses both unidirectional and bidirectional shock and volatility spillover among the markets to determine the direction of risk transmissions. The analysis utilizes a multivariate GARCH-BEKK model. The results indicate that volatility transmissions increased due to the GFC. Furthermore, the South Asian emerging markets exhibit shock and volatility spillover during the crisis period compared to the pre- and postcrisis periods. Additionally, the patterns of risk transmission were timevarying. These findings support the notion that market volatility increases during turbulent events, such as the Global Financial Crisis. The implications of these results are important for policymakers, investors, and researchers interested in international diversification and market efficiency within the studied markets especially during turbulent periods. [ABSTRACT FROM AUTHOR]