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This paper aims to examine whether the volatility of returns in commodity (gold, oil), bond and forex markets is related over time to the volatility of returns in equity markets of Bangladesh, Indonesia, Pakistan, Philippines, Turkey and Vietnam. In addition, the authors analyze the integration of the commodity, bond, forex and equity markets across these markets.

The dynamic conditional correlation GARCH (DCC-GARCH) model is used to capture the time-varying conditional correlation among markets. The authors use daily data of stock prices, oil prices, gold prices, exchange rates and 10 years' bond yields of the six countries from Datastream and investing.com from January 2001 to April 2021.

Findings reveal that the parameters of dynamic correlation are statistically significant which indicates the importance of time-varying co-movements. Estimation of the DCC-GARCH model suggests that the stock market is significantly correlated with bond, forex, gold and oil markets in all six countries.

This study has practical implications for policymakers and investment professionals. A better understanding of dynamic linkages among the markets would help in constructing effective hedging and portfolio diversification strategies. Policy makers can get insight to build proper strategies in order to insulate the economy from factors that cause volatility.

Several studies have investigated the linkage between commodity and stock markets and the volatility spillover effect, but very little attention is given to study the interrelationship between groups of market segments of different economies. No study has comparatively examined the dynamic relationship of multiple markets of a group of emerging countries simultaneously.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war.

Researchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model.

The findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis.

This study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.

This paper investigates the impact of financial stress on the dynamic connectedness and hedging for oil market and stock-bond markets of the Gulf Cooperation Council (GCC).

This study uses the wavelet coherence model to examine the interactions between financial stress, oil and GCC stock and bond markets. Second, the authors apply the time–frequency connectedness developed by Barunik and Krehlik (2018) so as to identify the direction and scale connectedness among these markets. Third, the authors examine the optimal weights, hedge ratio and hedging effectiveness for oil and financial markets based on constant conditional correlation (CCC), dynamic conditional correlation (DCC) and Baba-Engle-Kraft-Kroner (BEKK)-GARCH models.

The authors have found that the correlation between the oil and stock-bond markets tends to be stable in nonshock periods, but it evolves during oil and financial shocks at lower frequencies. Moreover, the authors find that the oil market and financial stress are the main transmitters of risks. The connectedness is mainly driven by the long term, demonstrating that the markets rapidly process the financial stress spillover effect, and the shock is transmitted over the long run. Optimal weights show different patterns for each negative and positive case of the financial stress index. In the negative (positive) financial stress case, investors should have more oil (stocks) than stocks (oil) in their portfolio in order to minimize risk.

This study has gone some way toward enhancing one’s understanding of the time–frequency connectedness between the financial stress, oil and GCC stock-bond markets. Second, it identifies the impact of financial stress into hedging strategies offering important insights for investors aiming at managing and reducing portfolio risk.

This paper aims to investigate the conditional equicorrelation and cross-quantile dependence between the DeFi, European and GCC currency markets (Oman, Qatar, Bahrain, Kuwait, Saudi Arabia and the United Arab Emirates).

This study applies the GARCH-DECO model and cross-quantilogram framework.

The findings reveal evidence of weak and negative average equicorrelations between the examined markets through time, excluding the COVID-19 outbreak and Russia–Ukraine conflict, which is consistent with the literature examining relationships in different markets. From the cross-quantilogram model, the authors note that the dependence between DeFi, EURO and GCC foreign exchange rate markets is greatest in the short run and diminishes over the medium- and long-term horizons, indicating rapid information processing between the markets under consideration, as most innovations are transmitted in the short term.

For the pairs of DeFi and currency markets, the static and dynamic optimal weights and hedging ratios are also estimated, providing new empirical data for portfolio managers and investors.

To the best of the authors’ knowledge, this is one of the most important research looking into the conditional correlation and predictability between the DeFi, EURO and GCC foreign exchange markets. More importantly, this study provides the first empirical proof of the safe-haven, hedging and diversification qualities of DeFi, EURO and GCC currencies, and this work also covers the COVID-19 pandemic and the Russia–Ukraine war with the use of a single dynamic measure produced by the GARCH-DECO model. In addition, the directional predictability between variables under consideration using the cross-quantilogram model is examined, which can be capable of capturing the asymmetry in the quantile dependent structure. The findings are helpful for both policymakers and investors in improving their trading selections and strategies for risk management in different market conditions.

The purpose of this work is to study the connections generated between stock market indices, representing firms whose practices focus on fighting climate change and several global risk factors in accordance with the sustainability objectives defined in the 2030 Agenda. An endogenous perspective is adopted, considering the spillovers generated within the low carbon stock market sector, as well as the latter’s exposure to exogenous shocks of an economic and financial nature.

This work uses a multivariate model of dynamic correlation (GARCH-corrected dynamic conditional correlation [cDCC]), which can accompany the correlations generated over time.

Considering five low carbon indices, representing various parts of the world, and four global macro-economic and financial variables, over a period of approximately eight years, it was possible to understand that the variables studied transmit between each other a statistically significant spillover. The period of the pandemic crisis shows a sharp increase in the information transmission process. It was also possible to conclude that some global variables are risk factors, performing the role of transmission channels for the spillover effects to low carbon indices, increasing the risk of contagion and reducing the possibilities of diversifying the investment portfolio.

Firstly, this work analyses the connection and spillover effects between low carbon indices. Secondly, considers an extended sample covering different market phases, particularly that of the pandemic crisis and the Ukrainian War, creating conditions to compare connection patterns between those indices. Thirdly, it studies the variable influence over time of global risk factors in the transmission of spillover between low carbon indices.

This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.

This study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.

It is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.

To the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

The aim of this study is to examine the nature and determinants of stock market integration between India and other Asia–Pacific countries (Malaysia, Hong Kong, Singapore, South Korea, Japan, China, Indonesia, the Philippines, Thailand and Taiwan) over the period 1991–2021.

Unit root tests, the dynamic conditional correlation-Glosten Jagannathan and Runkle-generalized autoregressive conditional heteroscedasticity (DCC-GJR-GARCH), pooled ordinary least squares (OLS) regression and random effects models are employed for the analysis.

The empirical results show that the DCC between each pair of sample countries is less than 0.5, indicating weak ties between the pairs of sample countries. Also, the DCC between India and other Asia–Pacific stock markets is positive and low, implying low level of integration. The correlation between India and China stock markets is found to be the highest, implying significant level of integration. The main reason for it would be strong economic linkages and bilateral trade relationship between India and China. Moreover, gross domestic product (GDP), interest rate (IR), consumer price index (CPI)-inflation and money supply (MS) differentials are the major driver of stock market integration between India and other Asia–Pacific countries.

The findings of the study have important implications for investors, portfolio managers and policymakers. It is found that the DCC between India and other Asia–Pacific countries (considered in the study) except China is low, which indicates weak ties between the pairs of sample countries. This implies that the Indian stock market provides good investment opportunities for foreign investors. Also, investors and portfolio managers can attain more diversified benefits and can minimize country risk by investing across Asia–Pacific countries. Further, knowledge about the factors that integrate the Indian stock market with the other Asia–Pacific stock markets will help policymakers frame suitable economic and financial stabilization policies.

This study contributes to the extant literature: first, by examining the linkages of Indian stock market with other Asia–Pacific countries; second, although previous studies confirmed the existence of linkages among the various stock markets, few researchers pay attention to the factors driving the process of stock market integration. This study provides additional evidence by examining the significant macroeconomic factors driving the process of such integration in the Asia–Pacific region considered under the study.