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This study examines the short- and medium-run dependence structures across carbon emissions prices, commodity futures, equity prices, and geopolitical uncertainty in the case of the BRICS countries. Previous studies have focused on the co-movement between commodity futures and equity prices, with a few attempts at capturing the economic uncertainty in that relationship but failed to look at the broader context of both emission prices and geopolitical uncertainty. This study employs the spillover index developed by Diebold and Yilmaz and data spaning from January 3, 2008, to October 31, 2021 to identify the dependence structure across ETS, commodity futures, equity prices, and geopolitical uncertainty (GPR). Our key findings suggest that the contribution of the MOEX Russia Index to the natural gas prices return is the highest. As the ETS is concerned, the GPR of China plays the most crucial role compared to other BRICS countries. Comparing energy commodities (e.g. ETS and natural gas), the net pairwise spillover effects of oil to metal commodities are more evident. Among the GPRs of BRICS countries, China, India, Russia, and Brazil have a positive net pairwise directional connectedness transmitted to South Africa. For stakholders working in commodity trading, the insights obtained from this research is essential for developing strategies that lessen the effects of geopolitical risks and help in promoting more resilient and stable commodity markets. [ABSTRACT FROM AUTHOR]

This study examines the co-movement between geopolitical risk (GPR), energy price, and bitcoin (BTC) in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Previous studies have focused on the impact of GPR on the volatility and risk premium of BTC investment. However, very limited studies have focused on integrating BTC as an extension of the mix of GPR on the co-movement with energy price. The analysis is based on monthly data of GPR index for BRICS countries, brent oil futures, natural gas futures and BTCs covering the period between March 2012 and Jun 2021. We employ the Bayesian graphical structural vector autoregressive model and time-varying parameter vector autoregressions-based dynamic connectedness to investigate the network-dependence structure. This research project provides useful empirical evidence for assessing the impact of both BTC and GPR on energy prices. Nonetheless, it will also be informative about the likelihood of co-movements occurring at different stages. [ABSTRACT FROM AUTHOR]