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This paper examines the volatility spillover between the soybean futures contracts traded in the US Chicago Board of Trade (CBOT) and China Dalian Commodity Exchange (DCE) through a normalized Copula – GARCH(1,1) - t model with structural changes. The structural change points are identified through a combination of Bayesian diagnosis with Z-test. The study finds that the volatility spillover exists between the DCE and CBOT soybean futures and weakens through time. We further identify seven structural change points in the volatility spillover relationship, suggesting it is going through significant structural changes. The changes are related to major social-political events including the trade conflict between China and the US, the COVID-19 pandemic and the Russia-Ukraine war. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic has already disrupted large swathes of the global economy, threatening the growth models of major oil and gas producer countries like Russia. A collapse in oil prices, triggered by the pandemic, once again exposed Russia's enduring susceptibility to sharp falls in oil and gas prices. We argue that this decline in oil prices came at a time when tectonic shifts in global energy markets had already threatened to reduce the value of future oil and gas revenues. These structural changes were accelerated by the collapse in demand caused by the pandemic. Because the Russian leadership had done little to address the threat posed by the emergence of a new energy order, the economy remained highly dependent on oil and gas sales as the primary source of growth. While measures undertaken since 2014 to reduce the government's fiscal dependence on high oil prices will help Russia avoid a worst-case outcome of a fiscal or financial crisis, the dependence of other branches of the economy on the energy sector remains high. This has left Russia set once again to experience a deeper recession than that forecast for other energy exporting economies. [ABSTRACT FROM AUTHOR]

This article investigates the asymmetric and long memory volatility properties and dynamic conditional correlations (DCCs) between Brazilian, Russian, Indian, Chinese, and South African (BRICS) stock markets and commodity (gold and oil) futures markets, using the trivariate DCC-fractionally integrated asymmetric power autoregressive conditional heteroskedasticity (FIAPARCH) model. We identify significant asymmetric and long memory volatility properties and DCCs for pairs of BRICS stock and commodity markets, and variability in DCCs and Markov Switching regimes during economic and financial crises. Finally, we analyze optimal portfolio weights and time-varying hedge ratios, demonstrating the importance of overweighting optimal portfolios between BRICS stock and commodity assets. [ABSTRACT FROM AUTHOR]