Mutual coupling between stock market and cryptocurrencies.

We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.

Mitigating degradation and emissions in China: The role of environmental sustainability, human capital and renewable energy.

China's carbon-embedded growth trajectory is gradually becoming a burden to environmental sustainability, hence, requires much attention. The complexity of human capital attributed emissions coupled with fossil fuel inclined energy utilization for industrialization underscores the failure of China to meet its mitigation target. We developed a policy-driven conceptual tool based on disaggregate energy utilization, human capital, trade, income level and natural resource exploitation in a carbon and environmental degradation function. Using a battery of statistics and econometric techniques such as neural network, SIMPLS, U test, dynamic ARDL Simulations, and Prais-Winsten first-order autoregressive [AR(1)] regression with robust standard errors, we examined the theme based on a data spanning 1961-2016. The study demonstrates that fossil fuel energy consumption and human capital are conducive catalysts for climate change. The instantaneous increase in renewable energy, environmental sustainability and income level has a diminishing effect on emissions and environmental degradation. The environmental Kuznets curve (EKC) hypothesis is validated in both emissions and degradation function - at a turning point of US$ 5469.79 and US$ 5863.70, respectively. The study highlights that the over-dependence on fossil fuel energy and natural resources for economic development, carbon-intensive trade and carbon-embedded human capital, thwart efforts to mitigating climate change and its impacts. Thus, the onus of responsibility for achieving a cleaner environment in China depends majorly on governmental policies that favour or dampens environmental sustainability.