ABSTRACT
This paper explores the asymmetric effect of COVID-19 pandemic news, as measured by the coronavirus indices (Panic, Hype, Fake News, Sentiment, Infodemic, and Media Coverage), on the cryptocurrency market. Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heteroskedasticity model, the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns, indicating fear of missing out behavior does not prevail. Furthermore, when the nonlinear autoregressive distributed lag model is estimated, both positive and negative shocks in pandemic indices promote Bitcoin's daily changes; thus, Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil. The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns, whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index. These findings may have significant policy implications for investors and governments because they highlight the importance of news during turbulent times. The empirical results indicate that pandemic news could significantly influence Bitcoin's price.
ABSTRACT
Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people’s everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China’s carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper.
ABSTRACT
This study investigated time-frequency transmission and connectedness among green indexes dealing with clean energy, environmental preservation, and technological innovation and information uncertainty related to economics news, the COVID-19 pandemic, and Twitter usage. First, by employing a quantile vector autoregression framework, we assessed how the static and dynamic connectedness between markets switched across a broad spectrum of market conditions, particularly bear, normal, and bull markets. Second, we examined the dynamics of the co-movement between green financial markets and the level of uncertainty in the time-frequency domain using novel vector wavelet coherence analysis. Our analysis yielded the following major findings: Statically, high spillover and volatility effects existed among the indexes;dynamically, evidence of very strong connectedness between climate change indexes was reported at extreme lower and extreme upper quantiles. The findings further exhibit the switching of climate change between net contributing/net receiving shock behavior during the pandemic. Technological innovation, the COVID-19 pandemic, and uncertainty have strong effects on climate change markets as revealed by multiple, quadruple, and vector wavelet analysis. Implications for both environmental investors and policymakers were revealed.