ArticleTechnological forecasting and social change2021
The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies.
Article in Technological forecasting and social change, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Price prediction of PFP NFT based on the sentiments of users in posts on social media.Scientific reports · 2025Article
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- Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI.Expert systems with applications · 2023Article
- Role of fiscal and monetary policies for economic recovery in China.Economic analysis and policy · 2023Article
- Connectedness between Defi assets and equity markets during COVID-19: A sector analysis.Technological forecasting and social change · 2023Article
- The impact of the COVID-19 outbreak on the connectedness of the BRICS's term structure.Humanities & social sciences communications · 2023Article
- Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic.Research in international business and finance · 2023Article
- Examining the hedge performance of US dollar, VIX, and gold during the coronavirus pandemic: Is US dollar a better hedge asset?PloS one · 2023Article
- Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality.Financial innovation · 2023Article
- Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach.Resources policy · 2022Article
- Modelling the asymmetric effect of COVID-19 on REIT returns: A quantile-on-quantile regression analysis.Journal of economic asymmetries · 2022Article
- The Return and Volatility Connectedness of NFT Segments and Media Coverage: Fresh Evidence Based on News About the COVID-19 Pandemic.Finance research letters · 2022Article
- An application of a TVP-VAR extended joint connected approach to explore connectedness between WTI crude oil, gold, stock and cryptocurrencies during the COVID-19 health crisis.Technological forecasting and social change · 2022Article
- Social-economic impacts of epidemic diseases.Technological forecasting and social change · 2022Article
- How does the crisis of the COVID-19 pandemic affect the interactions between the stock, oil, gold, currency, and cryptocurrency markets?Frontiers in public health · 2022Article
- Impact of COVID-19 effective reproductive rate on cryptocurrency.Financial innovation · 2022Article
- Cryptocurrencies and oil price shocks: A NARDL analysis in the COVID-19 pandemic.Resources policy · 2021Article
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3 authors.
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Abstract
This research explores the impact of COVID-19-related media coverage on the dynamic return and volatility connectedness of the three dominant cryptocurrencies (Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP)) and the fiat currencies of the euro, GBP and Chinese yuan. The sample period covers the first and second devasting waves of the COVID-19 pandemic crisis and ranges from January 1, 2020, to December 31, 2020. The dynamic return and volatility connectedness measures are estimated using the time varying parameter-VAR approach. Our return connectedness analysis shows that the media coverage index (only before the first wave) and the cryptocurrencies are the net transmitters of shocks while the fiat currencies are the net receivers of shocks. Similar results are obtained in terms of volatility, except for the euro, which shows a clear net receiver profile in January and February. This fiat currency (the euro) became a net transmitter in March and during the first wave of the COVID-19 crisis, which possibly shows the virulence of the pandemic on the European continent. Moreover, the most relevant differences between the net dynamic (return and volatility) connectedness of these two groups of currencies are focused on the beginning of the sample period, just before the first wave of the SARS-CoV-2 pandemic crisis, although some differences are observed during the first and second waves of the coronavirus outbreak.
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