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#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic

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  • Joseph J. French

    (Department of Finance, Monfort College of Business, University of Northern Colorado, Greeley, CO 80639, USA)

Abstract

We investigated the differential impacts of a new Twitter-based Market Uncertainty index (TMU) and variables for Bitcoin before and during the COVID-19 pandemic. Results showed that TMU is a leading indicator of Bitcoin returns only during the pandemic, and the effect of the TMU on Bitcoin’s conditional volatility is significantly greater during the pandemic. Furthermore, during the pandemic, the uncertainty content of people’s tweets is impacted by the highly salient Bitcoin market. Taken together, our results suggest that the information contained in virtual communities such as Twitter have a much larger impact on cryptocurrency markets following COVID-19.

Suggested Citation

  • Joseph J. French, 2021. "#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic," IJFS, MDPI, vol. 9(2), pages 1-7, May.
    • Handle: RePEc:gam:jijfss:v:9:y:2021:i:2:p:28-:d:565261
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    Cited by:

    1. Danai Likitratcharoen & Pan Chudasring & Chakrin Pinmanee & Karawan Wiwattanalamphong, 2023. "The Efficiency of Value-at-Risk Models during Extreme Market Stress in Cryptocurrencies," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    2. Sourav Prasad & Sabyasachi Mohapatra & Molla Ramizur Rahman & Amit Puniyani, 2022. "Investor Sentiment Index: A Systematic Review," IJFS, MDPI, vol. 11(1), pages 1-27, December.
    3. Kingstone Nyakurukwa & Yudhvir Seetharam, 2024. "Twitter policy uncertainty and stock returns in South Africa: Evidence from time‐varying Granger causality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2675-2684, November.
    4. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "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, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.

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