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COVID-19 vaccine and post-pandemic recovery: Evidence from Bitcoin cross-asset implied volatility spillover

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  • Di, Michael
  • Xu, Ke

Abstract

This paper examines implied volatility spillovers and connectedness between Bitcoin and a broad range of traditional financial assets (U.S. equity market, gold, crude oil, emerging markets and developing markets) from January 8, 2019 to January 20, 2022. Vector Auto-Regression and Generalized Forecast Error Variance Decomposition are used to compare results before COVID-19, during COVID-19 and after the vaccine becomes available. Results indicate higher connectedness during COVID-19 but very low connectedness after the vaccine is available, signaling recovery in financial markets. We also find that Bitcoin is a strong transmitter of volatility during COVID-19.

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  • Di, Michael & Xu, Ke, 2022. "COVID-19 vaccine and post-pandemic recovery: Evidence from Bitcoin cross-asset implied volatility spillover," Finance Research Letters, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:finlet:v:50:y:2022:i:c:s1544612322004731
    DOI: 10.1016/j.frl.2022.103289
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    References listed on IDEAS

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    Cited by:

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    2. Patel, Ritesh & Gubareva, Mariya & Chishti, Muhammad Zubair & Teplova, Tamara, 2024. "Connectedness between healthcare cryptocurrencies and major asset classes: Implications for hedging and investments strategies," International Review of Financial Analysis, Elsevier, vol. 93(C).
    3. Patel, Ritesh & Goodell, John W. & Chishti, Muhammad Zubair, 2023. "Assessing connectedness of transportation cryptocurrencies and transportation stocks: Evidence from wavelet quantile correlation," Finance Research Letters, Elsevier, vol. 58(PC).
    4. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    5. 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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