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COVID-19 pandemic and cryptocurrency markets: an empirical analysis from a linear and nonlinear causal relationship

Author

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  • Pradipta Kumar Sahoo

Abstract

Purpose - This paper aims to empirically examine the effect of Coronavirus disease 2019 (COVID-19) pandemic on cryptocurrency market returns with particular attention to top five cryptocurrencies and COVID-19 confirmed and death cases. Design/methodology/approach - The study applies the linear Toda and Yamamoto and nonlinear Diks and Panchenko Granger causality test to know the causal relationship of cryptocurrencies with COVID-19 pandemic. The study also uses the Narayan and Popp endogenous two structural break tests to capture the break period of the sample. Findings - The findings of the study confirm the existence of unidirectional causal relation from COVID-19 confirmed and death cases to cryptocurrency price returns. While examining the break periods, the post-break period result indicates the presence of unidirectional linear causality from COVID-19 confirmed cases to Bitcoin and Ethereum price returns. This shows that prior knowledge of COVID-19 pandemic growth helps to predict the return of cryptocurrencies. Originality/value - The study suggests the investors or crypto lovers to observe the growth of COVID-19 situations during their investment in cryptocurrency markets.

Suggested Citation

  • Pradipta Kumar Sahoo, 2021. "COVID-19 pandemic and cryptocurrency markets: an empirical analysis from a linear and nonlinear causal relationship," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 38(2), pages 454-468, March.
    • Handle: RePEc:eme:sefpps:sef-09-2020-0385
    DOI: 10.1108/SEF-09-2020-0385
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    Citations

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    Cited by:

    1. 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.
    2. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. Mingbo Zheng & Gen-Fu Feng & Xinxin Zhao & Chun-Ping Chang, 2023. "The transaction behavior of cryptocurrency and electricity consumption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-18, December.
    5. Ngo Thai Hung, 2022. "The COVID-19 effects on cryptocurrency markets: robust evidence from time-frequency analysis," Economics Bulletin, AccessEcon, vol. 42(1), pages 109-123.
    6. Marcel C. Minutolo & Werner Kristjanpoller & Prakash Dheeriya, 2022. "Impact of COVID-19 effective reproductive rate on cryptocurrency," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-27, December.
    7. Nitithumbundit, Thanakorn & Chan, Jennifer S.K., 2022. "Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 365-375.
    8. Foroutan, Parisa & Lahmiri, Salim, 2022. "The effect of COVID-19 pandemic on return-volume and return-volatility relationships in cryptocurrency markets," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    9. Zarifhonarvar, Ali, 2022. "The Effect of Covid Pandemic on Cryptocurrency Markets; A Literature Review," EconStor Preprints 266369, ZBW - Leibniz Information Centre for Economics.
    10. Ha, Le Thanh & Nham, Nguyen Thi Hong, 2022. "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, Elsevier, vol. 183(C).

    More about this item

    Keywords

    COVID-19; Cryptocurrency; Nonlinear causality; Structural break; C58; E42; G11;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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