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Cryptocurrencies, Diversification and the COVID-19 Pandemic

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  • David E. Allen

    (School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia
    Department of Finance, Asia University, Taichung 413305, Taiwan
    School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia)

Abstract

This paper features an analysis of cryptocurrencies and the impact of the COVID-19 pandemic on their effectiveness as a portfolio diversification tool and explores the correlations between the continuously compounded returns on Bitcoin, Ethereum and the S&P500 Index using a variety of parametric and non-parametric techniques. These methods include linear standard metrics such as the application of ordinary least squares regression (OLS) and the Pearson, Spearman and Kendall’s tau measures of association. In addition, non-linear, non-parametric measures such as the Generalised Measure of Correlation (GMC) and non-parametric copula estimates are applied. The results across this range of measures are consistent. The metrics suggest that, whilst the shock of the COVID-19 pandemic does not appear to have increased the correlations between the cryptocurrency series, it appears to have increased the correlations between the returns on cryptocurrencies and those on the S&P500 Index. This suggests that investments in cryptocurrencies are not likely to offer key diversification strategies in times of crisis, on the basis of evidence provided by this crisis.

Suggested Citation

  • David E. Allen, 2022. "Cryptocurrencies, Diversification and the COVID-19 Pandemic," JRFM, MDPI, vol. 15(3), pages 1-25, February.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:3:p:103-:d:758104
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    Cited by:

    1. Xu, Lei & Kinkyo, Takuji, 2023. "Hedging effectiveness of bitcoin and gold: Evidence from G7 stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    2. Marcin Wątorek & Jarosław Kwapień & Stanisław Drożdż, 2022. "Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time," Future Internet, MDPI, vol. 14(7), pages 1-15, July.
    3. 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.
    4. Ivan Mužić & Ivan Gržeta, 2022. "Expectations of Macroeconomic News Announcements: Bitcoin vs. Traditional Assets," Risks, MDPI, vol. 10(6), pages 1-15, June.
    5. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2022. "Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time," Papers 2208.01445, arXiv.org.
    6. Hrishikesh Vinod, 2023. "Causality Estimation in Panel Data," Fordham Economics Discussion Paper Series dp2023-09er:dp2023-09, Fordham University, Department of Economics.

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    More about this item

    Keywords

    Bitcoin; Ethereum; copula; kernel estimation; non-parametric; GMC;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G01 - Financial Economics - - General - - - Financial Crises
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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