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Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?

Author

Listed:
  • Kejia Yan

    (Department of Accounting, Finance and Economics, Griffith University, Nathan 4111, Australia)

  • Huqin Yan

    (Xiamen National Accounting Institute, Xiamen 361005, China)

  • Rakesh Gupta

    (Department of Accounting, Finance and Economics, Griffith University, Nathan 4111, Australia)

Abstract

This paper examines the dynamic conditional correlations among 10 cryptocurrencies and the possibility of hedging investment strategies among multiple cryptocurrencies over the period affected by COVID-19 from 2017 to 2022. After studying the relationship between Bitcoin, Ethereum, and the other eight cryptocurrencies, four main results were obtained in this paper: first, from the pre-COVID-19 period to the COVID-19 period, almost all of the cryptocurrencies’ return growth rates increased, and COVID-19 had a positive effect on the returns of cryptocurrencies. Second, all of the cryptocurrencies’ return indices had features of volatility clustering and memory persistence in the long run; from pre-COVID-19 to COVID-19, these cryptocurrencies’ GARCH values decreased, but the correlations among the varying GARCH values increased. Third, the varying correlations between the return indices of Bitcoin, Ethereum, and the other cryptocurrencies were very strong; from pre-COVID-19 to COVID-19, the average dynamic correlations between Bitcoin and the others increased. Fourth, Tether can be used as a hedge cryptocurrency against the other cryptocurrencies as COVID-19 enhanced its hedging feature.

Suggested Citation

  • Kejia Yan & Huqin Yan & Rakesh Gupta, 2022. "Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?," JRFM, MDPI, vol. 15(3), pages 1-25, March.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:3:p:113-:d:762024
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    References listed on IDEAS

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    1. Ciaian, Pavel & Rajcaniova, Miroslava & Kancs, d'Artis, 2018. "Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 173-195.
    2. Caporale, Guglielmo Maria & Zekokh, Timur, 2019. "Modelling volatility of cryptocurrencies using Markov-Switching GARCH models," Research in International Business and Finance, Elsevier, vol. 48(C), pages 143-155.
    3. Będowska-Sójka, Barbara & Kliber, Agata, 2019. "The causality between liquidity and volatility in the Polish stock market," Finance Research Letters, Elsevier, vol. 30(C), pages 110-115.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    5. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    6. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    7. Huynh, Toan Luu Duc & Nasir, Muhammad Ali & Vo, Xuan Vinh & Nguyen, Thong Trung, 2020. "“Small things matter most”: The spillover effects in the cryptocurrency market and gold as a silver bullet," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    8. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    9. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Larkin, Charles & Oxley, Les, 2020. "Any port in a storm: Cryptocurrency safe-havens during the COVID-19 pandemic," Economics Letters, Elsevier, vol. 194(C).
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    Cited by:

    1. Julien Chevallier, 2023. "‘Safe Assets’ during COVID-19: A Portfolio Management Perspective," Commodities, MDPI, vol. 2(1), pages 1-39, January.
    2. Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis," JRFM, MDPI, vol. 16(1), pages 1-17, January.
    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.

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