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Modeling the Resilience of the Cryptocurrency Market to COVID-19

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Business Information Systems Workshops (BIS 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 444))

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Abstract

This paper is the first to examine the reaction of the cryptocurrency market to COVID-19 in terms of volatility resilience. Seven GARCH-type models are used to measure, predict, and audit the volatility behavior of the most eminent cryptocurrencies that represent almost 60% of the total crypto market namely, Bitcoin (BTC), Ripple (XRP), Litecoin (LTC), Monero (XMR), Dash (DASH), and Dogecoin (DOGE). The in-sample period extends from January 1, 2015 up to November 30, 2019 and the out-of-sample period covers the COVID-19 period spanning from December 1, 2019 up to April 6, 2021. Results showed that CGARCH (1,1) and GARCH (1,1) are the prevailing models to forecast the volatility of Bitcoin and Ripple respectively in both the in- and out-of-sample periods and that advanced GARCH models appear to better predict asymmetries in cryptocurrencies’ volatilities pre and post COVID-19. Also, the COVID-19 contributed in significantly affecting the volatility of Bitcoin, Ripple, Monero and Dash.

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Authors and Affiliations

  1. Notre Dame University, Louaize-72 Zouk Mikayel, Zouk Mosbeh, Lebanon

    Viviane Naimy, Omar Haddad & Rim El Khoury

Authors
  1. Viviane Naimy
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  2. Omar Haddad
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  3. Rim El Khoury
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Corresponding author

Correspondence to Viviane Naimy .

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Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. TIB Leibniz Information Center Science and Technology and University of Hannover, Hannover, Germany

    Sören Auer

  3. Poznań University of Economics and Business, Poznan, Poland

    Milena Stróżyna

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Naimy, V., Haddad, O., El Khoury, R. (2022). Modeling the Resilience of the Cryptocurrency Market to COVID-19. In: Abramowicz, W., Auer, S., Stróżyna, M. (eds) Business Information Systems Workshops. BIS 2021. Lecture Notes in Business Information Processing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-031-04216-4_30

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  • DOI: https://doi.org/10.1007/978-3-031-04216-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04215-7

  • Online ISBN: 978-3-031-04216-4

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