IDEAS home Printed from https://ideas.repec.org/a/wei/journl/v11y2021i2p170-181.html
   My bibliography  Save this article

Cryptomarket Volatility in Times of COVID-19 Pandemic: Application of GARCH Models

Author

Listed:
  • Mrestyal Khan

    (COMSATS University Islamabad, Islamabad, Pakistan)

  • Maaz Khan

    (Islamabad Policy Research Institute (IPRI), Islamabad, Pakistan, and COMSATS University Islamabad, Islamabad, Pakistan)

Abstract

COVID-19 pandemic has caused significant losses and an increase in the level of risk in the financial markets and global economy. Thus in this study, we model the crypto market volatility behavior during the COVID-19 crisis. GARCH (1, 1) and GJR-GARCH (1, 1) were applied to model the volatility clustering and leverage effects in the intraday day (15-minute interval) returns of Bitcoin, Ethereum, and Litcoin ranging from 11th April 2019 to 8th February 2021. The empirical findings from GARCH (1, 1) model indicates the presence of volatility clustering in the crypto market. Moreover, the results of the GJR-GARCH (1, 1) indicate the presence of leverage effects in the financial returns series of all three crypto currencies. Furthermore, the excess kurtosis confirms the existence of fat-tail phenomena in the crypto market. Overall, the findings from this study showed that in times of COVID 19 pandemic the crypto market returns series showed volatility persistence, fat-tail phenomena, and leverage effects. These outcomes provide a better understanding for financial investors to invest rationally and cautiously during pandemic times.

Suggested Citation

  • Mrestyal Khan & Maaz Khan, 2021. "Cryptomarket Volatility in Times of COVID-19 Pandemic: Application of GARCH Models," Economic Research Guardian, Weissberg Publishing, vol. 11(2), pages 170-181, December.
  • Handle: RePEc:wei:journl:v:11:y:2021:i:2:p:170-181
    as

    Download full text from publisher

    File URL: https://www.ecrg.ro/files/p2021.11(2)1y1.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maaz Khan & Umar Nawaz Kayani & Mrestyal Khan & Khurrum Shahzad Mughal & Mohammad Haseeb, 2023. "COVID-19 Pandemic & Financial Market Volatility; Evidence from GARCH Models," JRFM, MDPI, vol. 16(1), pages 1-20, January.

    More about this item

    Keywords

    COVID-19; GARCH; GJR-GARCH; Volatility; Cryptocurrency;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wei:journl:v:11:y:2021:i:2:p:170-181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mihai Mutascu (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.