IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v88y2023icp1444-1463.html
   My bibliography  Save this article

Cryptocurrencies are not immune to coronavirus: Evidence from investor fear

Author

Listed:
  • Hoang, Lai T.
  • Baur, Dirk G.

Abstract

This paper examines the effects of fear of coronavirus on returns and volatility of five major cryptocurrencies during the COVID-19 outbreak. Adopting Google search volume on a comprehensive list of coronavirus-related terms to construct a gauge of fear, we show that daily innovations in coronavirus fear are associated with lower prices and higher volatility. The effects are driven by the extreme events and associated googling in March 2020. Out-of-sample tests further show a significant contribution of fear to forecasting next-day returns and volatility. The results indicate that (i) cryptocurrencies (particularly bitcoin) are not a safe haven for investors against the COVID-19 pandemic, and (ii) Google searches contain important information to explain cryptocurrency market movements during times of crisis.

Suggested Citation

  • Hoang, Lai T. & Baur, Dirk G., 2023. "Cryptocurrencies are not immune to coronavirus: Evidence from investor fear," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1444-1463.
  • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:1444-1463
    DOI: 10.1016/j.iref.2023.06.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056023001879
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2023.06.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    2. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    3. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    4. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    5. Dwyer, Gerald P., 2015. "The economics of Bitcoin and similar private digital currencies," Journal of Financial Stability, Elsevier, vol. 17(C), pages 81-91.
    6. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Naeem, Muhammad Abubakr & Husain, Afzol & Bossman, Ahmed & Karim, Sitara, 2024. "Assessing the linkage of energy cryptocurrency with clean and dirty energy markets," Energy Economics, Elsevier, vol. 130(C).
    2. Okorie, David Iheke & Bouri, Elie & Mazur, Mieszko, 2024. "NFTs versus conventional cryptocurrencies: A comparative analysis of market efficiency around COVID-19 and the Russia-Ukraine conflict," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 126-151.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hoang, Lai T. & Baur, Dirk G., 2022. "Loaded for bear: Bitcoin private wallets, exchange reserves and prices," Journal of Banking & Finance, Elsevier, vol. 144(C).
    2. Eross, Andrea & McGroarty, Frank & Urquhart, Andrew & Wolfe, Simon, 2019. "The intraday dynamics of bitcoin," Research in International Business and Finance, Elsevier, vol. 49(C), pages 71-81.
    3. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    4. Mensi, Walid & Sensoy, Ahmet & Aslan, Aylin & Kang, Sang Hoon, 2019. "High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    5. Clark, Ephraim & Lahiani, Amine & Mefteh-Wali, Salma, 2023. "Cryptocurrency return predictability: What is the role of the environment?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    6. 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).
    7. Tzeng, Kae-Yih & Su, Yi-Kai, 2024. "Can U.S. macroeconomic indicators forecast cryptocurrency volatility?," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    8. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    9. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    10. Chen, Jian & Jiang, Fuwei & Liu, Yangshu & Tu, Jun, 2017. "International volatility risk and Chinese stock return predictability," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 183-203.
    11. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    12. Dai, Yingtong & Harris, Richard D.F., 2023. "Average tail risk and aggregate stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    13. Tim Bollerslev & Sophia Zhengzi Li & Viktor Todorov, 2014. "Roughing up Beta: Continuous vs. Discontinuous Betas, and the Cross-Section of Expected Stock Returns," CREATES Research Papers 2014-48, Department of Economics and Business Economics, Aarhus University.
    14. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    15. Chen, Bin-xia & Sun, Yan-lin, 2024. "Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    16. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    17. Chiu, Yen-Chen, 2020. "Macroeconomic uncertainty, information competition, and liquidity," Finance Research Letters, Elsevier, vol. 34(C).
    18. Karstanje, Dennis & Sojli, Elvira & Tham, Wing Wah & van der Wel, Michel, 2013. "Economic valuation of liquidity timing," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5073-5087.
    19. Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    20. Ajithakumari Vijayappan Nair Biju & Ann Susan Thomas, 2023. "Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation," SN Business & Economics, Springer, vol. 3(8), pages 1-21, August.

    More about this item

    Keywords

    Cryptocurrencies; Bitcoin; Coronavirus; Pandemic; Google Trends;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    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:eee:reveco:v:88:y:2023:i:c:p:1444-1463. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    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.