IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v58y2022i5p1320-1338.html
   My bibliography  Save this article

Re-examining Bitcoin Volatility: A CAViaR-based Approach

Author

Listed:
  • Zhenghui Li
  • Hao Dong
  • Christos Floros
  • Athanasios Charemis
  • Pierre Failler

Abstract

The article aims to explore the heterogeneous feature in the determination of Bitcoin volatility using a Markov regime-switching model and test its forecasting ability. The forecasting methodology of the risk measurement of Bitcoin’s returns is based on the Conditional Autoregressive Value at Risk models (CAViaR) approach. Our results show that Bitcoin’s volatility is significantly related to the volatility of the crypto-asset’s return and the main determinants of volatility are speculation, investor attention, market interoperability and the interaction between speculation and market interoperability. In addition, we present evidence that investors’ attention is the main source of volatility. Speculation and the interaction term are related in a “U-shaped” form, whereas investor attention and market interoperability show a linear trend on the volatility of Bitcoin.

Suggested Citation

  • Zhenghui Li & Hao Dong & Christos Floros & Athanasios Charemis & Pierre Failler, 2022. "Re-examining Bitcoin Volatility: A CAViaR-based Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(5), pages 1320-1338, April.
    • Handle: RePEc:mes:emfitr:v:58:y:2022:i:5:p:1320-1338
    DOI: 10.1080/1540496X.2021.1873127
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2021.1873127
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1540496X.2021.1873127?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.

    Citations

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


    Cited by:

    1. Yan Peng & Hanzi Chen & Tinghui Li, 2023. "The Impact of Digital Transformation on ESG: A Case Study of Chinese-Listed Companies," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    2. Yan Ding & Yue Liu & Pierre Failler, 2022. "The Impact of Uncertainties on Crude Oil Prices: Based on a Quantile-on-Quantile Method," Energies, MDPI, vol. 15(10), pages 1-35, May.
    3. Obanya, Praise Otito & Seitshiro, Modisane & Olivier, Carel Petrus & Verster, Tanja, 2024. "A permutation entropy analysis of Bitcoin volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    4. Li, Zhenghui & Mo, Bin & Nie, He, 2023. "Time and frequency dynamic connectedness between cryptocurrencies and financial assets in China," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 46-57.
    5. Kaiming Zhong & Hongyan Fu & Tinghui Li, 2022. "Can the Digital Economy Facilitate Carbon Emissions Decoupling? An Empirical Study Based on Provincial Data in China," IJERPH, MDPI, vol. 19(11), pages 1-25, June.
    6. Jiang, Yonghong & Ao, Zhiming & Mo, Bin, 2023. "The risk spillover between China’s economic policy uncertainty and commodity markets: Evidence from frequency spillover and quantile connectedness approaches," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    7. Wang, Yong & Liu, Shimiao & Abedin, Mohammad Zoynul & Lucey, Brian, 2024. "Volatility spillover and hedging strategies among Chinese carbon, energy, and electricity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).

    More about this item

    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:mes:emfitr:v:58:y:2022:i:5:p:1320-1338. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/MREE20 .

    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.