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Re-examining Bitcoin Volatility: A CAViaR-based Approach

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  • 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
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    Citations

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    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. 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.
    4. 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.
    5. 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).

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