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The ripple effects of CBDC-related news on Bitcoin returns: Insights from the DCC-GARCH model

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  • Akin, Isik
  • Khan, Muhammad Zubair
  • Hameed, Affan
  • Chebbi, Kaouthar
  • Satiroglu, Hakan

Abstract

Central bank digital currencies (CBDCs) have emerged as a potential substitute for current payment methods, and, as such, major announcements, events and policy discussions regarding CBDCs have the potential to influence cryptocurrency returns. In light of this, the present study undertakes an in-depth analysis of the CoinMarketCap data between August 1, 2017 and April 1, 2022 by implementing the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model. The study reveals a noteworthy influence of news and events related to CBDCs on Bitcoin returns. Precisely, CBDC uncertainty index and CBDC attention index have resulted in significant fluctuations in Bitcoin returns, indicating that positive news can result in significant Bitcoin returns. The findings suggest that future expectations of investors regarding cryptocurrencies are shaped by CBDC-related news and events.

Suggested Citation

  • Akin, Isik & Khan, Muhammad Zubair & Hameed, Affan & Chebbi, Kaouthar & Satiroglu, Hakan, 2023. "The ripple effects of CBDC-related news on Bitcoin returns: Insights from the DCC-GARCH model," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001861
    DOI: 10.1016/j.ribaf.2023.102060
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    1. Hoque, Mohammad Enamul & Billah, Mabruk & Alam, Md Rafayet & Lucey, Brian, 2024. "Does news related to digital economy and central bank digital currency affect digital economy ETFs? Evidence from TVP-VAR connectedness and wavelet local multiple correlation analyses," Global Finance Journal, Elsevier, vol. 61(C).

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