Skip to content
All library documents

How Bitcoin Forks Affect Returns and Volatility

Article arXiv papers · Author: Florentina Şoiman et al.
Read the original

Summary

This study examines how cryptocurrency forking events affect Bitcoin, the parent coin. It uses a modified exponential GARCH model to analyze changes in returns and volatility while accounting for volatility clustering and fat-tailed return distributions. The model also considers market dynamics, although the document does not describe the specific controls or estimation details.

The reported findings are that forks do not significantly affect Bitcoin returns but have a strong positive association with volatility. Volatility remains elevated for the three days after a fork, and simultaneous forks do not add to the volatility impact. These results describe the study’s model-based findings; the supplied summary gives no sample period, event count, uncertainty estimates, or robustness checks, limiting assessment of how broadly they apply.

Key ideas

  • A modified exponential GARCH model assesses Bitcoin’s response to forking events.
  • The model accounts for volatility clustering and fat-tailed return distributions.
  • The study reports no significant return effect but a positive volatility effect around forks.
  • Volatility remains elevated for three days after a fork in the reported findings.
  • Simultaneous forks do not increase the reported volatility impact.

Tags

CryptoVolatilityEvent-drivenStatistics
Full text
# The forking effect


# The forking effect









This study introduces the concept of the forking effect in the cryptocurrency market,specifically focusing on the impact of forking events on bitcoin, also called parent coin.We use a modified exponential GARCH model to examine the bitcoin's response inreturns and volatility. Our findings reveal that forking events do not significantlyaffect the bitcoin's returns but have a strong positive impact on its volatility, especially when considering market dynamics. Our model accounts for key features likevolatility clustering and fat-tailed distributions. Additionally, we observe that following a fork event, volatility remains elevated for the next three days, regardless ofother forking events, and the volatility impact does not increase when multiple forksoccur simultaneously on the same day.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.