Predicting cryptocurrency crash dates

C. Vladimir Rodríguez-Caballero*, Mauricio Villanueva-Domínguez

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

The nature and novelty of crypto markets have given rise to speculative bubbles, which have permeated almost all cryptocurrencies. This paper shows that the log-periodic model with conditional heteroscedasticity structures has predictive capabilities to estimate the most likely crash date of cryptocurrency bubbles. We use the 2017 bitcoin bubble to perform the primary analysis and date a potential crash just four days before the price peak. We detect the crash date a month before the Bitcoin prices reach their highest value. The bitcoin price fell 30% two weeks after reaching its maximum value. Robustness exercises include the Ether bubble in 2021 and others in Bitcoin’s history to show that the model can be helpful to crypto investors.

Original languageEnglish
JournalEmpirical Economics
Volume63
Issue6
Pages (from-to)2855-2873
Number of pages19
ISSN0377-7332
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Bitcoin
  • Bubbles
  • Crashes
  • Cryptocurrency
  • BITCOIN
  • MODEL

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