Forecasting the volatility of the cryptocurrency market by garch and stochastic volatility

Jong Min Kim, Chulhee Jun, Junyoup Lee

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This study examines the volatility of nine leading cryptocurrencies by market capitalization— Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast models in the complex market of cryptocurrencies.

Original languageEnglish (US)
Article number1614
Issue number14
StatePublished - Jul 8 2021

Bibliographical note

Funding Information:
Funding: No funding for this research.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Bitcoin
  • Cryptocurrencies
  • Stochastic volatility


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