Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility

Jong-Min Kim, Chulhee Jun and Junyoup Lee
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Jong-Min Kim: Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA
Chulhee Jun: Department of Finance, Bloomsburg University of Pennsylvania, Bloomsburg, PA 17815, USA
Junyoup Lee: School of Business Administration, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea

Mathematics, 2021, vol. 9, issue 14, 1-16

Abstract: 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.

Keywords: cryptocurrencies; Bitcoin; GARCH; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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