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

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.

Automated summary

In handling extremely volatile financial data such as cryptocurrencies, the SV model outperforms the GARCH family models, with forecasting errors becoming more accurate as the forecast time horizon increases. This study provides deeper insights into volatility forecast models in the complex cryptocurrency market.