Forecasting volatility of Bitcoin.

[Display omitted] • We study which models are suitable for forecasting Bitcoin volatility. • We consider many GARCH and two heterogeneous autoregressive (HAR) models. • Realized variance estimated is used as a proxy for true volatility. • EGARCH and APARCH perform best among the GARCH models. • HAR models perform better than GARCH models. Since Bitcoin price is highly volatile, forecasting its volatility is crucial for many applications, such as risk management or hedging. We study which model is the most suitable for forecasting Bitcoin volatility. We consider several GARCH and two heterogeneous autoregressive (HAR) models and compare them. Since we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can draw sharper conclusions than studies which use only daily data. We find that EGARCH and APARCH perform best among the GARCH models. HAR models based on realized variance perform better than GARCH models based on daily data. Superiority of HAR models over GARCH models is strongest for short-term volatility forecasts. [ABSTRACT FROM AUTHOR]