Abstract
ABSTRACT This study investigates the role of the probability distribution in forecasting the volatility and value-at-risk (VaR) of cryptocurrency returns using generalized auto-regressive conditional heteroskedasticity (GARCH)-type models. We consider GARCH, EGARCH, GJR-GARCH, TGARCH and Realized GARCH models and show that the role of the probability distribution varies across different situations. A skewed and heavy-tailed distribution contributes to better performance in forecasting the VaR; however, it does not improve the accuracy of volatility forecasting. The results help us to better understand the role of the probability distribution in GARCH-type models.
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