Forecasting volatility and value-at-risk for cryptocurrency using GARCH-type models: the role of the probability distribution

Qihao Chen, Zhuo Huang and Fang Liang

Applied Economics Letters, 2024, vol. 31, issue 18, 1907-1914

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.

Date: 2024
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DOI: 10.1080/13504851.2023.2208824

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