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

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  • Qihao Chen
  • Zhuo Huang
  • Fang Liang
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.

Suggested Citation

  • Qihao Chen & Zhuo Huang & Fang Liang, 2024. "Forecasting volatility and value-at-risk for cryptocurrency using GARCH-type models: the role of the probability distribution," Applied Economics Letters, Taylor & Francis Journals, vol. 31(18), pages 1907-1914, October.
    • Handle: RePEc:taf:apeclt:v:31:y:2024:i:18:p:1907-1914
    DOI: 10.1080/13504851.2023.2208824
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