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Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Nonlinear GARCH-Type Models

Author

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  • Huthaifa Alqaralleh
  • Alaa Adden Abuhommous
  • Ahmad Alsaraireh

Abstract

This study is set out to model and forecast the cryptocurrency market by concentrating on several stylized features of cryptocurrencies. The results of this study assert the presence of an inherently nonlinear mean-reverting process, leading to the presence of asymmetry in the considered return series. Consequently, nonlinear GARCH-type models taking into account distributions of innovations that capture skewness, kurtosis and heavy tails constitute excellent tools for modelling returns in cryptocurrencies. Finally, it is found that, given the high volatility dynamics present in all cryptocurrencies, correct forecasting could help investors to assess the unique risk-return characteristics of a cryptocurrency, thus helping them to allocate their capital.

Suggested Citation

  • Huthaifa Alqaralleh & Alaa Adden Abuhommous & Ahmad Alsaraireh, 2020. "Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Nonlinear GARCH-Type Models," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(4), pages 346-356, July.
    • Handle: RePEc:jfr:ijfr11:v:11:y:2020:i:4:p:346-356
    DOI: 10.5430/ijfr.v11n4p346
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    2. Li, Ke & Yuan, Weihong, 2021. "The nexus between industrial growth and electricity consumption in China – New evidence from a quantile-on-quantile approach," Energy, Elsevier, vol. 231(C).
    3. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).

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