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Ensemble Learning and an Adaptive Neuro-Fuzzy Inference System for Cryptocurrency Volatility Forecasting

Author

Listed:
  • Saralees Nadarajah

    (Department of Mathematics, University of Manchester, Manchester M13 9PL, UK)

  • Jules Clement Mba

    (School of Economics, College of Business and Economics, University of Johannesburg, Johannesburg 2092, South Africa)

  • Patrick Rakotomarolahy

    (LaMAF—Laboratory of Mathematics and their Applications, University of Fianarantsoa, Fianarantsoa 301, Madagascar)

  • Henri T. J. E. Ratolojanahary

    (LaMAF—Laboratory of Mathematics and their Applications, University of Fianarantsoa, Fianarantsoa 301, Madagascar)

Abstract

The purpose of this study is to conduct an empirical comparative study of volatility models for three of the most popular cryptocurrencies. We study the volatility of the following cryptocurrencies: Bitcoin, Ethereum, and Litecoin. We consider the GARCH-type, boosting-family-tree-based ensemble learning, and ANFIS volatility models for these financial crypto-assets, which some have claimed capture stylized facts about cryptocurrency volatility well. We conduct comparative studies on in-sample and out-of-sample empirical analyses. The results show that tree-based ensemble learning delivers better forecast accuracy. Nevertheless, the performance of some GARCH-type volatility models is relatively close to that of the best model on both training and evaluation samples.

Suggested Citation

  • Saralees Nadarajah & Jules Clement Mba & Patrick Rakotomarolahy & Henri T. J. E. Ratolojanahary, 2025. "Ensemble Learning and an Adaptive Neuro-Fuzzy Inference System for Cryptocurrency Volatility Forecasting," JRFM, MDPI, vol. 18(2), pages 1-15, January.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:2:p:52-:d:1575689
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    References listed on IDEAS

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    1. da Gama Silva, Paulo Vitor Jordão & Klotzle, Marcelo Cabus & Pinto, Antonio Carlos Figueiredo & Gomes, Leonardo Lima, 2019. "Herding behavior and contagion in the cryptocurrency market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 22(C), pages 41-50.
    2. Ze Shen & Qing Wan & David J. Leatham, 2021. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN," JRFM, MDPI, vol. 14(7), pages 1-18, July.
    3. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    4. Ma, Donglian & Tanizaki, Hisashi, 2019. "The day-of-the-week effect on Bitcoin return and volatility," Research in International Business and Finance, Elsevier, vol. 49(C), pages 127-136.
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