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

Saralees Nadarajah (), Jules Clement Mba, Patrick Rakotomarolahy and Henri T. J. E. Ratolojanahary
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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

JRFM, 2025, vol. 18, issue 2, 1-15

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.

Keywords: ANFIS; cryptocurrency; GARCH; GBM; LightGBM; volatility; XGBM (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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