Empirical Calibration of XGBoost Model Hyperparameters Using the Bayesian Optimisation Method: The Case of Bitcoin Volatility

Saralees Nadarajah (), Jules Clement Mba, Ndaohialy Manda Vy Ravonimanantsoa, 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
Ndaohialy Manda Vy Ravonimanantsoa: Ecole Supérieur Polytechnique, Université d’Antananarivo, Antananarivo 101, Madagascar
Patrick Rakotomarolahy: Laboratory of Mathematics and Their Applications, University of Fianarantsoa, Fianarantsoa 301, Madagascar
Henri T. J. E. Ratolojanahary: Laboratory of Mathematics and Their Applications, University of Fianarantsoa, Fianarantsoa 301, Madagascar

JRFM, 2025, vol. 18, issue 9, 1-18

Abstract: Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin.

Keywords: Bayesian optimization; Bitcoin volatility; hyperparameters; XGBoost (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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