City Research Online

Abstract

The aim of this paper is to investigate the performance of machine learning algorithms along with traditional GARCH and GARCH-MIDAS models in forecasting volatility of dry bulk shipping freight rates, known as one of the most volatile asset classes. In doing so, we introduce a new market tightness index, capturing physical constraints in shipping markets as an explanatory variable. The results suggest that significant incremental information can be extracted by Machine Learning algorithms from additional volatility predictors with minimal noise fitting, if regularization is applied. However, traditional GARCH models perform better in capturing the long-term persistence of the volatility. Therefore, a novel hybrid ensemble stacking algorithm that combines GARCH models and tree-based algorithms is proposed. This hybrid model, which utilizes exogenous predictors and the GARCH-MIDAS specification with the marked tightness index, produces accurate and robust out-of-sample volatility forecasts over a range of time horizons, from one day to one month.

Publication Type:	Article
Additional Information:	© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Publisher Keywords:	Volatility forecasting: Machine learning, CatBoost, Random forest, GARCH-MIDAS, Forecast combination: Freight rate, Shipping
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School Bayes Business School > Faculty of Finance
SWORD Depositor:	Symplectic Administrator

Export

Downloads