Regime‐dependent commodity price dynamics: A predictive analysis

Jesus Crespo Cuaresma, Ines Fortin, Jaroslava Hlouskova and Michael Obersteiner

Journal of Forecasting, 2024, vol. 43, issue 7, 2822-2847

Abstract: We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime‐dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict turning points, and the returns implied by a simple trading strategy. Our analysis provides overwhelming evidence that allowing for regime‐dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub‐indices (energy, industrial metals, precious metals, agriculture, and livestock). Our results suggest the existence of a trade‐off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/for.3152

Related works:
Working Paper: Regime-dependent commodity price dynamics: A predictive analysis (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:43:y:2024:i:7:p:2822-2847

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().