Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228666
Authors: 
Crespo Cuaresma, Jesús
Fortin, Ines
Hlouskova, Jaroslava
Obersteiner, Michael
Year of Publication: 
2021
Series/Report no.: 
IHS Working Paper No. 28
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 adverse movements and returns implied by a 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, 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.
Subjects: 
Commodity prices
forecasting
threshold models
forecast performance
states of economy
JEL: 
Q02
C53
F47
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Working Paper

Files in This Item:
File
Size
989.94 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.