Regime-dependent commodity price dynamics: A predictive analysis

Crespo-Cuaresma, Jesús; Fortin, InesORCID: https://orcid.org/0000-0003-4517-455X; Hlouskova, JaroslavaORCID: https://orcid.org/0000-0002-2298-0068 and Obersteiner, Michael (January 2021) Regime-dependent commodity price dynamics: A predictive analysis. IHS Working Paper Series 28, 50 p.

[thumbnail of ihs-working-paper-2021-crespo-cuaresma-fortin-hlouskova-obersteiner-regime-dependent-commodity-price-dynamics.pdf]
Preview
Text
ihs-working-paper-2021-crespo-cuaresma-fortin-hlouskova-obersteiner-regime-dependent-commodity-price-dynamics.pdf - Published Version
Available under License Creative Commons Attribution.

Download (821kB) | Preview
[thumbnail of not available to the public] Other (not available to the public)
ua-wp28.pdf - Other
Restricted to repository staff only

Download (69kB)

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.

Item Type: IHS Series
Keywords: Commodity prices, forecasting, threshold models, forecast performance, states of economy
Funders: FWF Project, 30915-G27
Classification Codes (e.g. JEL): Q02, C53, F47
Research Units: Macroeconomics and Business Cycles
Related URLs:
Date Deposited: 13 Jan 2021 11:55
Last Modified: 13 Dec 2024 07:04
URI: https://irihs.ihs.ac.at/id/eprint/5600

Actions (login required)

View Item
View Item