Regime‐dependent commodity price dynamics: A predictive analysis
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| Titel: | Regime‐dependent commodity price dynamics: A predictive analysis |
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| Autoren: | Crespo Cuaresma, Jesus, Fortin, Ines, Hlouskova, Jaroslava, Obersteiner, Michael |
| Weitere Verfasser: | Institut für Höhere Studien (IHS), Wien |
| Quelle: | IHS Working Paper |
| Verlagsinformationen: | Wiley, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | National Economy, commodity prices, Volkswirtschaftstheorie, F47, Economics, Rohstoff, Prognose, income statement, forecasting, Q02, states of economy, ddc:330, Preisbildung, C53, Gewinn- und Verlustrechnung, forecast performance, 1. No poverty, Wirtschaft, threshold models, Commodity prices, raw materials, 8. Economic growth, prognosis, formation of prices |
| Beschreibung: | 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. |
| Publikationsart: | Article Research |
| Dateibeschreibung: | text |
| Sprache: | English |
| ISSN: | 1099-131X 0277-6693 |
| DOI: | 10.1002/for.3152 |
| Zugangs-URL: | https://www.ssoar.info/ssoar/handle/document/71223 http://hdl.handle.net/10419/228666 https://irihs.ihs.ac.at/id/eprint/6982/ https://pure.iiasa.ac.at/id/eprint/19748/ |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....c6528d3ddf36acd06ad21e702d795f9d |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | 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. |
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| ISSN: | 1099131X 02776693 |
| DOI: | 10.1002/for.3152 |