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Commodity prices and related equity prices

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  • Shiu‐Sheng Chen

Abstract

This paper shows that commodity‐sensitive stock price indices have strong power in predicting nominal and real commodity prices at short horizons (one‐month‐ahead predictions) using both in‐ and out‐of‐sample tests. The forecasts based on commodity‐sensitive stock price indices are able to significantly outperform naïve no‐change forecasts. For example, the one‐month‐ahead forecasts for nominal commodity prices reduce the mean squared prediction error by between 1.5% (for natural gas prices) and 20% (for copper prices). Moreover, the one‐month‐ahead directional forecast is found to perform significantly better than a 50:50 coin toss. As stock prices are not subject to revision, the proposed variable, which reflects timely and readily available market information, can potentially be a valuable predictor and thereby help to improve the accuracy of commodity price forecasts. Prix des biens et prix des actifs boursiers qui y sont reliés. Ce texte montre que les indices de prix des actifs sensibles aux prix des biens ont une grande puissance de prévision des prix nominaux et réels des biens pour des horizons temporels courts (prévisions un mois d'avance) en utilisant à la fois des tests dans et hors de l'échantillon. Ces prévisions performent mieux et de façon significative que les prévisions naïves de non changement. Par exemple, les prévisions un mois d'avance des prix nominaux des biens réduisent l'écart quadratique moyen de prédiction d'un ordre de 1.5% (pour les prix du gaz naturel) et de 20% (pour le prix du cuivre). De plus, les prévisions un mois d'avance de la direction du changement de prix performent mieux et de manière significative que de faire tourner une pièce de monnaie bien balancée 50‐50. Comme les prix des actifs boursiers ne sont pas sujets à révision, la variable proposée qui reflète en temps opportun l'information sur le marché et est aisément disponible peut s'avérer potentiellement un prédicteur de grande valeur et donc aider à améliorer la précision des prévisions du prix des biens.

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  • Shiu‐Sheng Chen, 2016. "Commodity prices and related equity prices," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(3), pages 949-967, August.
    • Handle: RePEc:wly:canjec:v:49:y:2016:i:3:p:949-967
    DOI: 10.1111/caje.12220
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    2. Schischke, A. & Papenfuß, P. & Brem, M. & Kurz, P. & Rathgeber, A.W., 2023. "Sustainable energy transition and its demand for scarce resources: Insights into the German Energiewende through a new risk assessment framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    3. Djeutem, Edouard & Dunbar, Geoffrey R., 2022. "Uncovered return parity: Equity returns and currency returns," Journal of International Money and Finance, Elsevier, vol. 128(C).
    4. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    5. Wang, Qiao & Balvers, Ronald, 2021. "Determinants and predictability of commodity producer returns," Journal of Banking & Finance, Elsevier, vol. 133(C).

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    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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