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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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    1. Rossi, Barbara, 2005. "Optimal Tests For Nested Model Selection With Underlying Parameter Instability," Econometric Theory, Cambridge University Press, vol. 21(5), pages 962-990, October.
    2. Shiu-Sheng Chen, 2014. "Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 830-844, April.
    3. Charles Engel & Kenneth D. West, 2005. "Exchange Rates and Fundamentals," Journal of Political Economy, University of Chicago Press, vol. 113(3), pages 485-517, June.
    4. Lardic, Sandrine & Mignon, Valérie, 2008. "Oil prices and economic activity: An asymmetric cointegration approach," Energy Economics, Elsevier, vol. 30(3), pages 847-855, May.
    5. Yu-Chin Chen & Kenneth S. Rogoff & Barbara Rossi, 2010. "Can Exchange Rates Forecast Commodity Prices?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 1145-1194.
    6. Chen, Yu-chin & Turnovsky, Stephen J. & Zivot, Eric, 2014. "Forecasting inflation using commodity price aggregates," Journal of Econometrics, Elsevier, vol. 183(1), pages 117-134.
    7. Barbara Rossi, 2012. "The Changing Relationship Between Commodity Prices and Equity Prices in Commodity Exporting Countries," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(4), pages 533-569, December.
    8. Javier Ordóñez & Hector Sala & José I. Silva, 2011. "Oil Price Shocks and Labor Market Fluctuations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 89-118.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Gubler, Matthias & Hertweck, Matthias S., 2013. "Commodity price shocks and the business cycle: Structural evidence for the U.S," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 324-352.
    11. Lutz Kilian & Clara Vega, 2011. "Do Energy Prices Respond to U.S. Macroeconomic News? A Test of the Hypothesis of Predetermined Energy Prices," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 660-671, May.
    12. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    13. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    14. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    15. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    16. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    17. Barbara Rossi, 2012. "The changing relationship between commodity prices and equity prices in commodity exporting," Economics Working Papers 1405, Department of Economics and Business, Universitat Pompeu Fabra.
    18. Menzie D. Chinn & Olivier Coibion, 2014. "The Predictive Content of Commodity Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(7), pages 607-636, July.
    19. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    20. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    21. Nikolay Gospodinov & Serena Ng, 2013. "Commodity Prices, Convenience Yields, and Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 206-219, March.
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    2. Djeutem, Edouard & Dunbar, Geoffrey R., 2022. "Uncovered return parity: Equity returns and currency returns," Journal of International Money and Finance, Elsevier, vol. 128(C).
    3. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
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    5. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    6. 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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