Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons |
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| Institution: | 1. Graduate School of Economics – EPGE, Getulio Vargas Foundation, Brazil;2. Investor Relations Department, Vale, Brazil;1. Department of Construction and Manufacturing Engineering, University of Oviedo, 33204 Gijón, Spain;2. Mining Exploitation and Prospecting Department, University of Oviedo, 33004 Oviedo, Spain;3. Department of Business Management, University of Oviedo, 33004 Oviedo, Spain;4. Department of Industrial Risk Assessment, Central Mining Institute, Plac Gwarków 1, 40-166 Katowice, Poland;1. Department of Economics, Frostburg State University, Guild Center, 101 Braddock Rd, Frostburg, MD 21532-2303, United States;2. Department of Economics, Frostburg State University, United States;1. Mining Engineering Faculty, Hamedan University of Technology, Hamedan, Iran;2. Technical Faculty, University of Belgrade, Belgrade, Serbia |
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| Abstract: | The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900 to 2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009).We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree.Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts – including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation. |
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| Keywords: | Common cycles Common features Metal commodity prices Forecast combination C3 C5 F44 F47 |
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