Abstract
This paper applies the Hylleberg et al. (J Econom 44(1):215–238, 1990) parametric seasonal unit root test to test for cyclical non-stationarity in commodity prices. The testing procedure is simple and involves evaluating various linear restrictions on lagged price levels in an error correction model of prices, equivalent to the Augmented Dickey–Fuller test. Unit root behaviour at low frequencies implies cyclical non-stationarity. In our empirical application, we fail to reject unit roots at frequencies associated with 3- to 5-year-long price cycles for 7 of 16 major commodities. Our results suggest that longer cycles in many commodity prices are highly stochastic, and care should be taken when interpreting the regularity and out-of-sample predictability of such cycles using historical price movements.
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Notes
El Nino is a weather pattern that strongly influence biomass growth in the Chilean and Peruvian fisheries that makes up about 50 % of the global supply of fishmeal (Asche et al. 2012).
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The authors would like to thank the Norwegian Research Council for financial support.
Appendix: Data description and sources
Appendix: Data description and sources
Commodity | Description and source |
---|---|
Soybeans | World Bank Commodity Price Data, $/mt, (US), c.i.f. Rotterdam |
Corn | World Bank Commodity Price Data, $/mt, (US), no. 2, yellow, f.o.b. US Gulf ports |
Wheat | World Bank Commodity Price Data, $/mt, no. 1, Western Red Spring (CWRS), in store, St. Lawrence, export price |
Rice | World Bank Commodity Price Data, $/mt, 5 % broken, white rice (WR), milled, indicative price based on weekly surveys of export transactions, government standard, f.o.b. Bangkok |
Coffee | World Bank Commodity Price Data, cents/kg, International Coffee Organization indicator price, other mild Arabicas, average New York and Bremen/Hamburg markets, ex-dock |
Sugar | World Bank Commodity Price Data, cents/kg, Sugar (US), nearby futures contract, c.i.f. |
Beef | World Bank Commodity Price Data, cents/kg, beef (Australia/New Zealand), chucks and cow forequarters, frozen boneless, 85 % chemical lean, c.i.f. US port (East Coast) |
Fishmeal | World Bank Commodity Price Data, $/mt, (any origin), 64–65 %, c&f Bremen, estimates based on wholesale price |
Pork | International Monetary Fund, US cents per Pound, 51–52 % (.8–.99 inches of backfat at measuring point) lean Hogs |
Poultry | World Bank Commodity Price Data, cents/kg, (US), broiler/fryer, whole birds, 2-1/2 to 3 pounds, USDA grade “A”, ice-packed, Georgia Dock preliminary weighted average, wholesale |
Lamb | International Monetary Fund, US cents per Pound, New Zealand, PL, frozen, wholesale price at Smithfield Market, London Cts/lb (National Business Review, Auckland, New Zealand) |
Oil | World Bank Commodity Price Data, $/bbl, UK Brent 38 API |
Natural Gas | World Bank Commodity Price Data, $/mmbtu, spot price at Henry Hub, Louisiana |
Copper | World Bank Commodity Price Data, $/mt, (LME), grade A, minimum 99.9935 % purity, cathodes and wire bar shapes, settlement price |
Aluminium | World Bank Commodity Price Data, $/mt, (LME) London Metal Exchange, unalloyed primary ingots, high grade, minimum 99.7 % purity |
Gold | World Bank Commodity Price Data, $/toz, 99.5 % fine, London afternoon fixing, average of daily rates |
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Oglend, A., Asche, F. Cyclical non-stationarity in commodity prices. Empir Econ 51, 1465–1479 (2016). https://doi.org/10.1007/s00181-015-1060-6
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DOI: https://doi.org/10.1007/s00181-015-1060-6