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Cyclical non-stationarity in commodity prices

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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

  1. 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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Author information

Authors and Affiliations

  1. Department of Industrial Economics, University of Stavanger, 4036, Stavanger, Norway

    Atle Oglend & Frank Asche

  2. Institute for Sustainable Food Systems and School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA

    Frank Asche

Authors
  1. Atle Oglend
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  2. Frank Asche
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Corresponding author

Correspondence to Atle Oglend.

Additional information

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

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