Main Challenges of Price Volatility in Agricultural Commodity Markets

Abstract

Prices of agricultural commodities undergoing rapid adjustments were in the spotlight following the “food crises” in late 2007 and early 2008, and again more recently in summer and fall of 2010, raising concerns about increased price volatility, whether temporal or structural. Although price volatility is a normal feature of markets given the seasonal production cycle and discontinuity of supply in the face of a continuing demand, a greater uncertainty of a rapidly changing economic and natural environment contributes to and magnifies its occurrence. This chapter focuses on the main challenges of price volatility in agricultural commodity markets. We start by briefly touching upon the theoretical aspects of volatility, followed by a comparison of international and European markets to identify whether one was more affected than the other by increases in price volatility. Factors, implications and preliminary policy considerations of increased volatility follow before initial conclusions on future prospects are drawn.

Appendices

Appendix 1: Description of Price Series Used in Section 2.2

World grains, oilseeds, and meats: compilation of various sources by World Bank Commodity Price Data (Pink Sheet), available at http://go.worldbank.org/2O4NGVQC00

• Barley (Canada), feed, Western No. 1, Winnipeg Commodity Exchange, spot, wholesale farmers’ price

• Wheat (US), no. 2, soft red winter, export price delivered at the US Gulf port for prompt or 30 days shipment

• Maize (US), no. 2, yellow, f.o.b. US Gulf ports

• Wheat (US), no. 1, hard red winter, ordinary protein, export price delivered at the US Gulf port for prompt or 30 days shipment

• Rice (Thailand), 5% broken, white rice (WR), milled, indicative price based on weekly surveys of export transactions, government standard, f.o.b. Bangkok

• Sorghum (US), no. 2 milo yellow, f.o.b. Gulf ports

• Soybeans (US), c.i.f. Rotterdam

• Soybean oil (Any origin), crude, f.o.b. ex-mill Netherlands

• Soybean meal (any origin), Argentine 45/46% extraction, c.i.f. Rotterdam beginning 1990; previously US 44%

• Meat, beef (Australia/New Zealand), chucks and cow forequarters, frozen boneless, 85% chemical lean, c.i.f. U.S. port (East Coast), ex-dock, beginning November 2002; previously cow forequarters

• Meat, chicken (US), broiler/fryer, whole birds, 2-1/2 to 3 pounds, USDA grade “A”, ice-packed, Georgia Dock preliminary weighted average, wholesale

World dairy prices: FAO compilation of average of mid-point of price ranges reported bi-weekly by Dairy Market News (USDA). Available at http://www.fao.org/es/esc/prices/PricesServlet.jsp?lang=en

• Butter, Oceania, indicative export prices, f.o.b.

• Cheddar Cheese, Oceania, indicative export prices, f.o.b.

• Skim Milk Powder, Oceania, indicative export prices, f.o.b.

• Whole Milk Powder, Oceania, indicative export prices, f.o.b.

EU market prices for representative products (monthly) Available at http://ec.europa.eu/agriculture/markets/

Appendix 2: Theoretical Consideration

The CME calculation of historical volatility calculation is the annualised standard deviation of the first difference in the logarithmic values of nearby futures settlement prices. Mathematically, it can be written as

$${\textrm{Volatility}} = {\textrm{STDEV}}_{{\textrm{Day}}1}^{{\textrm{Day}}N}\left( {{\textrm{ln}}\frac{{{\textrm{Settle }}PxT}}{{{\textrm{Settle }}PxT - 1}}} \right)\sqrt {252}$$

where 252 is the estimated number of trade days in a year to convert volatility into annualised terms.