Asymmetric spot‐futures price adjustments in grain markets

• Published date: 01 December 2018
• Date: 01 December 2018
• Author: Alex Maynard,Getu Hailu,Zhige Wu,Alfons Weersink
• DOI: http://doi.org/10.1002/fut.21966

Received: 23 March 2017

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Accepted: 2 September 2018

DOI: 10.1002/fut.21966

RESEARCH ARTICLE

Asymmetric spot‐futures price adjustments

in grain markets

Zhige Wu

1

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Alex Maynard

2,3

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Alfons Weersink

4,5

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Getu Hailu

4

1

Bangor College, Central South Universityof

Forestry and Technology, Changsha, China

2

Department of Economics and Finance,

University of Guelph, Guelph, Ontario,

Canada

3

Faculty of Forestry, University of

Toronto, Toronto, Ontario, Canada

4

Department of Food, Agricultural and

Resource Economics, University of

Guelph, Guelph, Ontario, Canada

5

School of Agricultural and Resource

Economics, The University of Western

Australia, Crawley, Australia

Correspondence

Zhige Wu, Bangor College, Central South

University of Forestry and Technology,

498 Shaoshan Road South, Changsha,

Hunan 410004, China.

Email: z.wu@bangor.ac.uk

Funding information

Ontario Ministry of Agriculture Food and

Rural Affairs (OMAFRA); Central South

University of Forestry & Technology,

Grant/Award Numbers: 2017QZ005,

Social Science Youth Award; Social

Sciences and Humanities Research

Council of Canada, Grant/Award

Numbers: 410‐2010‐0074, 435‐2017‐0582;

China Scholarship Council (CSC)

Abstract

Recent volatility in food prices in the grain market has generated much interest

among agricultural market participants. This study examines the nonlinear

dynamic relationship between spot and futures prices in grain markets. The

empirical results provide strong evidence of price asymmetries. The corn spot

price adjusts faster to futures price increases than futures price decreases,

whereas the soybean spot price adjusts faster to futures price decreases than

futures price increases. Although this asymmetric adjustment is found for a

single market in Ontario, Canada, the results may also provide insights on the

spot‐futures price convergence issues in other commodity markets.

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INTRODUCTION

The relationship between spot and futures prices for a commodity is the foundation for using the futures market to

hedge against price volatility (Shawky, Marathe, & Barrett, 2003). By using the futures market to lock in delivery prices

in advance, farmers, grain elevators, and other market participants can protect themselves from an adverse movement

in spot prices. Essentially, the futures market allows farmers to lock in a price for their product in advance, providing

for more predictable planning and budgeting. The return to such a hedge is sensitive to the spread between the local

spot and futures prices, or the basis. A strengthening (weakening) basis benefits short (long) hedgers seeking to protect

a selling (buying) price through a futures contract. According to the standard no‐arbitrage price theory, the futures and

spot prices must converge at the expiration of the contract. Otherwise, arbitrage opportunities may arise.

J Futures Markets. 2018;38:1549–1564. wileyonlinelibrary.com/journal/fut © 2018 Wiley Periodicals, Inc.

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Temporal changes in the basis at any location should reflect both the cost of carrying grain and local demand and

supply conditions. Increases in the interest rate, and thus the cost of storing grain, increase the local price of grain

relative to the corresponding futures price. This results in a strengthening of the basis (Hailu, Maynard, & Weersink,

2015). Factors, such as increased ethanol capacity, that increase local demand (Gallagher, Wisner, & Brubacker, 2005;

McNew & Griffith, 2005) and poor crop yield that reduces local supply (Adjemian, Garcia, Irwin, & Smith, 2013; Bohi &

Toman, 1987), will strengthen the local basis.

The changes in the basis may also reflect changes in the underlying futures market. The volatility of futures prices

for agricultural commodities rose significantly beginning with the jump in prices for many major crops in the fall of

2006 (Wright, 2011). In addition to higher and more volatile prices, commodity markets have also experienced an

uncoupling of spot and futures prices leading to many situations of nonconvergence at the expiration of the futures

contract (Garcia, Irwin, & Smith, 2015). The nonconvergence leads to concerns about potential bias in the price

discovery process and declines in storage hedging effectiveness (Irwin, Garcia, Good, & Kunda, 2008; Karali, McNew, &

Thurman, 2018). The nonconvergence could be because of an increased participation in commodity markets by

institutional investors. Some argue that the increased participation by institutional investors in commodity futures

markets and the rolling of index funds positions by these investors could have resulted in an increase in commodity

futures prices since 2005 (e.g., Masters, 2008). However, Fattouh, Kilian, and Mahadeva (2013), Irwin, Garcia, Good,

and Kunda (2011), and Hamilton and Wu (2015) did not find empirical support for this argument.

In addition to the issue of nonconvergence at contract expiration, the spread between the spot and futures may also

reflect price discovery issues during the life of the contract (Bekiros & Diks, 2008; Garbade & Silber, 1983; Schroeder &

Goodwin, 1991). For example, some producer groups have claimed that grain merchants are quick to adjust local spot

prices when the futures price drops but slow to transmit increases in the futures price. Grain farmers may not be as

price sensitive during periods of increasing commodity prices, allowing merchants to delay strengthening the local

basis. On the other hand, a rapid increase in the futures price may have prompted grain merchants to accelerate the

local price as a means to obtain product for sale. The direction of the asymmetry may depend on whether the crop is

primarily exported or used within the local area.

Asymmetric price transmission (APT) has been tested primarily within the context of the price movements for crude

oil and gasoline following the seminal rocket‐feather hypothesis of Bacon (1991), which suggests that retail gasoline

prices adjust faster to crude oil price increases than crude oil price decreases. Numerous studies have also tested for an

asymmetric price adjustment between farm and food retail prices (Goodwin & Harper, 2000; Goodwin & Holt, 1999;

Grasso & Manera, 2007; Meyer & von Cramon‐Taubadel, 2004).

The price asymmetry suggested between the prices of crude oil and gas could be due to gasoline retailers taking

advantage of their market power (Honarvar, 2009). Inventory management is another possible reason for price

transmission asymmetry. Production lags and finite inventories imply that negative shocks to the future optimal

consumption path can be accommodated more quickly than positive shocks (Borenstein, Cameron, & Gilbert, 1997).

Balke, Brown, and Yucel (1998) showed that accounting methods such as first‐in‐first‐out can lead to APT. Simioni,

Gonzales, Guillotreau, and Le Grel (2013) argued that asymmetric transmission might stem from transaction costs, such

as transportation expenses. They argue that transaction costs limit the transmission of price shocks below a critical level

because potential gains from trade cannot outweigh these costs. Hence, a perfect price adjustment may not occur

(Meyer, 2004).

To test for the presence of APT, recent studies have carefully considered the time‐series properties of the price data.

As a result, many forms and variants of the cointegration and error correction models (ECMs) have been applied (Frey

& Manera, 2007). Von Cramon‐Taubadel and Fahlbusch (1994) first incorporated cointegration and error correction

techniques into models of APT, thereby allowing for the distinction between positive and negative shocks to the error

correction terms (ECTs; Vavra & Goodwin, 2005). Borenstein et al. (1997) defined regimes of rising and falling prices

and used a threshold of zero on upstream price changes to show that the retail price of gasoline responds more quickly

to increases in crude oil prices than to decreases. Enders and Granger (1998) and Enders and Siklos (2001) extended the

threshold to allow nonzero values. They modified the standard Dickey–Fuller test to form the proposed threshold

autoregressive (TAR) and momentum TAR (M‐TAR) models, in which deviations from long‐run equilibrium will lead

to different price responses depending on whether a specific threshold level is exceeded. The TAR and M‐TAR models

have been widely used in assessing price transmission between the global and domestic markets (Mofya‐Mukuka &

Abdulai, 2013; Subervie, 2011) and price transmission between the producer and retail stages (Abdulai, 2002; Awokuse

& Wang, 2009).

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