A Roller Coaster Ride:
an empirical investigation of the main
drivers of wheat price
1 February 2013
Background
Food commodity prices have increased rapidly and wheat prices
in particular have registered marked upsurges interrupted only
briefly by the global financial crisis
P hikes
detrimental
↑ poverty
(IMF, 2011; von Braun &
Tadasse, 2012)
↓ economic growth
(Jacks et al. 2011)
threaten political stability
(Zawojaska, 2009;)
Movements in commodity prices matter for countries “external” and
“internal” balances and price increase is far greater in the poor periphery
(core-periphery asymmetry hypothesis, Poelhekke and van der Ploeg, 2007)
Contents
I.
Objective and research features
II.
Price dynamics
III.
Literature review
IV.
Theoretical framework and model set up
V.
Analysis of results
VI.
Main conclusions
Objective & Research Features
Shed light on the main drivers of wheat prices ;
Period going from 1980:1-2012:1 and the sub-period 1995:1-2012:1.
Monthly data are used.
Sources: Datastream, Chicago Board of Trade, USDA and Bloomberg
dataset
Price dynamics
Monthly price indices for major grains and oilseeds …..
330
280
230
180
130
80
ott-09
ott-07
ott-05
ott-03
ott-01
ott-99
ott-97
ott-95
ott-93
ott-91
ott-89
ott-87
ott-85
ott-83
ott-81
ott-79
ott-77
ott-75
ott-73
ott-71
30
US MARKET PRICE INDEX - SOYBEAN MEAL, US (ROTTERDAM), CIF NADJ
US MARKET PRICE INDEX - SOYBEAN OIL, US (ROTTERDAM), FOB NADJ
US MARKET PRICE INDEX - WHEAT U.S.GULF PORTS NADJ
US MARKET PRICE INDEX - MAIZE US GULF PORTS NADJ
MY MARKET PRICE INDEX-PALM KERNAL OIL, MALAYSIA (ROTTERDAM) NADJ
CN MARKET PRICE INDEX: BARLEY CANADA (WINNEPEG) NADJ
TH MARKET PRICE INDEX - RICE (BANGKOK) NADJ
Striking aspects:
Periodic spikes in the prices during the last 40 years ;
the size of price surges during 2006-2008 was impressive (“2008” spike);
Price hikes-falls across commodities occurred simultaneously (tendency to co-move)
Literature Review
Vivacious discussion regarding the causes of commodity prices ups
and downs.
Three strands:
• the “fundamentalist” view;
• the “broad” macro view;
• the “financialisation” view.
Fundamentalist view
The price of any good is driven by D and S in the absence of “irrational exuberance.”
(Krugman, 2011; Yellen, 2011; Irwin and Sanders, 2010)
- Shocks to S and + shocks to D => ↑ price
adverse weather conditions
- S shocks to agricultural commodities are determined:
collapses in stock-to-use ratios
Extreme weather conditions
Stock-to-use is ↓
greater yield Δ with likely damage to existing
cropping areas and consequent p changes.
mkt is less prone to cope with supply drops or
demand excesses => prices skyrocket.
Stock levels have declined by 3.4% per year since mid-1990s, and the highest prices were
registered during a period in which stock-to-use were at historical lows (FAO, 2009).
+ D shocks => process of income caught-up between developing and advanced countries.
More than 90% of the augmented demand for agricultural commodities has originated
from developing countries, mainly from India and China (Fawley and Juvenal 2011)
Broad Macro-view
Other macro-determinants affect p levels and their fluctuations via D or S channels.
Exchange rates can influence commodity prices via international purchasing power and
the effects on margins for producers with non-US dollar costs (Roache, 2010; Mussa,
1986; Gilbert, 1989). A Δ in $ exchange rate conditions prices measured in $.
Monetary policies can impact on a number of demand and supply channels (Frankel,
2008; Calvo, 2008; Bakucs et al., 2009),
when changes in interest rates are frequent
↑p volatility
when interest rates are low
↑p since there is an incentive to
hoard physical commodities as an
investment vehicle
Inflation is a common factor driving prices of different commodities.
Broad Macro-view
Oil prices have been mentioned as an additional shock to food price via supply and
demand channels (Mercer-Blackman et al. 2007, Thompson et al. 2009). This
because a ↑ in oil price leads to an ↑ in input costs and ↑ demand for grains as
biofuels with a consequent ↑ in food commodity prices.
“Thinness” of markets affects commodity price movements. It does this because in
thinner markets, where domestic prices do not follow the international market, world
market prices have to vary more to accommodate an external shock to traded
quantities (OECD, 2008).
Financialisation view
Commodity prices have been exuberant and divorced from market fundamentals.
“Financialisation” of commodity market & speculation are main culprits of commodity
p volatility (Gilbert & Morgan 2011; Hamilton, 2009; Masters, 2008; Stewart, 2008).
The “financialisation” refers to ↑ flows of capital into the commodity market long-only
commodity index fund. Index fund investments ↑ from $90 billion in 2006 to $200
billion in 2007 (Barclay).
Speculation involves buying, holding, selling of stocks, bonds, commodities, or any
valuable financial instrument to profit from fluctuations in its price as opposed to
buying it for use or for dividend or interest income (Robles, Torero, von Braun, 2009).
Theoretical framework
An VECM model used to estimate the p equation for wheat
The VECM captures long-run effects of p dynamics and isolate
different sources of p fluctuations.
Approach to model identification and estimation: first a VAR system of
variables is constructed to test if p are cointegrated with specific
market variables, macroeconomic variables and financial factors. The
proper model is identified through Johansen tests, the Maximum
Likelihood is then adopted to estimate the time variable parameters in
the regression.
Model set up for wheat p
p
Financial variables
Mkt specific variables
Broad macro
stock to use
Thinness of Mkt
Weather conditions
Financialisation
Speculation
Global economic
activity
Interest rate
Oil spot price
Exchange rate
Inflation
1980:1 – 2012:1
Market specific variables
Stocks are used to:
stock to use
(-)
a) ↓ costs of adjusting
production over time in
response to ∆ in D
when stock ↓ price ↑
b) ↓ marketing costs by
facilitating timely deliveries
and prevent stock-outs.
Thinness of w Mkt
 EX w + IM w 

TH ≡ 
 Consw 
(-)/(+)
describes to which extent agriproducts
are
internationally
traded.
↓ ratio => thinness => ↑
volatility (illiquid)
e.g. Rice mkt => only
5-7% of its production
is exported
↑ ratio => fatness => ↑
liquid
Data Source: USDA
Broad macro variables
Interest rate
Federal Funds deflated CPI
Stance of monetary policy
(-)
US i spread = 10 years Treasury bond –
federal funds (yield curve)
(-)/(+)
Financial conditions
(leading indicator)
Two mechanisms of impact on commodity prices:
1) An ↑ in i ↓ inventory demand (due to ↑ the cost of carrying inventories) => ↑ p
2) Relates to financial speculation in commodity markets.
Commodities can be thought also as a financial asset, thus when i are ↓, investors are more prone to take open positions
in the financial mkt for commodities, and this pushes p ↑
Conversely, an ↑ in i encourages speculators to shift from spot commodity contracts to T-bond, and this ↓ commodity p
If the presence of risk-premiums in T-bond markets represents rewards to investors for exposure to economy-wide
macroeconomic risks => a strong + linkage between ∆ in commodity p and measures of risk in T-bond mkts.
⇒↑ yield spreads (i.e. ↓ risk tolerance in the T-bond mkt) are correlated with ↑ commodity p, which indicate an ↑ risk
tolerance in the commodity mkt
This pattern => that the asset classes are being treated as substitutes in diversified portfolios.
If risk aversion is expressed in similar ways across T and commodity mkts => ↑ T-yields are correlated with ↓ p
This pattern => the asset classes are being treated as complements in diversified portfolios.
Broad macro variables
Global economic
activity
(+)
Oil Spot price
(+)
REX
Kilian (2009) => index of dry
cargo single voyage freight rates
susceptible to sector-specific shocks e.g. ∆ in insurance premiums
West Texas Intermediate Spot
deflated
$ effective exchange rate
(-)/(+)
Inflation
(+)
Industrial prod index
↑ cost of processing,
transportation & distribution
↑ biofuel prod
Trade in agri commodities is
denominated in US$
movements in REX affect
the commodit p as perceived
by all countries outside US
Commodities are considered as
store of wealth their demand as
financial assets or stocks ↑ with π
Data Source: Datastream
Financial variable
The excessive speculation index by Working:


NC OI Short
ESPI = 1 +
 ⋅ 100
 (C OI Short + C OI Long ) 
if
C OI Short ≥ C OI Long


NC OI Long
ESPI = 1 +
 ⋅ 100
 (C OI Short + C OI Long ) 
if
C OI Short  C OI Long
This metrics assesses the relative importance of speculative positions with respect to hedging
positions. The level of speculation is meaningful only in comparison with the level of hedging in
the mkt.
Speculation could have positive or negative effects on commodity markets:
It could stabilise mkt (Friedmann, 1953)
=>
by buying low and selling high so to
bring prices closer to fundamentals
=>
Rational speculators finish setting p
trends and leading short term p
away from fundamentals by
anticipating the buy sell orders of
trend followers.
(-)
destabilise mkt (De Long et al., 1990)
(+)
Data Source: Datastream & Bloomberg
Global weather variables
The sea surface
temperature
anomalies (SST)
The Southern
Oscillation Index
anomalies (SOI)
measures the deviations
between the sea surface
temperatures in the El Niño
region 3.4 and its historical
average
measures the fluctuations in
air pressure between the W
and E tropical Pacific during
El Niño and La Niña
episodes. It is a standardised
index based on the observed
sea
level
pressure
differences between Tahiti &
Darwin, Australia.
Although the events arise in the
Pacific Ocean, they have strong
effects on the world’s weather
and an important influence on
world’s production and price of
primary non-oil commodities
(Brunner, 2002)
Data Source: National Climatic Data Center
Weather conditions
SST
+ anomalies (>0) are related to abnormally warm ocean waters across the eastern
tropical Pacific typical of El Niño event
- anomalies are related to cool phase typical of El Niña episode.
SOI
+ values coincide with La Niña events when water becomes cooler than normal and
vice-versa.
La Niña is associated with ↑ droughts throughout the mid-latitudes, where much of wheat, corn and
soybeans are produced => ↓ their yield (Hurtado and Berri, 1998) => ↑ prices.
La Niña has historically been associated with global food crises.
El Niño is associated with an ↑ likelihood of droughts in tropical land areas, mainly affecting crops such
6
as sugar and palm oil.
4
2
-SST
0
-2
+SOI
-4
-6
-8
1985
1990
1995
SST
2000
SOI
2005
2010
↑p
L_REAL_P_INDEX
L_REAL_POIL
6.0
5.0
5.6
4.5
5.2
4.0
4.8
3.5
4.4
3.0
4.0
2.5
1985
1990
1995
2000
2005
1985
2010
1990
L_END_STOCK_TO_USE
1995
2000
2005
2010
2005
2010
L_REX_CPI
3.6
5.0
4.9
3.4
4.8
3.2
4.7
3.0
4.6
2.8
4.5
4.4
2.6
1985
1990
1995
2000
2005
2010
1985
1990
1995
2000
Analysis of variables
Census- X12
Seasonal adjustment
Augmented Dickey-Fuller (ADF)
Stationary properties
Phillips-Perron (PP)
ADF
PP
Levels
1° diff.
levels
1° diff.
lp
Prob.
0.8029
Prob.
0.0004
Prob.
0.7564
Prob.
0.0000
l poil
Prob.
0.5946
Prob.
0.0000
Prob.
0.3672
Prob.
0.0000
li
I(1)
I(0)
I(1)
I(0)
l rex
I(1)
I(0)
I(1)
I(0)
Lag lenght
Schwaiz Bayesian Criterion/
Newey West Bandwidth
Estimation Results
The basic results can be expressed in the same way as the classical regression…
Cointegrating vector β
1981:1-2012:1
1995:1-2012:1
0.231 (4.44)
0.294 (2.84)
ln real fed funds
-0.132 (-2.55)
-0.207 (-6.03)
ln rex
-0.771 (-3.12)
-3.629 (-9.77)
ln end-stock-to-use
-0.999 (-3.94)
-0.436 (-1.99)
sst
0.244 (3.50)
0.248 (4.54)
soi
0.166 (5.71)
0.104 (4.26)
ln world ind prod
3.29 (2.80)
1.807 (2.63)
us fed spread
0.045 (1.99)
0.021 (1.09)
-1.008 (-2.56)
0.340 (1.42)
ln real poil
ln thinness
ln speculation
trend
0.715 (7.14)
0.006 (3.51)
0.006 (3.01)
-0.069 (-4.87)
-0.085 (-2.07)
Speed of adjustment α
dln real price index
Regressand: ln real wheat price index. t stat in brakets.
Results
Real wheat p is cointegrated with market specific, broad economic variables, weather
events, and speculation.
Cointegrating vector β
ln real poil
ln rex
−poil => ↑ wheat p,
1981:1-2012:1
1995:1-2012:1
0.231 (4.44)
0.294 (2.84)
-0.771 (-3.12)
-3.629 (9.77)
A poil ↑ => upward pressure on input costs such as
fertilizers, irrigation, and transportation => a ↓ in
profitability & production, with a shift of S curve to the
left, and a ↑ in wheat p.
A poil ↑ => a higher derived D for wheat and other
grain, such as corn or soybeans to be destined to biofuels
production and has resulted in ↑ p of these grains
(Krugman, 2008).
This result testifies that energy and agricultural prices have become increasingly
interwoven. In line with Tang and Xiong (2010) and Chen et al. (2010)
Wheat p is sensitive to changes in REX
the intensity after financialisation of mkt
Results
Cointegrating vector β
1981:1-2012:1
1995:1-2012:1
ln real fed funds
-0.132 (-2.55)
-0.207 (-6.03)
0.045 (1.99)
0.021 (1.09)
-0.999 (-3.94)
-0.436 (-1.99)
us fed spread
ln end-stock-to-use
The real fed fund
confirms the presence of the monetary policy effect. A loose
monetary stance of 1% => ↑ in p level by about 0.1% and
0.2%. When real i is high money flows out of commodities
and prices shrink. In line with Dornbusch (1976), Frankel
(2008), Anzuini (2012).
The spread variable
has a + sign, signaling that the future expectations on
tighten monetary policy have not a depressing effect on
wheat p. An ↑ in spread by 10% rises prices by about 0.5% .
The stocks-to-use ratio
captures the effects of market S and D factors on p
determination (Westcott & Hoffman, 1999). The variable
has a - sign. A faster growth in use than in ending stocks =>
that D growth outpaces S growth. This put a ↑ pressure on p.
Results
Cointegrating vector β
1981:1-2012:1
1995:1-2012:1
sst
0.244 (3.50)
0.248 (4.54)
soi
0.166 (5.71)
0.104 (4.26)
ln world ind prod
3.290 (2.80)
1.807 (2.63)
-1.008 (-2.56)
0.340 (1.42)
ln thinness
ln speculation
0.715 (7.14)
Bad weather conditions
SST anomalies have a > impact than the fluctuations in air
pressure occurring between the western and eastern tropical
Pacific during El Niño and La Niña episodes. Since the
variability of SOI is > SST, the effect of SOI could be more
detrimental for wheat production and prices.
Industrial production
A ↑ 1% produces a ↑ in price by about 3.3% and 2.5%
=> global D is an important determinant of commodity p
Thinness of the mkt,
while - and significant for the sample 1981:1-2012:1, it turns
out to not be significant for the sample 1995:1-2012:1.
Speculation
=> Futures traders finish amplifying the price fluctuations on
cash market. Speculative behaviour in the wheat futures mkt
affects the associated spot mkt.
Model Validation
H0: No serial correlation vs. H1corr.
H0: Normality vs. H1 Non normality
H0: Homosked. vs. Heterosk.
Goodness of fit
Ljung Box stat.
OK
Doornik Hansen test
Heteroskedasticity test
Akaike Information
NOK
OK
Conclusions
All the theories on drivers of commodity p do not necessarily contradict, but rather
complement each other.
A complex of factors together have caused quick price ↑ in the wheat mkts, including
speculation, macroeconomic fundamentals, market specific variables, and weather
conditions. This would require a complex response at the international level.
Loose monetary policy, strong economic activity, and speculative pressure push wheat prices
up. An increase in rex and stock-to-use has had a curbing effect on wheat prices.
This would suggest that policy makers should give more consideration to the impact of
monetary manoeuvres on food commodity prices. This because monetary policy tends to be
more focused on core inflation - i.e. a measure of inflation that excludes the rate of increase
of prices for food and energy products - than on headline inflation. Since households spend a
major portion of their budgets on food and energy, a focus on both core and headline
inflation would be necessary when determining the appropriate stance of monetary policy.
Conclusions
A further factor behind rising food commodity prices is the increase in energy price. This
would indicate that biofuel policies should be carefully monitored in some case changed to
avoid unnecessary subsidisation.
Financial and wheat markets have become more and more interwoven, and “speculation” is
an important determinant of price dynamics. Although a presence of “speculators” on the
derivatives markets is a necessary condition for functioning markets and efficient hedging,
price fluctuations can attract significant speculative activity and destabilize markets. Policy
measures should be addressed at supervising the financial market, in order to avoid that
speculation becomes excessive.
As regards stocks, it seems important to develop better data collection systems at global
level and across countries. This will be important to have a better knowledge on the state of
the food commodity market and facilitate policy makers in their decisions.
Thank you for your attention!