Modern Economy, 2011, 2, 71-76
doi:10.4236/me.2011.22011 Published Online May 2011 (
Copyright © 2011 SciRes. ME
Volatility Spillover from Oil to Food and Agricultural
Raw Material Markets
Muge Kaltalioglu, Ugur Soytas
Department of Busine ss Administration, Middle East Technical University, Ankara, Turkey
Received January 25, 2011; revised February 24, 2011; accepted February 25, 2011
The upward movement in oil and food prices in the 2000s has attracted interest in the information transmis-
sion mechanism between the two markets. This paper investigates the volatility spillover between oil, food
consumption item, and agricultural raw material price indexes for the period January 1980 to April 2008.
The results of the Cheung-Ng procedure show that variation in oil prices does not Granger cause the variance
in food and agricultural raw material prices. Since there is no volatility spillover from oil markets to food and
agricultural raw material markets, investors can benefit from risk diversification. However, there is
bi-directional spillover between agricultural raw material and food markets.
Keywords: Oil Prices, Food Prices, Agricultural Raw Material Prices, Volatility Spillover
1. Introduction
The simultaneous upward trend in world food prices and
oil prices in the 2000s has triggered an increased interest
on information transmission dynamics between the two
markets. As commodity markets are increasingly viewed
as alternative investment areas, existence and direction
of spillovers must be carefully evaluated by investors.
The existence and nature of the link between alternative
investments will determine the extent to which investors
will be involved in each market for risk management
One conjecture about the recent rise in food prices is
that rising energy prices drive the food prices up [1].
This argument is due to the fact that energy is an impor-
tant input in agricultural activities. The link between
food and energy markets, however, may be more com-
plicated than that (Abbott et al. 2008) [2]. There might
be feedback mechanisms that result in food prices lead-
ing the energy prices. One such mechanism may exist
due to the use of some food items in energy generation.
Increased demand for energy may be driving the oil
prices as well as food prices. Hence, in order to fully
understand the link between the two markets a me- thod
that allows such dynamic feedbacks is required.
In Agricultural Trade Policy Analysis [3] it is stated
that from 2005 to 2007 biodiesel production increased by
5.5 million tones. Additionally, Collins [4] put forth that
60% of the increase in maize prices from 2006 to 2008
may be caused from the increase in maize used in etha-
nol. One can conjecture that the increased demand for
bio-energy results in an increase in food prices and there
might be a switch to the traditional fossil fuel alternatives.
If this conjecture holds, then one expects to see world
food prices leading the oil prices. However, the increase
in food prices is not limited to food items that are also
used in bio-fuel production.
Food consumption item prices are also showing an
upward trend. However, to the extent of our knowledge,
there are no studies that examine the dynamic link be-
tween world oil and food consumption item prices. This
paper is probably the first to examine the volatility spill-
over between world oil, food, and agricultural raw mate-
rial prices. Applying a relatively new methodology that
allows us to test causality both ways, we find that there is
no volatility spillover from oil prices to food consump-
tion item price index or to agricultural raw materials price
index. Furthermore, there is no feedback to the oil mar-
ket as well. We discover bi-directional Granger causality
in variance between the food and agricultural raw mate-
rial markets. The results of this study may have important
implications for both policy makers and global investors
who need to follow the price shocks and transmission
mechanisms between alternative investment areas closely.
The remaining of the paper evolves as follows. Next
section discusses the relevant literature. Third section
introduces the data and discusses methodological issues.
Fourth section presents the empirical findings and the
last section concludes.
2. Price Transmissions
There is a large literature on information transmission
between various commodity markets. For the sake of
brevity, we concentrate on the studies related to the food
prices and oil prices. Coyle et al. [5] examine the struc-
tural changes in the food market and argue that the
changes in the food market can also be associated to the
production process where food is an input to the system.
They find that increased demand for maize used in ethanol
production and the increased demand for rapeseed used in
biodiesel production are responsible for rising prices
(Soaring Food Prices: Facts, Perspectives, Impacts and
Actions Required) [6]. Also the USDA’s chief Economist
asserts that much of the increase in farm prices of maize
and soybeans is due to bio-fuel production [7].
There are a group of studies that focus on transmission
between various food markets. Rezitis [8] underlines that
both farm and retail prices in Greece have significant
effects on each other. Volatility spillover effects are also
present between producer and consumer prices. In an-
other study that deals solely with food prices, Christian
and Rashad [9] examine the increased food prices be-
tween 1950 and 2005 and report a decrease in farm value
of retail prices. Vavra and Goodwin [10] examine the
relation between retail prices and consumer prices of
food and discover presence of asymmetric affects of
price changes in U.S. They find that with decreasing re-
tail prices, consumer prices decline as well. Furthermore,
the links between retail and farm prices is not contempo-
raneous but with a time lag. In an earlier study, Minten
and Kyle [11] emphasize that the increase in the whole-
sale prices is significantly transmitted to the retail prices
within the same week of the price change in wholesale
level. Aksoy and Isik-Dikmelik [12] document that a
change in the commodity prices is more significant in
countries in which people consume more staples rather
than various kinds of foods to extend that consumption
of staple food crops affects the household income. They
infer that the increase in the staple food crop prices has a
significant influence on the household welfare.
Since there is no study that addresses the relationship
between world consumption food items, agricultural raw
materials, and oil prices, we next consider studies on
volatility spillover in various commodity markets. In the
literature, the return and volatility spillover effects are
examined by a variety of methods. Worthington et al. [13]
apply MGARCH method to analyze transmission prices
and price volatility in Australian electricity spot markets.
Fan et al. [14] look for the spillover effect between two
markets, WTI (West Texas Intermediate crude oil) and
Brent crude oil spot markets. GED-GARCH method is
used to estimate the conditional heteroscedasticity. The
results point out two-way Granger causality.
Spillover effects in energy futures markets have been
the subject of many studies as well. Lin and Tamvakis
[15], for example, examine the information transmission
between two oil markets (NYMEX and IPE). They find
that closing prices in NYMEX lead prices in IPE the next
morning. However, there is bidirectional spillover when
both exchanges are trading simultaneously. Baffes [16]
examines the price transmission between crude oil prices
and 35 other commodity prices between 1960 and 2005.
He states that there is information transmission from
crude oil to agricultural commodities. He mentions that
as long as the crude oil prices continue to remain high for
a certain amount of time the price booms will be higher
than the booms experienced before, especially for food
commodities, fertilizers and precious metals. This sug-
gests a volatility spillover from oil to agricultural mar-
kets. Ewing and Thompson [17] argue that a possible
explanation for the increase in consumer prices is the
increase in crude oil prices. But they also point out that
with the in- crease in the industrial production; there is
an avowed rise in oil prices.
As Askari and Krichene [18] state even if the oil pric-
es rise tremendously, change in the demand for com-
modities or for oil will be relatively small if the elasticity
is low. That is, increasing oil price will not have a sig-
nificant influence on demand for food commodities.
As world food markets are open to investors and spe-
culators, just like the oil markets, the prices in both
commodity markets may be governed by similar dynam-
ics. Food, oil and other commodity prices have been stu-
died extensively in the literature. However, to the extent
of our knowledge there aren’t any studies that explicitly
examine the volatility link between world food, agricul-
tural raw material, and oil prices. This paper is concerned
with the spillover effects between agricultural raw mate-
rial, food consumption items, and oil prices. In the next
section we introduce the data and data sources.
3. Data Characteristics
We use monthly data on agricultural raw material spot
prices (ARMI), food spot prices (FPI) and oil spot prices
(OPI) for the period January-1980 to April-2008. ARMI
measures the price changes for timber, cotton, wool,
rubber and hides price indices. FPI measures the price
changes for fruits, vegetables, meat, poultry, fish, gro-
cery food and non-alcoholic beverages. We have chosen
the FPI to understand whether variation in world oil prices
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are transferred to food consumption items. The FPI clearly
represents food items that are not alternatively used in
biodiesel production. ARMI is chosen to understand
whether other agricultural raw materials also oil prices.
OPI measures the price changes for crude oil. The three
price indexes are sourced from International Monetary
Fund (IMF). All price indexes are converted to log returns.
DLARMI: differenced natural log of agricultural raw
material spot prices
DLFPI: differenced natural log of food spot prices
DLOPI: differenced natural log of oil spot prices
The descriptive statistics are given in Table 1 . We see
that standard deviation of OPI is far more than FPI and
ARMI. In addition, OPI has the highest coefficient of
variation while FPI has the lowest of all. That is the most
volatile variable is the oil prices followed by agricultural
raw material prices and food prices respectively. Ac-
cording to Table 1, kurtosis exceeds 3 pointing out the
presence of fat tails which can also be seen in OPI and
FPI. Additionally, negative skewness and significant
Jarque-Bera test statistics imply deviations from normal-
ity. The three price indexes seem to have similar charac-
teristics with most financial series. Therefore, the meth-
ods used in the examination must account for these
properties. The next section discusses the stationarity
properties of the series in concern. The stationarity of the
series are essential for GARCH modeling of the condi-
tional variances.
4. Unit Root Tests
In order to have robust estimation results, the stationarity
of the data is very important. To investigate the station-
arity properties of the series six different unit root tests
are conducted: augmented Dickey-Fuller (ADF) [19],
Elliot-Rothenberg-Stock [20] Dickey-Fuller GLS de-
trended (DF-GLS) [19], Phillips-Perron (PP) [21],
Kwiatkowski-Phillips-Schmidt-Sh in (KPSS) [22], Point
Optimal (ERS-PO), and Ng and Perron’s MZα (NP) [23].
The results of the unit root tests are presented in Ta ble 2
for levels and first differences, respectively.
Table 1. Descriptive statistics.
Mean 93.15299 52.89554 101.7999
Median 97.28017 46.18479 100.3424
Maximum 132.3509 204.3880 172.5399
Minimum 56.92235 18.51047 75.39381
Std. Dev. 18.14362 30.91256 14.49051
Skewness –0.416911 2.104470 1.261623
Kurtosis 2.275578 7.933221 7.063554
Jarque-Bera 17.28401 595.7344 324.1227
Probability 0.000177 0.000000 0.000000
Table 2. Unit root test resultsa.
LOPI –0.867 (1) –1.06 (1) –0.265 (8) 0.678b (15) 6.75a (1) –3.72 (1)
LARMI –1.49 (1) –0.961 (1) –1.32 (3) 1.33a (15) 8.66a (1) –2.79 (1) Intercept
LFPI –0.818 (1) –0.992 (1) 0.436 (0) 0.374c (15) 7.15a (1) –3.40 (1)
LOPI –1.51 (1) –1.08 (1) –0.879 (10) 0.463a (15) 20.9a (1) –4.07 (1)
LARMI –2.37 (1) –2.21 (1) –2.28 (4) 0.348a (15) 9.38a (1) –9.86 (1)
Trend and
LFPI –0.263 (1) –0.768 (1) 0.269 (5) 0.214b (14) 20.8a (1) –3.33 (1)
First differences
LOPI –14.0a (0) –13.6a (0) –13.5a (12) 0.437c (8) 0.185 (0) –158a (0)
LARMI –15.0a (0) –2.66a (4) –15.0a (0) 0.065 (3) 0.506 (0) –9.80b (4)
LFPI –13.5a (0) –11.0a (0) –13.4a (7) 0.553b (1) 0.231 (1) –162a (0)
LOPI –14.1a (0) –14.1a (0) –13.6a (14) 0.034 (11) 0.586 (0) –162a (0)
LARMI –15.0a (0) –4.63a (4) –15.0a (0) 0.069 (3) 0.793 (0) –20.9a (4)
Trend and
LFPI –13.7a (0) –12.3a (0) –13.4a (10) 0.138c (2) 0.692 (0) –161a (0)
aSuperscripts a, b, and c represent significance at the 1%, 5%, and 10% respectively.
According to Table 2 results, although there are slight
differences in test results, we can safely conclude that all
the variables are integrated of order 1. That is price in-
dexes are I(1) in levels. Taking the natural logs and the
first differences converts them into compounded returns
and makes them stationary.
5. Volatility Spillover
Volatility spillover can be viewed as risk spillover. High
volatility means high risk. Financial asset returns (and
commodity returns that follow them closely) generally
exhibit volatility clusters through time. We observe high
volatility periods, and then low volatility periods as
clusters. When return fluctuations in one market lead
fluctuations in the returns of another market, then there is
volatility spillover.
In order to test whether there is volatility spillover
between the three price indexes used in this study, we
utilize the Granger causality in variance approach de-
veloped by Cheung and Ng (1996) (CN hereafter) [24].
Following this procedure we first examine the mean eq-
uations of the three series. The series in concern must be
stationary, therefore the first differences of natural logs
are employed in the mean equations, as suggested by the
unit root tests. For the food and oil returns Akaike in-
formation criteria selects a mean equation with a con-
stant only; whereas, for the agricultural raw material re-
turns ARMA(2, 2) are selected. We find that there are
ARCH effects that need to be modeled explicitly. Hence,
we construct the univariate GARCH models. For agri-
cultural raw material and food returns GARCH(1,1), for
oil returns EGARCH(1,1) model were appropriate (re-
sults are available upon request).
The CN procedure takes the squared standardized re-
itit itit
 2
h from the univariate models
and examines the cross-correlations, where zit are the
stationary variables and are the time varying vari-
ances. Then the sample residual cross-correlation func-
tions between the two standardized residuals (
ˆuu k
12 )
are derived. The sample residual cross-correlation func-
tions between the squares of the two standardized re-
siduals (
ˆvv k
) are derived and the test statistic
is computed (where T is the sample size vi
are the squared standardized error terms estimated via
). The test statistic asymptotically follows the normal
distribution. The CN procedure enables us to see the time
lag through which the volatility spillover occurs. Tab le 3
summarizes the CN Granger causality in variance tests.
Table 3 indicates that volatility spillover in food re-
turns leads fluctuations in agricultural raw material returns
Table 3. Granger causality in variance test statisticsa.
i lag lead lag lead lag lead
0 0.73981 0.73981 1.806384b 1.80638b 0.07917 0.07917
1 –0.7123 –1.2942c –0.1395 –0.7527 –0.0644 –1.3404c
2 0.58194 –0.6444 –0.8059 –0.3047 –0.6684 –0.2338
3 –0.6444 1.07759 –1.0152 0.63701 –0.1123 –1.0863
4 –0.2607 0.27353 0.70677 0.3286 0.37008 0.3977
5 1.672374b 1.36397c –0.8004 0.16338 0.80829 –0.4548
6 –1.1786 0.27353 –1.2575 0.74715 –0.8433 –0.8396
7 0.87382 –0.8573 –0.4681 0.64802 0.69229 –0.5468
8 –1.1051 0.23681 1.92938b 0.2056 0.55604 –0.0939
9 –0.0991 –0.2056 –0.8702 0.05874 –0.3903 0.67756
10 –0.6554 0.22396 –0.9509 –0.279 –0.3406 0.32221
11 –0.4443 –0.7765 –0.8812 –0.8885 –0.5671 –0.4861
12 –0.6756 0.14135 0.1799 1.04271 –0.0442 –1.1158
aSuperscripts a, b, and c denote significance at 1%, 5%, and 10% respectively. The second variable Granger causes the first variable in variance if the test statis-
ic is significant for some lags; vice versa if the test statistic is significant for some leads. t
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at lag 5 at the 5% significance level. There is also weak
evidence of Granger causality in variance from raw ma-
terials to food returns at lags 1 and 5. The results also
show that there is a contemporaneous link between oil
and agricultural raw material returns. This is not surpris-
ing since both are used as inputs in further production. At
the 5% significance level oil volatility leads agricultural
raw material volatility at lag 8. The CN procedure pro-
vides some evidence of a volatility spillover from oil to
food returns at lag 1. However, the result is weak since
the test statistic is very close to the 10% critical value of
Although the CN procedure seems to have uncovered
links between the volatilities of the three indexes, the
evidence is not too strong and the fact that the spillover
occurs in 5 to 8 months indicate that the markets respond
with a lag to changes in the volatility in the other market.
The only link that can be easily interpreted is the con-
temporaneous adjustment of the agricultural raw material
and oil returns since they are closely linked to the pro-
duction processes. The neutrality between agricultural
raw material, food and oil returns is confirmed by the
volatility spillover test results.
6. Conclusions
The commodity markets are viewed as attractive invest-
ment areas as alternatives to financial markets. If they
are seen as alternative investment areas, then commodity
prices must respond to the same factors as financial asset
prices. One such factor is oil price shocks. The respon-
siveness of financial returns to oil price shocks has been
studied a lot in the literature. However, the commodity
market and energy market links are only recently attract-
ing attention. In the commodity price-energy price, food
and agricultural raw material prices is probably the least
Countries that rely on commodity trade are more vul-
nerable to risk and uncertainty in commodity prices. Price
instability affects producers, investors, financial inter-
mediaries and policy makers in addition to its negative
impact on growth and income distribution. Volatility has
been a major source of price instability and its impor-
tance has not diminished due to more liberalization, re-
duction of barriers to trade, and globalization. There is a
developed commodity derivatives market available for
hedging against the commodity price risk, but problems
still remain due to low accessibility of such markets,
spread between local and international prices, low liquid-
ity, lack of local reference prices, lack of derivative in-
struments for certain commodities [25]. The transfer of
volatility between commodity markets makes decisions
even harder for producers, traders and policy makers. If
there is no volatility spillover between alternative com-
modity markets, then market based approaches can be
used to diversify risk. However, if there is evidence of
risk transmission, traditional methods like regulations,
buffer stocks, buffer funds, and international agreements
[25] can be sought.
This paper investigates the volatility spillover between
world oil, food, and agricultural raw material price in-
dexes. We find that there is no volatility spillover from
the oil returns to the food returns. Overall our results
indicate only a contemporaneous link between oil and
agricultural raw materials. Since there is no relationship
between the three market returns studied, there are risk
reduction benefits from employing the three price in-
dexes in portfolio formation. Furthermore, policy makers
cannot use developments in the world oil market to im-
prove their forecasts of the food and agricultural raw
material prices and volatilities. Our results do not support
the claim that oil price hikes are causing the inflation in
food prices.
Further research examining the information transmis-
sion mechanisms between oil prices and individual prices
of different food items or different agricultural indexes
(i.e., wheat, corn, soybean etc.) may prove to be fruitful.
The price and volatility spillover from international
markets to local markets is also an area where further
research is needed. Since commodity markets are increas-
ingly viewed as assets, the dynamic relationship between
commodity prices and financial markets is also of inter-
est to producers, traders, policy makers and scholars.
7. References
[1] C. P. Timmer, “Causes of High Food Prices,” ADB Eco-
nomics Working Paper Series, Asian Development Bank,
Vol. 128, 2008.
[2] P. C. Abbott, C. Hurt and W. E. Tyner, “What’s Driving
Food Prices?” Farm Foundation Issue Reports, July 2008.
[3] Agricultural Trade Policy Analysis, “High Prices on
Agricultural Commodity Markets: Situation and Pros-
pects: A Review of Causes of High Prices and Outlook for
World Agricultural Markets,” Review, European Com-
mission, Directorate-General for Agriculture and Rural
Development, Brussels, 2008.
[4] K. Collins, “The Role of Biofuels and Other Factors in
Increasing Farm and Food Prices: A Review of Recent
Development with a Focus on Feed Grain Markets and
Market Prospects,” Supporting Material for a Review
Conducted by Kraft Foods Global, Inc., 2008.
[5] W. Coyle, M. Gehlhar, T. Hertel, Z. Wnag and W. Yu,
“Understanding the Determinants of Structural Change in
World Food Markets,” American Journal of Agricultural
Economics, Vol. 80, No. 5, 1998, pp. 1051-1061.
[6] Food and Agriculture Organization of the United States,
“Soaring Food Prices: Facts, Perspectives, Impacts and
Actions Required,” High-Level Conference on World
Food Security: The Challenges of Climate Change and
Bioenergy, Rome, 2008.
[7] J. Glauber, USDA Chief Economist, in Testimony before
the Joint Economic Committee of Congress on 1 May
[8] A. Rezitis, “Mean and Volatility Spillover Effects in
Greek Producer-Consumer Meat Prices,” Applied Econo-
mics Letters, Vol. 10, No. 6, 2003, pp. 381-384.
[9] T. Christian and I. Rashad, “Trends in U.S. Food Prices,
1950-2007,” Economics and Human Biology, Vol. 7, No.
1, March 2009, pp. 113-120.
[10] P. Vavra and B. K. Goodwin, “Analysis of Price Trans-
mission along the Food Chain,” OECD Food, Agriculture
and Fisheries Working Papers, OECD Publishing, Paris,
[11] B. Minten and S. Kyle, “Retail Margins, Price Trans-
mission and Price Asymmetry in Urban Food Markets:
The Case of Kinshasa,” Journal of African Economies,
Vol. 9, No. 1, 2000, pp. 1-23. doi:10.1093/jae/9.1.1
[12] M. A. Aksoy and A. Isik-Dikmelik, “Are Low Food
Prices Propoor? Net Food Buyers and Sellers in Low-
Income Countries,” Policy Reaseach Working Paper 4642,
The World Bank, June 2008.
[13] A. Worthington, A. Kay-Spratley and H. Higgs, “Trans-
mission of Prices and Price Volatility in Australian Elec-
tricity Spot Markets: A Multivariate GARCH Analysis,”
Energy Economics, Vol. 27, No. 2, March 2005, pp. 337-
350. doi:10.1016/j.eneco.2003.11.002
[14] Y. Fan, Y.-J. Zhanh, H.-T. Tsai and Y.-M. Wei, “Estima-
ting ‘Value at Rİsk’ of Crude oil Price and Its Spillover
Effect Using the GED-GARCH Approach,” Energy Eco-
nomics, Vol. 30, No. 6, November 2008, pp. 3156-3171.
[15] S. X. Lin and M. N. Tamvakis, “Spillover Effects in
Energy Futures Markets,” Energy Economics, Vol. 23, No.
1, January 2001, pp. 43-56.
[16] J. Baffes, “Oil Spills on Other Commodities,” Resources
Policy, Vol. 32, No. 3, September 2007, pp. 126-34.
[17] B. T. Ewing and M. A. Thompson, “Dynamic Cyclical
Comovements of Oil Prices with Industrial Production,
Consumer Prices, Unemployment, and Stock Prices,”
Energy Policy, Vol. 35, No. 11, November 2007, pp.
5535-5540. doi:10.1016/j.enpol.2007.05.018
[18] H. Askari and N. Krichene, “Oil Price Dynamics,”
Energy Economics, Vol. 30, No. 5, 2008, pp. 2134-2153.
[19] D. A. Dickey and W. A. Fuller, “Distribution of Estima-
tors for Time Series Regressions with a Unit Root,” Jour-
nal of the American Statistical Association, Vol. 74, No.
366, June 1979, pp. 427-431. doi:10.2307/2286348
[20] G. Elliott, T. J. Rothenberg and J. H. Stock, “Efficient
Tests for an Autoregressive Unit Root,” Econometrica,
Vol. 64, No. 4, July 1996, pp. 813-836.
[21] P. C. Phillips and P. Perron, “Testing for a Unit Root in
Time Series Regression,” Biometrika, Vol, 75, No. 2,
1988, pp. 335-346. doi:10.1093/biomet/75.2.335
[22] D. Kwiatkowski, P. C. B. Phillips, P. Schmidt and Y.
Shin, “Testing the Null Hypothesis of Stationarity against
the Alternative of a Unit Root,” Journal of Econometrics,
Vol. 54, No. 1-3, October-December 1992, pp. 159-178.
[23] N. G. Serena and P. Pierre, “A Note on the Selection of
Time Series Models,” Boston College Working Papers in
Economics 500, 2001.
[24] Y. Cheung and L. K. Ng, “A Causality-in-Variance Test
and Its Application to Financial Market Prices,” Journal
of Econometrics, Vol. 72, No. 1-2, May-June 1996, pp.
33-48. doi:10.1016/0304-4076(94)01714-X
[25] D. F. Larson, P. Varangis and N. Yabuki, “Commodity
Risk Management and Development,” World Bank Policy
Research Paper No. 1963, April 1998.
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