Volatility Spillover from Oil to Food and Agricultural Raw Material Markets

doi:10.4236/me.2011.22011

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Modern Economy, 2011, 2, 71-76

doi:10.4236/me.2011.22011 Published Online May 2011 (http://www.SciRP.org/journal/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

E-mail: [email protected], [email protected]

Received January 25, 2011; revised February 24, 2011; accepted February 25, 2011

Abstract

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

purposes.

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

M. KALTALIOGLU ET AL.

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

M. KALTALIOGLU ET AL.

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.

ARMI OPI FPI

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.

Levels

ADF DF-GLS PP KPSS ERS-PO

NG(Mz)

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

intercept

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)

Intercept

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

intercept

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.

M. KALTALIOGLU ET AL.

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-

siduals



ˆˆ

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 (

ˆit





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





ˆvv



is computed (where T is the sample size vi

are the squared standardized error terms estimated via

ˆit



). 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.





ˆvv



DLARMI and DLFPI DLARMI and DLOPI DLOPI and DLFPI

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

M. KALTALIOGLU ET AL.

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

1.28.

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

studied.

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.

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