Sunday 28 April 2013

ECONOMY JOURNAL



                            THE VOLATILITY OF WORLD CRUDE OIL PRISES
                                                          
                                                           Abstrak

Peran minyak dalam ekonomi adalah sangat penting. Artikel ini mengukur ketidakpastian harga
minyak dunia berdasarkan pada standar deviasi bersyarat. Artikel ini berfokus pada volatilitas
harga minyak mentah di pasar Inggris, Texas, dan Dubai, selama periode Januari 1980 sampai Mei
2010. Hasil analisis menemukan bukti bahwa dampak leverage yang asimetris tidak ditemukan.
Hasil analis juga menemukan bahwa proses volatilitas return terhadap rata-ratanya hanya terjadi di
Dubai. Temuan ini memiliki beberapa implikasi penting bagi Indonesia. Pemerintah dapat
menggunakan dinamika harga minyak di pasar Dubai sebagai patokan untuk mengatur anggaran
negara dalam rangka mewujudkan kesinambungan fiskal.
Keywords: Harga minyak, volatilitas, asymmetric leverage, fiskal berkesinambungan
JEL classification numbers: C22, Q43
                                                  
                                                    INTRODUCTION

Oil is arguably the most influential physical
commodity in the world and plays a prominent
role in an economy. It is not a surprise,
therefore, that the price of oil changes attracts
a considerable degree of attention for
many decades. Various attempts have been
undertaken to explain the behaviour of the
oil price as well as to assess the macroeconomic
consequences of oil price shocks especially
since oil crisis in 1970s.
The oil price shocks was repeated in
early 2000s. The wide price fluctuations in
2000s, when crude oil price index has increased
272 percent between January 2000
and March 2008, and fluctuations by more
than USD 20 a barrel in mid 2008 reinforce
the idea that oil prices are volatile. The
lates one, in early February 2011, the world
oil price touched USD 100 per ba rrel.
The volatility of oil prices has
prompted governments, especially in developing
countries, to intervene in the oil
market in various ways. Most countries in
the world have been conducting some policies
including price-smoothing schemes for
end users, fuel tax adjustments, price controls,
and subsidies for lower income class,
or even incentives for diversification away
from oil.
At the same period, the world witnessed
the most marked commodity price

boom of the past century. The price of metals,
food grains, and other commodities
rose sharply, and over a sustained period.
Like commodity booms in earlier decade,
this one was associated with strong global
growth, but was exceptional in its duration
and in the range of commodities affected.
By mid 2008, metals and minerals were
296 percent higher and internationally
traded food prices 138 percent higher —
mainly due to higher grain prices.
The high food commodity and oil
prices have significant political impacts.
Haiti, for example, faced serious internal
governmental problems. China, Vietnam,
and India imposed some protections to their
international trades. Indonesia, among others,
desired to develop the national food and
energy security (Sugiyanto, 2008). Coupled
with the financial crisis that erupted in September
2008 and the subsequent global economic
downturn, some developing countries
have suffered dramatic increase in poverty
incidence (World Bank, 2009).
The recent sharp increase in oil
price raises the question as to the nature i.e.
permanent or temporary. Knowledge of oil
price fluctuation under market-oriented energy
policy is very important. The greater
oil price volatility would increase a household’s
income risk and a potential output
loss for business. For the government, the
greater oil price volatility would increase
subsidies. In short, the oil price shocks
would deteriorate the whole economy by
meant various channels (Rodriguez and
Sanchez, 2005).
In the case of Indonesia, oil price is
set by the government. It is under government
subsidy since 1970s. Despite the fact
that Indonesia is exporting oil, the country
also imports oil from other countries. The
surplus of importing value over the exporting
value makes Indonesia a net oil importing
country. Despite these facts, the repercussions
from price increase in the world
market could not be avoided from spillover
to the local market.
Being a government control item,
the event of oil price surge has inflicted a
soaring fuel subsidy bill to the government.
This situation pressured the Indonesia’s
government to review its policy on oil
prices and finally decides implement oil
price increase in the local market. The government’s
decision to slowly liberalize the
local oil market has triggered mixed responses
from the public, particularly
households and business units.
The main objective of this paper is
to understand the nature of dependence of
the conditional variance on past volatility
in oil prices. The conditional standard deviation
is interpreted as a measure of uncertainty.
The rest of this paper proceeds as
follows. The next section describes the oil
prices behaviour. This is followed by exploring
previous empirical evidences. The
methodological framework and the data are
delivered in the proceeding section; the penultimate
section discusses empirical results;
and the last section concludes and
points to some directions for future research.
Some policy implications for Indonesia
are also drawn.
Oil Prices Fluctuation
The world oil price fluctuation has very
long history. Crude oil prices behave much
as any other commodity with wide price
swings in times of shortage or oversupply.
The crude oil price cycle may extend over
several years responding to changes in demand
as well as OPEC and non-OPEC
supply. Table 1 below presents some major
factors that have influenced the world oil
markets and therefore the oil price.
Let us start explaining the oil price
dynamics within 1970s. In 1972, the price
of crude oil was about USD 3.00 per barrel,
increased 50 percent compared to the beginning
decade. By the end of 1974, the
price of oil had quadrupled to over USD
12.00. After embargo, the world crude oil
price was relatively flat ranging from USD
12.21 per barrel to USD 13.55 per barrel.
The Volatility of World … (Kuncoro) 3
Table 1: Some Major Influencing Factors on the World Oil Markets and Oil Price
No. Year Moment
1 1973-1974 · Oil embargo began (October 19-20, 1973)
· Oil embargo ended (March 18, 1974)
2 1979-1982 · Iranian revolution; Shah deposed
· OPEC raised prices 14.5% on April 1, 1979 and OPEC raised prices 15%
· Iran took hostages; President Carter halted imports from Iran
· Saudis raised marker crude price from 19 $/bbl to 26 $/bbl
· Kuwait, Iran, and Libya production cut drop OPEC oil production to 27
million b/d
· Saudi Light raised to USD 28/bbl, Saudi Light raised to USD 34/bbl
· First major fighting in Iran-Iraq War
3 1983-1986 · Libya initiated discounts
· OPEC cut prices by USD 5/bbl and agreed to 17.5 million b/d output
· Norway, United Kingdom, and Nigeria cut prices
· OPEC accord cut Saudi Light price to USD 28/bbl
4 1990-1991 · Iraq invaded Kuwait
· Operation Desert Storm began
· Persian Gulf war ended
5 1996-2001 · U.S. launched cruise missile attacked into southern Iraq following an Iraqi
supported invasion of Kurdish safe haven areas in northern Iraq.
· Prices rose as Iraq’s refusal to allow United Nations weapons inspectors
into "sensitive" sites raises tensions in the oil-rich Middle East.
· OPEC raised its production ceiling. This was the first increase in 4 years.
· World oil supply increased by 2.25 million barrels per day in 1997, the
largest annual increase since 1988.
· Oil prices continued to plummet as increased production from Iraq coincides
with no growth in Asian oil demand due to the Asian economic crisis
and increases in world oil inventories following two unusually warm
winters.
· Oil prices tripled between January 1999 and September 2000 due to
strong world oil demand, OPEC oil production cutbacks, and other factors,
including weather and low oil stock levels.
· Oil prices fell due to weak world demand (largely as a result of economic
recession in the United States) and OPEC overproduction.
· Oil prices declined sharply following the September 11, 2001 terrorist
attacks on the United States, largely on increased fears of a sharper
worldwide economic downturn (and therefore sharply lower oil demand).
6 2002-2010 · Political instability within various oil producing nations
· Rising costs of oil
· Speculator entered the oil market
· Global financial crisis
· European sovereign debt crisis
Source: http://www.eia.doe.gov/emeu/cabs/chron.html, http://www.wtrg.com and Kuper
(2002)
4 ___________  ___________________________________________
In 1979 and 1980, events in Iran
and Iraq led to another round of crude oil
price increases. The Iranian revolution resulted
in the loss of 2 to 2.5 million barrels
per day of oil production between November
1978 and June 1979. The combination
of the Iranian revolution and the Iraq-Iran
War caused crude oil prices to more than
double increasing from USD 14 in 1978 to
USD 35 per barrel in 1981.
From 1982 to 1985, OPEC attempted
to set production quotas low
enough to stabilize prices. These attempts
met with repeated failure as various members
of OPEC produced beyond their quotas.
During most of this period, Saudi Arabia
acted as the swing producer cutting its
production in an attempt to stem the free
fall in prices. Crude oil prices plummeted
below USD 10 per barrel by mid-1986 in
accordance with world economic recession.
The price of crude oil spiked in
1990 with the lower production and uncertainty
associated with the Iraqi invasion of
Kuwait and the ensuing Gulf War. From
1990 to 1997 world oil consumption increased
6.2 million barrels per day. Asian
consumption accounted for all but 300,000
barrels per day of that gain and contributed
to a price recovery that extended into 1997.
Declining Russian production contributed
to the price recovery.
The price increases came to a rapid
end in 1997 and 1998 when the impact of
the economic crisis in Asia was either ignored
or severely underestimated by
OPEC. In December, 1997 OPEC increased
its quota by 2.5 million barrels per day (10
percent) to 27.5 MMBPD effective January
1, 1998. The rapid growth in Asian economies
had come to a halt. In 1998 Asian Pacific
oil consumption declined for the first
time since 1982. The combination of lower
consumption and higher OPEC production
sent prices into a downward spiral. In response,
OPEC cut quotas by 1.25 million
b/d in April and another 1.335 million in
July. Price continued down through December
1998.
With minimal Y2K problems and
growing US and world economies the price
continued to rise throughout 2000. Russian
production increases dominated non-OPEC
production growth from 2000 forward and
was responsible for most of the non-OPEC
increase since the turn of the century. In the
absence of the September 11, 2001 terrorist
attack this would have been sufficient to
moderate or even reverse the trend. In the
wake of the attack crude oil prices plummeted.
Spot prices for the U.S. benchmark
West Texas Intermediate were down 35
percent by the middle of November. The
oil prices were moving into the USD 25
range by March, 2002.
During much of 2004 and 2005 the
spare capacity to produce oil was under a
million barrels per day. A million barrels
per day is not enough spare capacity to
cover an interruption of supply from most
OPEC producers. In a world that consumes
over 80 million barrels per day of petroleum
products that added a significant risk
premium to crude oil price and was largely
responsible for prices in excess of USD 40-
50 per barrel.
Throughout the first half of 2008,
oil regularly reached record high prices. On
February 29, 2008, oil prices peaked at
USD 103.05 per barrel, and reached USD
110.20 on March 12, 2008, the sixth record
in seven trading days. Prices on June 27,
2008, touched USD 141.71/barrel, for August
delivery in the New York Mercantile
Exchange (after the recent USD
140.56/barrel), In January 2009, oil prices
rose temporarily because of tensions in the
Gaza Strip. From mid January to February
13, oil fell to near USD 35 a barrel. As of
May 2010, crude oil prices have started to
decline again due to the 2010 European
sovereign debt crisis. On May 17, 2010 the
price for a barrel of crude oil fell below
USD 70 a barrel to USD 69.41.
The Volatility of World … (Kuncoro) 5
While the evolution of commodity
prices is relatively stable, that of oil prices
is more volatile (Reigner, 2007). Various
attempts to explain the behaviour of the oil
price have been undertaken in the past few
years. Three main approaches can be identified
in this vast literature: first, Hotelling’s
(1931) notion of oil as exhaustible
resource; second the ascertainment that the
global macroeconomic situation is an important
factor, and, thirdly, the notion that
additional factors such as OPEC announcements
as well as speculation affect
the price of oil.
Regarding the first approach, Hotelling’s
(1931) seminal paper proposes the
notion that oil is exhaustible and that the
price of oil, in optimum, grows at the rate
of interest. Various extensions of this rule
have been suggested and are still subject of
scientific debates, see e.g. Sinn (2008). In
particular Krautkraemer (1998), however,
provides evidence of frequent failure of
empirically testing Hotelling-type hypotheses.
Dvir and Rogoff (2009) epitomize this
skepticism: they apply the storage rather
than a Hotelling resource extraction model
in order to model oil price behaviour.
Papers such as Slade (1982) and
Pindyck (1999) deal with oil price behaviour
in the very long run. These papers deal
with the question as to whether the price of
oil follows a deterministic trend. While
Slade (1982) finds evidence of quadratic
trends in real oil prices, Pindyck (1999) argues
that the oil price fluctuates around a
long-run trend. The trend itself is - due to
changes in demand, extraction costs and
new site discoveries – stochastically fluctuating
over time. Livornis (2009) provides
an excellent survey of this literature and
expresses a less pessimistic view on the
significance of the Hotelling rule.
In contrast to this line of research,
Krichene (2002) and Dees et al. (2007) argue
that the price of oil is determined by
global economic conditions and employ
demand and supply frameworks in order to
explain the oil price. Krichene (2002) uses
a structural multiple equation model of the
global oil market and focuses on the calculation
of demand and supply elasticity.
Among the more salient findings of this
paper is that short-run demand and supply
of oil is very price inelastic and that longrun
oil supply elasticity significantly decreased
after the first oil crisis 1973/74.
Dees et al. (2008), in contrast, use a
country-by- country approach and explicitly
incorporate geological factors as well as
OPEC behaviour in their oil supply function.
The model is generally able to reproduce
responses of the global oil market to
changes in OPEC behaviour. The papers by
Kaufmann et al. (2004) and Dees et al.
(2008) also focus on the role of OPEC behaviour,
but do not explicitly model oil
supply. Both papers make use of an error
correction approach and show that variables
such as OPEC capacity utilization
and OPEC quotas Granger cause real oil
prices but not vice versa.
While these results are more of very
general character, Kaufman and Ullmann
(2009) show that the 2008 oil price hike
can be explained by a combination of fundamental
factors and speculative behaviour,
and Miller and Ratti (2009), finally, provide
evidence of the existence of oil price
bubbles.
The unstable world oil price pumps
dozen empirical studies dealing with its
impacts on economic activity in all aspects.
Sadorsky (1999), among others, tested the
relationship between oil price and stock
market. In developing countries, Sari
(2006) simultaneously examined the link of
oil price, stock returns, interest rates, and
output in Turkey. Gronwald et al. (2009)
analyzed the oil price fluctuation in Kazakhstan
related to economic growth.
Mohammad (2010) observed the impact of
oil prices volatility on export earning in
Pakistan. Aliyu (2009) connected the oil
price to exchange and inflation rates in Ni6
___________      ___________________________________________
geria. In general the found a negative impact
generated from oil price volatility.
Bacon and Kojima (2008) investigated
the degree of oil price volatility
Ghana, Chile, India, Philippine, and Thailand
during July 1999-March 2007. They
observed some adverse impacts on exchange
rates and fiscal condition. Dealing
with world oil price fluctuation, they point
out some policies including the role of
hedging, strategic stocks, price-smoothing
scheme, and reducing the importance of oil
consumption to achieve energy security.
In the case of Indonesia, the world
crude oil price is used as basic assumption
to set up budget state in current year. Kuncoro
(2010) found that the increase in oil
price marginally induces fiscal stance for
about 0.02 percent. His study implied that
the primary balance surplus is vulnerable to
maintain fiscal sustainability. This finding
would suggest that price smoothing based
on long-term trends would have imposed a
considerable fiscal drain.
To summarize, the price of oil is affected
by numerous factors and subject to a
considerable degree of volatility. Hamilton
(2008) nicely summarizes these findings:
“Changes in the real price of oil have historically
tended to be permanent, difficult
to predict, and governed by very different
regimes at different points in time”. Thus,
deriving future predictions is a very difficult
task. In any case, expecting the oil
price to begin a stable increase in the near
future would definitely be hazardous.
METHODS
The brief literature review above suggests
the potential for some interesting hypotheses
about potential linkages among energy
commodities, macroeconomic variables,
and more importantly dependency across
energy markets. The purpose of this section
is to develop an analytical framework
within which these can be clearly stated as
a set of formal propositions. We focus on
the oil market.
From an econometric point of view,
neglecting the exact nature of the dependence
of the variance of the error term conditional
on past volatility will result in loss
of efficiency. The ARCH models are developed
to model time-varying conditional
variances (see Bollerslev et al., 1994).
ARCH models consist basically of two
equations, one for the mean and one for the
conditional variance. The mean equation
can be univariate or may contain other
variables (multivariate). GARCH model
addresses the issues of heteroscedasticity
and volatility clustering by specifying the
conditional variance to be linearly dependent
on the past behaviour of the squared
residuals and a moving average of past
conditional variance. Formally, the model
can be expressed as follows:
yt = bxt + et (1)
The mean equation may also include the
conditional variance or the conditional
standard deviation (ARCH-in-Mean models).
The specification for the conditional
variance may allow for asymmetric effects.
Here we start with a symmetric univariate
specification.
In applications using monthly data
the error variance depends on past volatilities
going back a number of periods. For
these applications GARCH (Generalised
ARCH) models are developed. The
GARCH model depicts conditional variance
of a price series to depend on a constant,
past news about volatility and the
past forecast variance. The GARCH(p,q)
model has p ARCH terms and q GARCH
terms (the values of p and q are determined
by the Schwarz Information Criterion):
_ _ − − 2 = + 2 + 2
t t p t q s w a e b s (2)
It is commonly assumed that the innovations
_t are Gaussian. If this assumption
is violated the usual standard errors are
not consistent and the quasi-maximum likeThe
Volatility of World … (Kuncoro) 7
lihood covariances and standard errors described
by Bollerslev and Wooldridge
(1992) have to be used.
The simplest GARCH model is the
GARCH(1,1) model that in many applications
provides a good description of the
data. The error variance depends on all past
volatilities with geometrically declining
weights as long as bt < 1. Well-defined
conditional variances require that the parameters
w, _, b are non-negative. In many
applications the estimates for _ + b in the
GARCH(1,1) model are close to unity,
which means that the model is not covariance
stationary. In that case the model can be
used only to describe short-term volatility.
It is notable that in the symmetrical
model, the conditional variance is a function
of the size and not of the sign of
lagged residuals. One way to allow for
asymmetries is the Threshold GARCH
(TARCH) model:
t q t p
t t p
ln( ) [ / ]
ln( ) /
2
2
b s g e s
s w a e s
(4)
The coefficient g in the last term of equations
(3) and (4) measures the leverage effects.
In theory there may be many leverage
effects, Eviews only allows for one. In this
model, good news (_t < 0) and bad news (_t
> 0) have different effects on the conditional
variance. Good news has an impact of _,
while bad news has an impact of (_ + g).
According to Swaray (2002), the
strength of ARCH-class models as compared
with time-series models, lie in their
ability to allow the conditional variance of
underlying processes to vary over time. Also
the information that is used in forming conditional
expectations is similar to that used
to predict the conditional mean (i.e. variables
observed in previous periods). Hence,
the GARCH model maintains the desirable
forecasting properties of a traditional timeseries
but extends them to the conditional
variance (Holt & Aradhyula, 1990).
RESULTS DISCUSSION
Data of world crude oil prices are presented
by UK Brent (light blend), WTI Midland
Texas, and Dubai (medium) in USD per barrel
(fob). The sample periods chosen for this
study extend from January 1980 to the May
2010. The total observation is 365 sample
points. The data are provided by the International
Financial Statistics (IFS) online service
(International Monetary Funds, 2010).
The raw data are then transformed into first
log-differenced to obtain volatility measurement.
Figure 1 delivers the crude oil
prices volatility in three markets.
Table 2 presents the elementary statistics
covering mean, median, and extreme
values. The average of first log-differenced
is close to each other, around 2 percent for
the three markets. However, the median values
are far enough from the respective mean
especially in Texas and Dubai. Similarly,
the absolute (maximum and minimum) values
are not identical to each other. Those
preliminary indicate non normal distribution.
We will re-check more convincingly later.
The Table also delivers standard
deviation ranging from 0.082 to 0.089. Statistically,
a set data is said to be relatively
volatile if its CV (ratio of standard deviation
to its mean) is more than 50 percent.
Based on the empirical rule, the crude oil
price in UK is the most volatile indicated
by the highest CV, followed by that in Dubai
and Texas markets. This finding supports
to the theoretical background in the
previous section that the oil prices are not
stable.
8 ___________  ___________________________________________
The log-differenced oil prices are
asymmetrically distributed (bell-shaped)
indicated by the high value of Jarque-Bera
tests. The null hypotheses that the series
data is normally distributed can be rejected
in 95 percent confidence level. The lower
tail of the distribution is thicker than the
upper tail (indicated by the negative values
of skewness in Texas and Dubai) and the
tails of the distribution are thicker than the
normal (indicated by the kurtosis coefficient
greater than the thick tails can be
modelled by assuming a “conditional”
normal distribution for returns; where conditional
normality implies that returns are
normally distributed on each month, but
that the parameters of the distribution
change from month to month. Also, as evidenced
in Table 2, the volatility (standard
deviation) of oil price returns exhibits
“clustering” i.e. bursts of high volatility
separated by periods of relative tranquility.
The correllograms of the logdifferenced
oil prices and of the squared
log-differenced oil prices for 12 lags suggests
strong dependence in the mean of
variance. There is only a few insignificant
in the longer lags but substantial dependence
in the volatility. This time-varying
nature of variance is referred to in statistics
as heteroscedasticty. The persistence of
volatility is an indication of autocorrelation
in variances.
The Ljung-Box Q-statistic test can
be used to check for autocorrelation in
variance. Under the null hypothesis that a
time series is not autocorrelated, Q(p) is
distributed c2(p), where p denotes the number
of autocorrelations used to estimate the
statistic. For p = 12, the Q(p) statistic for
squared oil price returns is 53.3, 58.7, and
94.8 respectively, which rejects the hypothesis
that variances of monthly returns
are not autocorrelated. They seem that the
price volatility in the three oil markets is
persistent at least in one year.
The price volatility in the three oil
markets typically is indifferent each other
presented by the correlation matrices. The
correlation is high even close to unity. The
highest oil price volatility correlation is
more than 0.94 between Dubai and UK.
The oil price volatility in Dubai market is
lowest correlated with that in Texas (0.89)
compared to the others. The long distance
between Dubai and Texas might be the
source of explanation.
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
.5
1980 1985 1990 1995 2000 2005 2010
VUK
-.5
-.4
-.3
-.2
-.1
.0
.1
.2
.3
.4
1980 1985 1990 1995 2000 2005 2010
VTX
-.4
-.2
.0
.2
.4
.6
1980 1985 1990 1995 2000 2005 2010
VDB
Source: Data processed.
Figure 1: Crude Oil Prices Volatility
The Volatility of World … (Kuncoro) 9
Table 2: Descriptive Statistics
VUK VTX VDB
Mean 0.001772 0.001894 0.001936
Median 0.001748 0.000287 0.005566
Maximum 0.466400 0.391112 0.521421
Minimum - 0.313472 - 0.395148 - 0.335434
Std. Dev. 0.089002 0.081742 0.086547
Skewness 0.042534 - 0.393327 - 0.026617
Kurtosis 5.926260 6.838412 8.323111
Jarque-Bera 129.9819 232.8422 429.7982
Probability 0.000000 0.000000 0.000000
CV (%) 5022.69 4315.84 4470.40
Source: Data calculation.
Table 3: Causality Test
Null Hypothesis: Obs F-Statistic Probability
VTX does not Granger Cause VUK 352 1.80577 0.04616
VUK does not Granger Cause VTX 1.66045 0.07431
VDB does not Granger Cause VUK 352 1.28950 0.22293
VUK does not Granger Cause VDB 0.70846 0.74325
VDB does not Granger Cause VTX 352 1.61119 0.08687
VTX does not Gra nger Cause VDB 1.78951 0.04874
Source: Data estimation.
Correlation does not necessary present
causation. The traditional Granger test
could be employed to identify the direction
of causality. The test is done for 12 lags as
suggested from partial autocorrelation. Table
3 identifies how great the oil price volatility
in one market affects to the oil price
volatility in the other markets. Regardless
to the significance, Table 3 preliminary
suggest the existence of oil price volatility
co-movement.
Does the high volatility of the data
mean non stationary? Table 4 shows the
results of Augmented Dickey-Fuller (ADF)
and Phillips-Perron (PP) unit root tests for
the underlying data series in levels and first
differences. According to Swaray (2002)
the Phillips–Perron (PP) test can be more
appropriate in this case because of the evidence
of heteroscedasticity assumed in the
error process of the price series examined.
We assume that the level of the oil price is
not stationary.
Formal unit-root tests (including a
constant, no trend, and 12 lags) to log-oil
price data reject the hypothesis of a unit
root at 5% (the ADF test statistic equals -
1.7, 5% critical value equals –2.8693). The
similar results are obtained by implementing
PP unit root tests. However, these tests
have only little power if errors are not homogeneous
(Kim and Schmidt, 1993). Furthermore,
the power of unit root tests depends
more on the span of the data, which
in our case is only 30 years, than on the
number of observations (Perron and Shiller,
1985). Moreover, the presence of structural
breaks reduces the power of unit root tests
also (Perron, 1989). More details on unit
roots, structural breaks, and trends can be
found in Stock (1994).
The same method imposed to the
log-differenced oil price data gives the opposite
conclusion. The ADF test statistic
equals from -13.1 to -14.6 and the PP test
statistic ranges from -12.3 to 14.2 implying
the series data have a unit roots. The occur10
___________      ___________________________________________
rence of unit roots in the price series of
these commodities gives a preliminary indication
of shocks having permanent or
long lasting effect, thus making it very difficult
for traditional price stabilization policies
to survive.
Stationary is required to perform
co-integration. Co-integration is an important
concept to analyze the data behaviour.
Using Johansen’s maximum likelihood approach
(Johansen, 1988; 1991), we test the
bivariate among the three oil price markets
volatility with 4 lags in all the cases. The
trace and Max-Eigen value (_ max) statistics
for testing the rank of co-integration
are shown in Table 5.
The results of both tests deny the
absence of co-integrating relation oil prices
volatility series. Furthermore, both tests
suggest the presence of one co-integrating
equation at 5 percent level or better between
the non stationary prices of crude oil
which means that the linear combinations
of them are stationary and, consequently,
prices tend to move towards this equilibrium
relationship in the long-run. This is
complement to the result of correlation and
causality analysis.
Furthermore, does the stationary of
oil prices change imply that it will return to
its mean value? The following section presents
empirical results for a monthly time
series data. The results of GARCH estimation
model will clearly answer this question.
The Schwarz Information Criterion for
GARCH model suggests that a = 1 and b =
1. The GARCH model results are in Table 6.
The ARCH Lagrange Multiplier test
indicates that there is no autoregressive conditional
heteroscedasticity up to order 12 in
the residuals. An alternative test is the Ljung-
Box Q-statistic of the standardized squared
residuals. At the twentieth lag Q equals from
7.4 to 13.8, indicating that the standardized
squared residuals are serially uncorrelated.
From these tests, we conclude that the
GARCH volatility model is adequate.
Table 4: Unit Root Tests
ADF Test PP Test
Level t-stat 5% level t-stat 5% level
Log (OP UK) -1.676441 - 2.869285 1.459409 -2.869263
Log (OP TX) -1.766282 -2.869285 -1.450294 -2.869263
Log (OP DB) 1.771291 2.869285 1.272295 2.869263
First log-diff. t-stat 5% level t-stat 5% level
VUK -14.64803 -2.869285 -14.19856 -2.869285
VTX -13.78728 2.869285 13.25763 2.869285
VDB -13.09016 2.869285 12.28922 2.869285
Source: Data estimation.
Table 5: Multiple Co-integration Tests
Hypothesized
Eigenvalue
Trace 5 Percent 1 Percent
No. of CE(s) Statistic Critical Value Critical Value
None ** 0.315899 313.6462 29.68 35.65
At most 1 ** 0.246502 177.3519 15.41 20.04
At most 2 ** 0.190216 75.7447 3.76 6.65
Notes: (1) *(**) denotes rejection of the hypothesis at the 5%(1%) level, (2) Trace test indicates 3
cointegrating equation(s) at both 5% and 1% levels.
Source: Data estimation.
The Volatility of World … (Kuncoro) 11
Table 6: GARCH Model Estimates
VUK VTX VDB
Coeff. Z-stat Coeff. Z-stat Coeff. Z-stat
Constant - 0.002380 -0.67879 -0.003018 - 1.25735 0.005142 1.25949
w 0.000450 3.25528 0.000106 1.64696 0.003935 10.52062
a 0.348587 6.75545 0.351480 7.23237 0.501573 7.96304
b 0.647337 13.07202 0.703594 17.81267 -0.022678 - 0.39833
Diag. test Value Prob. Value Prob. Value Prob.
a + b = 1 0.01415 0.9054 3.57608 0.0594 42.0631 0.0000
0.01415 0.9053 3.57608 0.0586 42.0631 0.0000
J-B test 20.70801 0.0000 15.70913 0.0000 132.11880 0.0000
ARCH
LM(12)
0.97428 0.47312 0.73163 0.72034 1.04797 0.40418
11.73505 0.46719 8.88609 0.71263 12.59086 0.39947
Q(12) 11.3580 0.4980 7.4229 0.8289 13.8170 0.3130
Source: Data estimation.
The Wald test for (a + b = 1)
clearly indicates that the volatility process
does not return to its mean mainly in UK
and Texas. The F and c2 values are 0.01 for
UK and 0.06 for Texas respectively. Those
are enough to reject the null hypotheses
that (a + b = 1). For Dubai, the coefficient
b even is insignificant. The F and c2 values
are quite greater to accept the null hypotheses.
This means that the model can be used
only to describe short-term volatility especially
in UK and Texas in order to predict
in the near future.
.00
.05
.10
.15
.20
.25
.30
1985 1990 1995 2000 2005 2010
Conditional Standard Deviation
Source: Data processed
Figure 2a: Conditional Standard Deviation
of VUK
.00
.05
.10
.15
.20
.25
.30
1985 1990 1995 2000 2005 2010
Conditional Standard Deviation
Source: Data processed.
Figure 2b: Conditional Standard Deviation
of VTX
.05
.10
.15
.20
.25
.30
.35
.40
1985 1990 1995 2000 2005 2010
Conditional Standard Deviation
Source: Data processed.
Figure 2c: Conditional Standard Deviation
of VDB
12 ___________               ___________________________________________
Table 7: Asymmetric GARCH Model Estimates
VUK VTX VDB
TARCH Coeff. Z-stat Coeff. Z-stat Coeff. Z-stat
Constant -0.003480 - 0.93677 -0.004041 1.49348 0.004327 1.03028
w 0.000416 3.11633 9.46E-05 1.54344 0.003915 10.36967
a 0.281078 4.25853 0.282623 3.56680 0.399368 7.35491
b 0.667451 13.51501 0.717981 17.13441 -0.017900 0.31028
g 0.102887 0.85241 0.112852 1.06394 0.193605 1.13149
Test: g = 0 Value Prob. Value Prob. Value Prob.
F 0.726604 0.3946 1.131976 0.2881 1.280276 0.2586
c2 0.726604 0.3940 1.131976 0.2874 1.280276 0.2578
EGARCH Coeff. Z-stat Coeff. Z-stat Coeff. Z-stat
Constant 0.00190 - 0.5324 0.00315 - 1.1725 0.00535 - 1.5892
w -0.98086 - 4.4482 0.73476 - 3.8901 1.06589 - 7.7229
a 0.51025 6.7925 0.49732 6.1763 0.52547 9.1020
b 0.88365 25.1959 0.93344 32.7686 0.86583 42.7314
g -0.05553 - 0.9699 0.06908 - 1.3464 0.08179 - 1.5509
Test: g = 0 Value Prob. Value Prob. Value Prob.
F 0.940666 0.3328 1.812796 0.1790 2.405213 0.1218
c2 0.940666 0.3321 1.812796 0.1782 2.405213 0.1209
Source : Data estimation.
Volatility is plotted in Figure 2 that
shows the conditional standard deviation of
the GARCH (1,1) model. Because the volatility
process does not return to its mean
value, the conditional standard deviation
graph contour in UK and Texas rather fluctuates
without clear basic pattern. On the
contrary, even though also fluctuates, the
conditional standard deviation graph contour
in UK quite rather flats based on the
basic value a = 0.5015. Consequently, the
standard deviation of oil price in Dubai is
relatively more predictable than that in UK
and Texas.
As mentioned earlier, in the symmetrical
model the conditional variance is a
function of the size and not of the sign of
lagged residuals. TARCH and EGARCH
models take into account the sign of lagged
residuals. The results for the TARCH
(1,1,1) and EGARCH (1,1) models are presented
in Table 9. In general, the results of
TARCH and EGARCH models statistically
have no different from GARCH models as
presented in Table 7.
The individual tests using Z, F, and
c2 for g conclude that all of the leverage
effect terms is not significantly positive
(even with a one-sided of 5 percent level
test) so there does not appear to be an
asymmetric effect. In these models, good
news (_t < 0) and bad news (_t > 0) have no
different effects on the conditional variance
*). The absence of leverage effect that
can normally be found on financial markets
might be due to that commodity markets
are more prone to volatility when the price
goes up and when the price goes down as
what can be observed in the financial markets.
In term of forecasting, the asymmetric
effects imply that the prediction of
the oil price in the near future is then relatively
easy without considering bad news
and bad news. In other words, the conditional
variance and standard deviation are
controllable so that the prediction value is
asymptotically will be more accurate. Furthermore,
hedging cost associated with the
change in oil prices risk would be minimized.
Finally, the optimal position for all
*) We do not report results the tests for the TARCH
and EGARCH models completely since leverage
effects are not significant. They can be available
on request to the author.
The Volatility of World … (Kuncoro) 13
players in the oil market would be achieved
in the frame of market efficiency.
CONCLUSION
In this paper we tried to understand the nature
of dependence of the conditional variance
on past volatility in oil prices. The
volatility is measured by the first logdifferenced.
The measure of uncertainty we
choose is the within-month high-low range
of the conditional standard deviations.
Time-varying conditional variances are estimated
using univariate (G)ARCH models.
GARCH models depend on the frequency
of the data, so we also examine
monthly time series for the period January,
1980 to May, 2010 representing 365 observations.
We focus on volatility of the world
crude oil prices in UK, Texas, and Dubai
markets. We found that the preferred model
is a symmetric GARCH (1,1) model.
Asymmetric leverage effects are not found
in the three markets. In fact, the positive
shocks are more dominant than the negative
shocks. However, the volatility process
returns to its mean only in Dubai.
Those findings have some important
implications for Indonesia. The main
policy recommendation to emerge from this
paper is that any effort invested in reducing
the oil dependency of the Indonesian economy
is more than justified. Moreover, it is
worth considering a tightening of the stabilization
fund which would lead to a less
fragile economic development. Second, the
resurgence of energy price crises should
redirect energy security policy towards the
development and adoption of energysaving
technology, such as gas, coal, solar
panels, wind turbines, hydropower, biomass,
and other renewable energy.
Third, as a net oil importer country,
Indonesia faces a dilemma when the world
crude oil price increases. In one hand, the
central government revenue increases substantially
due to oil and gas taxes. On the
other hand, the central government has to
spend more subsidies to avoid the increase
of domestic fuel prices. In this case, the
government could use the dynamics of oil
price in Dubai market as a benchmark to
set up her state budget in order to realize
fiscal sustainability.
The volatility of oil prices is interesting
to be explored further. This study
used a univariate GARCH model. More
advance research could utilize the multivariate
GARCH to capture volatility persistence
across markets. It is also advisable to
use high frequency data i.e. daily data in
the longer time horizon to catch uncertainty
among oil, commodity, and stock markets.
There is no doubt that in the globalization
era, oil, commodity, and stock markets are
increasingly integrated.
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