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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