TX_1~AT/TX_2~AT

International Journal of Energy Economics and Policy | Vol 6 • Issue 2 • 2016152

International Journal of Energy Economics and
Policy

ISSN: 2146-4553

available at http: www.econjournals.com

International Journal of Energy Economics and Policy, 2016, 6(2), 152-158.

Indexing Oil from a Financial Point of View: A Comparison
between Brent and West Texas Intermediate

Cem Berk*

Department of Accounting Information Systems, School of Applied Sciences, Istanbul Arel University, Turkey.
*Email: cemberk@arel.edu.tr

ABSTRACT

Brent crude and West Texas intermediate (WTI) are major indices for purchases of oil worldwide among with some others such as OPEC basket.
Brent is traditionally a European index whereas WTI representing slightly sweeter and lighter crude is more applicable in USA. Until 2010, the spread
between WTI and Brent hasn’t been more than few dollars. However in recent years, the spread is widening in favor of Brent and then returning to
the mean. WTI which historically taken over Brent, has fallen below Brent which is now claimed to be the global oil index for the World. This is
sometimes argued with the Shale production and over-supply in the U.S. and several macroeconomic events such as Libyan crisis. The aim of this
paper is to analyze which of these indices is a better indicator for the energy industry. The variables from NYSE exchange traded funds namely energy
select sector SPDR ETF (XLE), Teucrium WTI crude oil ETF (CRUD), and United States Brent oil ETF (BNO) for the period December 1994 and
September 2014. The variables are analyzed for long-run and short-run relationships with unit root tests, vector autoregression models, and vector
error correction models as well as cointegration and Granger causality tests.

Keywords: Energy Modeling, Oil Indexing, Cointegration, Granger Causality
JEL Classifications: C58, P48, Q37

1. INTRODUCTION

For most trades and especially commodities, certain categorizations
are required to see the quality of the goods. For oil trade, this
is done through indices such as Brent, West Texas Intermediate
(WTI), Dubai, Urals, Isthmus, LLS and OPEC. All of these oil
have different characteristics, qualities, and market penetration
and therefore have different prices. OPEC is a basket composed
of Arab, Basrah, Bonny, Es Sider, Girassol, Iran, Kuwait, Marine,
Merey, Murban, Oriente, and Saharan oil. The number of global
indices used are over 150. These indices are used while pricing
oil, so they have importance for international oil trade. Other
crude oils are priced against major indices such as Brent, WTI,
and Dubai.

Technically WTI is the best quality oil among these. But this
is just a slight difference in quality, which means WTI should
trade a few U.S. Dollars premium to Brent. This is a light weight
and low sulphur oil. This means when refined it could generate

more gasoline. This is traditionally an American oil, however its
production is decreasing.

Brent represents a European index, and often characterized by the
North Sea. The oil is in very different locations. The oil is still
known to be light and sweet, however WTI is lighter and sweeter.
So we know from law of one price that the price differential
should be equal and otherwise arbitrage opportunities arise. This
is true however, it is the supply and demand conditions, and the
location differences as well as political risks (as in the case of
Libyan crisis and many others) that could create spread between
these two indices.

Historically, Brent and WTI have traded very close to each
other, spread almost mean reverted to zero level until 2010.
There are many reasons, but to tell the result WTI has lost value
against Brent, and nowadays recovered a bit. The most important
considerations are supply related and geostrategic. U.S. also
started to switch alternative and modern ways of using energy,

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International Journal of Energy Economics and Policy | Vol 6 • Issue 2 • 2016 153

such as Shale Gas. When WTI loses value, people producing and
trading based on WTI lose money. This spread is very important
for international trade, which is the research topic of this paper.
In this paper, it is investigated whether any of these indices have
explanatory power on energy industry.

The remainder of this paper is organized as follows. In Section 2,
some of the recent and important works in this research area are
presented. Then the methodology and research model is given in
Section 3. The information on data, as well as research results are
available in Section 4. In Section 5, some of the important findings
of the study are discussed. In Section 6, policy and financial
implications are discussed.

2. LITERATURE REVIEW

Liao et al., performed a unit root with structural breaks to
test whether international crude oil markets are globalized or
regionalized. Unit root is detected for lower quantiles however
mean reversion is detected for upper quantiles. With Kolmogov-
Sminov methodology it is proven that the price differential is
mean reverting and thus globalization view supported. Oil traded
in USA is more commonly used in WTI whereas out of USA Brent
is used. WTI is also a higher quality with larger quality and less
sulphur. It is argued that until 2010 WTI is traded with a premium
and after 2010 there is a structural break such that WTI crude oil
is traded at discount compared to Brent. Due to the non-normality
and structural breaks, the method in Koenker and Xiao, and
Enders and Lee, is used instead of conventional techniques. The
spreads show unit root in the lower quantiles but mean reversion
in the upper counterparts. The quantile Kolmogorov–Smirnov test
statistic over the whole range rejects the null hypothesis of unit
root which means that the differentials are globally stationary and
supports globalization hypothesis (Liao et al., 2014).

Creti et al., studies the relationship between oil price and stock
market in oil importing and oil exporting countries. The long-
run relationship with Engle-Granger causality are studied for
this purpose. The short run co-spectral analysis of Priestley and
Tong (1973) is also studied. The research period is 2000-2010.
Brent oil is chosen as the oil index for this study. The relationship
between oil index and stock market is found as a medium-term
phenomenon. The relationship is more recognizable for oil
exporting countries where oil shocks move together with stock
market (Creti et al., 2014).

Huang and Chao studies international and domestic oil prices and
indices in Taiwan. The results are interesting; domestic oil prices
don’t Granger cause international indices. Threshold vector error
correction model (VECM) and threshold autoregression is used for
this purpose. Brent crude oil is chosen to represent international
oil index. Another conclusion is the mean reversion is faster when
a small shock occurs than a big shock. Government intervention
to the oil market is ineffective (Huang and Chao, 2012).

Arouri analyzes the respond of European stock movements to oil
changes. The power of this relationship varies according to the
industry. The markets are analyzed between 1998 and 2010. Brent

oil is used as an indicator for oil index. Zivot–Andrews is used
for testing unit root. The study is a multifactor analysis including
return of stocks, industry, oil, and a dummy variable to include
whether there is a crisis. Furthermore Granger causality is short
term variable. It is found that there is a relationship between oil
price changes and stock markets. For the automotive industry
there is a clear negative correlation between industry returns
and oil. But the relationship is not such strong in other industries
(Arouri, 2011).

Wang et al., analyze oil price shocks with stock market activities.
As expected the results state that there are different effects on oil
exporting and oil importing countries. Also the dependence on oil,
increases the negative effects on stock market in case of a price
increase in oil. WTI is chosen as the benchmark oil for this study.
Granger causality and vector autoregression (VAR) is used in this
study. The results show that oil price shock explain 20-30% of
global stock return variations (Wang et al., 2013).

Lee et al., study stock market returns in G7 countries. The research
period is 1999-2009. The research is interesting since it focuses on
developed countries. Oil price changes don’t significantly affect
the stock markets, however stock price changes lead oil prices.
VAR, vector error correction and Granger causality are used in
this study (Lee et al., 2012).

Basher et al., study the relationship between oil price changes,
exchange rates and emerging market stocks. The research method
is structural VAR. Positive shocks of oil prices depress emerging
market stock prices and US Dollar exchange rates. Most of this
dynamic movements take place in the short run. Oil importers’
currency depreciate, whereas oil exporters’ currency appreciate in
case of an increase in oil price (Basher et al., 2011).

Tao et al., explain indexing in shale oil for industrial purposes for
Bogda Mountain oil shale in China. The oil is classified according
to petrological type, organic component content, hydrocarbon
generating potential. The findings show that lithologic types and
industrial classification of oil shales can be classified as follows:
The content of organic component lower than 5%, between 5%
and 15%, between 15% and 25%, and over 25% correspond to
low-quality, medium-quality, and high-quality oil shale (Tao
et al., 2010).

Buyuksahin et al., has shown that starting from Fall of 2008,
the benchmark WTI crude oil has traded at discount to Brent
benchmark. However the same discount isn’t reflected to other
oil indices. This spread is detected on oil futures positions when
controlled macroeconomic and physical market fundamentals.
WTI is historically a more reliable benchmark for U.S.A, where
Brent is a European benchmark. The spread is also analyzed for
several components both for WTI and Brent; such as WTI and
Louisiana Light Sweet, Louisiana Light Sweet and Brent, and
Brent for international oil and Brent. The macroeconomic events
are considered in the analysis namely Libyan crisis and Arab
Spring. The research period is between 2000 and 2012. There is
clear evidence that WTI crude oil traded at discount compared to
Brent. (Buyuksahin et al., 2013).

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International Journal of Energy Economics and Policy | Vol 6 • Issue 2 • 2016154

Kasibhatla studied whether there is a causal relationship between
crude oil and U.S. dollar. The relationship is studied empirically
with co-integration and error correction modeling. The study
reveals that there is Granger causality from U.S. dollar to crude
oil price. Over the past 15 years there wasn’t a stable correlation
between S and P and crude oil ranging from plus or minus 20%.
The data used in the study is U.S. Dollar index (usdx) and crude
oil prices (coil) for the period January 1990-May 2010. The
series are stationary with their first differences (I(1)) according
to augmented Dickey-Fuller (ADF) and Kwiatkowski–Phillips–
Schmidt–Shin. The series are then tested with trace test and
maximum eigenvalue cointegration where one vector is found
which is an indicator of long-run relationship. There is some
doubt on short term relationship; however there is a tendency to
restore equilibrium following a shock to the system. There is also
proven causality, U.S. dollar index Granger causes the crude oil
price (Kasibhatla, 2011).

Gammara et al., study the Granger causality between the price of
oil and integrated Latin American market index. The framework
proposed by Hatemi (2012) is used as methodology. The result
shows no significant causality. The authors further argue from the
law of one price that there is no arbitrage opportunity between oil
and index (Gamarra et al., 2015).

Lee et al., study the relationship between stock prices and WTI
oil index for the period January 1998 and March 2012. GARCH
methodology is used for G7 countries’ stock market performance
and WTI oil index. According to the results Canada has the
highest hedge effectiveness and Japan has the lowest. Because
of low correlation between the stock market index of Japan and
the oil price, the optimal portfolio weight of Japan is higher
(Lee, 2014).

Wei and Chen examine the relationship between WTI oil spot
returns and the S&P 500 energy index. Daily data is used for the
period January 2000 and September 2009. Multivariate GARCH
methodology is used in this paper. The result shows that WTI is
significantly affected by energy index returns. Investors can also
use energy index returns’ past volatility as the basis for WTI oil
price forecasting (Wei and Chen, 2014).

3. RESEARCH MODEL

In the study the variables are checked to see whether they are
stationary. This is done first with ADF methodology. If any of
the roots of the polynomial (1- ∂1L- ∂2L

2-…- ∂pL
p) of an AR (p)

stochastic process lie outside the unit circle, the process is said
to non-stationary. The traditional ADF way of testing for non-
stationarity of an AR (p) process involves testing for the null of
one unit root in:

∆ = + ∆ + + +− −
−

−

∑y y y t ut t j t j t
j

γ φ α β*
1

The stationary characteristics of the variables are tested also with
Phillip-Perron (PP) methodology. PP test is a non-parametric

modification to the standard Dickey-Fuller t-statistic to account
for the autocorrelation that may be present if the underlying
DGP is not AR (1). Instead of adding AR terms in the DGP to
account for (possible) MA terms, they modify the test statistic.
However, Schwert (1989) showed that PP test suffers from poor
size properties if the MA term is large negative. Thus, ADF and
PP tests suffer from quite opposite problems. While the ADF test
does not suffer from as severe size distortions, it is not as powerful
as the PP test.

The other “problem” with the PP test is that of consistent estimation
of the so called long-run variance or the variance of the sum of
the errors: (Virmani, 2001).

σ ε2 1 2 2
1

= −
−
∑p T E j
j

lim [( )]

Since there are differenced variables the variables are tested
for cointegration according to Johansen procedure. If the
coefficient matrix Π has reduced rank r<n, then there exist nxr
matrices α and β each with rank r such that Π = αβ′ and β yt ′
is stationary. r is the number of cointegrating relationships, the
elements of α are known as the adjustment parameters in the
VECM and each column of β is a cointegrating vector. It can
be shown that for a given r, the maximum likelihood estimator
of β defines the combination of yt−1 that yields the r largest
canonical correlations of Δyt with yt−1 after correcting for lagged
differences and deterministic variables when present. Johansen
proposes two different likelihood ratio tests of the significance
of these canonical correlations and thereby the reduced rank
of the Π matrix: The trace test and maximum eigenvalue test
(Hyalmarsson and Osterholm, 2007).

The variables are tested also for long run and short run causality
with Granger methodology. As it turns out, a notion of causality
that is highly relevant to the present context of temporal causal
modeling called Granger causality has been introduced in the
area of econometrics. This notion is based on the idea that a cause
should be helpful in predicting the future effects, beyond what
can be predicted solely based on their own past values. More
specifically, a time series (or a feature in the terminology of the
present paper) x is said to Granger cause another time series y, if
and only if regressing for yin terms of both past values of y and
x is statistically significantly more accurate than doing so with
past values of y only (Arnold et al.).

4. DATA ANALYSIS

The variables used in this paper are from NYSE exchange traded
funds namely Energy Select Sector SPDR ETF (XLE), Teucrium
WTI Crude Oil ETF (CRUD), and United States Brent Oil ETF
(BNO) for the period December 1994 and September 2014. The
data is daily, a total of 901 for all three variables.

The energy select sector SPDR Fund performs a passive
investment strategy to mimic the returns of energy select
sector index before the expenses. The replication strategy

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International Journal of Energy Economics and Policy | Vol 6 • Issue 2 • 2016 155

requires buying all of the securities represented in the energy
index. Fund has a minimum of 95% limit for extreme cases for
buying the assets in the exchange. This index include oil, gas
and consumable fuels; and energy equipment and services. The
companies selected are also in S and P 500 index. The variable
is denoted as XLE (like in the NYSE) shortly in for this paper in
the analysis. The Figure 1 below provides descriptive statistics
for the variable XLE.

Teucrium WTI Crude Oil ETF provides access to crude oil
investment through futures. The fund is unleveraged and
diversified with instruments of different maturities. This reduces
contango and backwardation and minimizes maturity risk. The
daily changes in net asset value of weighted average closing price
in WTI crude futures is reflected to ETF price. The benchmark
index is WTI Crude oil futures contracts, which are traded in
NYMEX. The variable is denoted as CRUD (like in the NYSE)
shortly in for this paper in the analysis. The Figure 2 below
provides descriptive statistics for the variable CRUD.

United States Brent Oil ETF uses short term oil futures which are
traded on ice futures exchange as the benchmark. Therefore the
daily changes in Brent crude oil affects the performance of ETF.
The duration of the contracts does in no circumstances exceed
6 weeks. Depending on market conditions fund sometimes invest

in other crude oil investments that are more liquid. The variable
is denoted as BNO (like in the NYSE) shortly in for this paper in
the analysis. The Figure 3 below provides descriptive statistics
for the variable BNO.

The variables are tested with ADF methodology to check
whether they are stationary. The test is applied under 5% level
of significance. The test statistic for the variable BNO is higher
than critical value with absolute values. This shows that variable
BNO is stationary in level (I(0)). This variable will be used in
level so there is no test for differenced series. The test statistic for
variable CRUD however doesn’t exceed critical value. Therefore
the variable isn’t stationary in level. The variable is then tested
with its first difference where test statistic is higher than critical
value. This means that the variable CRUD is stationary with its first
difference (I(1)). The test statistic for variable XLE fail to exceed
critical value. Therefore the variable isn’t stationary in level. The
variable is then tested with its first difference where test statistic
is higher than critical value. This means that the variable XLE is
stationary with its first difference (I(1)).

The variables are tested also with respected PP methodology to
check whether they are stationary. The findings are the same.
The test is applied under 5% level of significance. The test
statistic for the variable BNO is higher than critical value with

Figure 1: Histogram and descriptive statistics for the variable XLE

Figure 2: Histogram and descriptive statistics for the variable CRUD

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International Journal of Energy Economics and Policy | Vol 6 • Issue 2 • 2016156

absolute values. This shows that variable BNO is stationary in
level (I(0)). This variable will be used in level so there is no
test for differenced series. The test statistic for variable CRUD
however doesn’t exceed critical value. Therefore the variable
isn’t stationary in level. The variable is then tested with its
first difference where test statistic is higher than critical value.
This means that the variable CRUD is stationary with its first
difference (I(1)). The test statistic for variable XLE fail to exceed
critical value. Therefore the variable isn’t stationary in level. The
variable is then tested with its first difference where test statistic
is higher than critical value. This means that the variable XLE
is stationary with its first difference (I(1)). These findings are
presented in Table 1.

The variables BNO (in level), D(XLE) and D(CRUD) are tested to
be with VAR. (VAR) First lag length is determined with selection
criteria. According to Table 2, Schwarz information criterion 1 lag
is suggested. Hannan-Quinn suggests 2 lags, Final prediction
error and Akaike suggests 3 lags. For the principle of parsimony,

1 lag is used as suggested by Schwarz information criterion. 1 lag
model - VAR(1) is developed for most degrees of freedom.

VAR(1) model with inserted coefficients is given below.

D(XLE)=−0.0236144462846*D(XLE(−1))+0.011845650094
*D (CRUD(−1))-0.0120636442044*BNO(−1)+0.511881384436

D(CRUD)=0.107683738179*D(XLE(−1))−0.149917215877*D
(CRUD(−1))− 0.00554718257705*BNO(−1)+0.210270842466

BNO=0.0116398272568*D(XLE(−1))−0.0499537852161*D(CR
UD(−1))+0.979721715954*BNO(−1)+0.822174262873

The model is checked for robustness. This is done by checking
AR roots. For any autoregression model all the roots should be
within the unit circle. This is to say the modulus should be less
than one for the model to be reliable. According to Table 3, no
roots are outside the unit circle, so the model passes the stability
check.

Cointegration test is made to check the long-run relationship
between the variables. The test is made according to Trace and
max eigenvalue methodology. According to both tests there are
3 cointegrating equations among the variables. This means the
variable D(XLE), CRUD, and BNO is cointegrated. This is an
important result since we have I(1) data in our model. The results
are presented in Table 4.

Figure 3: Histogram and descriptive statistics for the variable BNO

Table 2: Lag length selection criteria for VAR model
Lag LogL LR FPE AIC SC HQ
0 −4022.146 NA 1.667556 9.024990 9.041112 9.031151
1 −2300.739 3427.376 0.035861 5.185512 5.249999* 5.210157
2 −2271.062 58.88656 0.034236 5.139153 5.252004 5.182282*
3 −2255.888 30.00811 0.033765* 5.125310* 5.286525 5.186922
4 −2249.410 12.76799 0.033957 5.130964 5.340544 5.211059
5 −2238.034 22.34278* 0.033777 5.125638 5.383582 5.224216
6 −2232.571 10.69398 0.034046 5.133567 5.439876 5.250630
7 −2227.915 9.083242 0.034379 5.143306 5.497980 5.278852
8 −2226.964 1.848059 0.035006 5.161354 5.564392 5.315383
*Indicates lag order selected by the criterion. LR: Sequential modified LR test statistic (each test at 5% level), FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz
information criterion, HQ: Hannan-Quinn information criterion, VAR: Vector autoregression

Table 1: Augmented Dickey-Fuller and PP test results
Model ADF test

crıtıcal value
Test

statıstıc
PP test

crıtıcal value
Test

statıstıc
BNO −2.8645 −3.1101 −2.8645 −3.0093
CRUD −2.8645 −2.7147 −2.8645 −2.6338
XLE −3.4115 −2.9845 −1.9412 0.6668
D(CRUD) −1.9412 −32.22 −1.9412 −32.19
D(XLE) −1.9412 −30.61 −1.9412 −30.70
ADF: Augmented Dickey-Fuller, PP: Phillip-Perron

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International Journal of Energy Economics and Policy | Vol 6 • Issue 2 • 2016 157

To test the short run relationship among the variables
VECM(1) - VECM with one lag is developed. Differenced series
for XLE and CRUD, and BNO in level is used for regression. The
model with substituted coefficients is given below.

D ( X L E , 2 ) = 0 . 0 1 3 3 9 0 3 7 0 5 4 2 3 * ( D ( X L E ( − 1 ) ) -
4 . 4 0 4 9 3 3 8 7 5 0 5 * D ( C R U D ( − 1 ) ) + 0 . 0 1 5 2 6 3 1 0 0 2 8 1 8 *
BNO(−1)-0.684215817684)−0.462598756341*D(XLE(−1),2
)+0.0628008941327*D(CRUD(−1),2)-0.553787639821*D(B
NO(−1))−0.000810155617473

D ( C R U D , 2 ) = 0 . 2 9 3 7 6 9 7 6 8 6 5 5 * ( D ( X L E ( − 1 ) ) − 4
. 4 0 4 9 3 3 8 7 5 0 5 * D ( C R U D ( − 1 ) ) + 0 . 0 1 5 2 6 3 1 0 0 2 8
18*BNO(−1)-0.684215817684)−0.14750935372*D(XLE(−1),
2)+0.0580699809554*D(CRUD(−1),2)+0.108753698777*D(B
NO(−1))−0.00198005307708

D ( B N O ) = 0 . 0 0 7 3 9 4 2 9 1 8 6 6 5 6 * ( D ( X L E ( − 1 ) ) -
4.40493387505*D(CRUD(−1))+0.0152631002818*BNO(−1)-
0.684215817684)−0.00501435687067*D(XLE(−1),2)+0.0
0793526657202*D(CRUD(−1),2)−0.0679807866109*D(B
NO(−1))+0.00169334201357

Finally the variables are tested for Granger Causality. The test
is run both with VAR(1) and VECM (1) to test for long run
and short run effects respectively. The test is applied to check
whether BNO or D(CRUD) Granger causes D(XLE). According
to Table 5, in the short run (with VECM(1))BNO Granger causes
XLE, no other Granger causality is detected for 5% level of
significance.

5. DISCUSSION

The variables representing Brent crude oil, WTI crude oil, and
Energy Industry are checked to see whether they are stationary.
This is done according to ADF and PP methodology. Both test
indicate that Brent crude oil is stationary in level, whereas WTI
crude oil and XLE.

The variables are modeled with VAR and VECM. This is done by
first testing number of lags in the model where 1 lag is chosen as
suggested by Schwarz Information Criterion. The VECM(1) is run
for the short run and VAR(1) for the long run analysis. AR roots
table also support stability of the models since all the roots are
within the circle. The cointegration results according to Johansen
procedure also indicate that the variables are cointegrated.

The final test is Granger causality. This is to see which index which
index provide better explanation in the energy industry. According
to Granger causality results, Brent oil explains Energy industry
perfectly in the short run. This is to say, Brent crude oil index is
a better indicator of explaining energy industry in the short run.
However in the long run this effect disappears. WTI doesn’t have
a statistical explanatory value for energy industry.

6. CONCLUSION

According to the research, Brent crude is statistically an excellent
indicator of energy industry in the short run. Brent traditionally
is a better global benchmark although once taken over by WTI.
This means Brent crude is now more reliable in representing oil
industry. WTI, being an American index from Texas region, fails
to represent energy industry for the research period statistically.
This is an early warning signal for USA, and there is a lot to do
for marketing and balancing the supply for WTI, which despite
its superior quality loses market penetration.

Another finding of the study is that there is no relationship between
oil and energy industry in the long run. This is partly because
long extraction and refinement period for crude oil. This result

Table 4: Cointegration results
Unrestricted cointegration rank test (trace)
Hypothesized number of CE(s) Eigenvalue Trace statistic 0.05 critical value Probality**
None* 0.454493 982.8148 29.79707 0.0001
At most 1* 0.380460 438.5907 15.49471 0.0001
At most 2* 0.009583 8.647326 3.841466 0.0033
Trace test indicates 3 cointegratingeqn(s) at the 0.05 level
*Denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) P values
Unrestricted cointegration rank test (maximum Eigenvalue)
Hypothesized number of CE(s) Eigenvalue Max-Eigen statistic 0.05 critical value Probality**
None* 0.454493 544.2241 21.13162 0.0001
At most 1* 0.380460 429.9433 14.26460 0.0001
At most 2* 0.009583 8.647326 3.841466 0.0033
Max-eigenvalue test indicates 3 cointegrating Eqn(s) at the 0.05 level. *Denotes rejection of the hypothesis at the 0.05 level. **MacKinnon-Haug-Michelis (1999) P values

Table 3: AR roots table
Root Modulus
0.979884 0.979884
−0.159148 0.159148
−0.014546 0.014546
No root lies outside the unit circle
VAR satisfies the stability condition
VAR: Vector autoregression

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International Journal of Energy Economics and Policy | Vol 6 • Issue 2 • 2016158

has important implications for oil industry. The industry should
actively look for alternative ways to extract and refine oil in less
time consuming and more efficient ways.

Finally, as it is stated in this paper for WTI, one source of oil may
not be adequate for explaining oil pricing. This has important
consequences for traders and decision makers who rely on oil
prices. In order to better grasp changes in the industry, there should
be more sophisticating work on indexing oil. This will probably
come with introducing a basket including oil such as Brent, WTI
and perhaps Dubai. This is left for future studies in this field.

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Table 5: Granger causality probability table for D(XLE)
Model BNO D (CRUD)
VECM (1) 0 0,3594
VAR (1) 0,2981 0,8395
VECM: Vector error correction model, VAR: Vector autoregression