1

How do oil prices affect stock returns in GCC markets? An asymmetric

cointegration approach.

Mohamed El Hedi AROURI (LEO-Université d‟Orléans & EDHEC, mohamed.arouri@univ-orleans.fr)

Julien FOUQUAU (ESC Rouen & LEO, julien.fouquau@univ-orleans.fr)

2009

Abstract

This paper investigates the existence of long-term relationships between oil prices and GCC stock markets. Since

GCC countries are major world energy market players, their stock markets are likely to be susceptible to oil price

shocks. To account for the fact that stock markets may respond asymmetrically to oil price shocks, we propose

an approach based on asymmetric cointegration. The results of the empirical analysis show that oil price shocks

indeed affect the stock returns in an asymmetric fashion in GCC countries.

Key words: GCC stock markets, oil prices, asymmetric cointegration analysis

JEL classifications: G12, F3, Q43.

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1. Introduction

There has been a large volume of studies on linkages between oil prices and macroeconomic

variables. Most of these studies have established significant effects of oil price changes on

economics activity for several developed and emerging countries [Cunado and Perez de

Garcia (2005), Balaz and Londarev (2006), Gronwald (2008), Cologni and Manera (2008) and

Kilian (2008)]. Furthermore, some papers have shown that the link between oil and

economics activity is not entirely linear and that negative oil price shocks (price increases)

tend to have larger impact on growth than positive shocks do [Hamilton (2003), Zhang

(2008), Lardic and Mignon (2006, 2008) and Cologni and Manera (2009)]. In sharp contrast

to the volume of work investigating the link between oil price shocks and economics activity,

there has been relatively little work done on the relationship between oil price variations and

stock markets. Furthermore, most of these works have focused on few industrial countries

(US, Canada, Europe and Japan). Concerning emerging markets, very few studies have been

carried out. These studies have mainly focused on the short term interaction between energy

price shocks and stock markets.

One rationale of using oil price fluctuations as a factor affecting stock prices is that value

of stock in theory equals discounted sum of expected future cash-flows. These cash-flows are

affected by macroeconomic events that possibly can be influenced by oil shocks. Thus, oil

price changes may influence stock prices. Most of previous studies have investigated this

relationship within the framework of a macroeconomic model, using low frequency (monthly

or quarterly) data from net oil importing countries. Using weekly data and new asymmetric

cointegration tests, this article investigates the long-term relationship between oil price shocks

and stock markets in the Gulf Cooperation Council (GCC) countries.

Studying the link between oil prices and stock markets in GCC countries is interesting for

several reasons. First, GCC countries are major suppliers of oil in world energy markets, their

stock markets may be susceptible to change in oil prices. Second, the GCC markets differ

from those of developed and from other emerging countries in that they are largely segmented

from the international markets and are overly sensitive to regional political events. Finally,

GCC markets represent very promising areas for regional and world portfolio diversification.

Studying the influence of oil price shocks on GCC stock market returns is important for

investors to make necessary investment decisions and for policy-makers to regulate stock

markets more effectively. All these reasons highlight the interest of a study centred on GCC

countries.

The pioneer paper by Jones and Kaul (1996) tests the reaction of international stock

markets (Canada, UK, Japan and US) to oil price shocks on the base of a standard cash-flow

dividend valuation model. They found that for the US and Canada this reaction can be

accounted for entirely by the impact of the oil shocks on cash-flows. The results for Japan and

the UK were inconclusive. Using an unrestricted vector autoregressive (VAR), Huang et al.

(1996) show a significant link between some American oil company stock returns and oil

price changes. However, they find no evidence of a relationship between oil prices and market

indices such as the S&P500. In contrast, Sadorsky (1999) applies an unrestricted VAR with

GARCH effects to American monthly data and shows a significant relationship between oil

price changes and aggregate stock returns in US. In particular, he shows that oil price shocks

have asymmetric effects: positive oil shocks have a greater impact on stock returns and

economy activity than do negative oil price shocks. Relying on nonlinear causality tests, Ciner

(2001) provides evidence that oil shocks affect in a non linear manner stock returns in US,

which is consistent with the documented influence of oil on economics activity.

Recently, some papers have tented to focus on major European, Asian and Latin American

emerging markets. The results of these studies show a significant short term link between oil

price changes and emerging stock markets. Using a VAR model, Papapetrou (2001) shows a

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significant relationship between oil price changes and stock markets in Greece. Basher and

Sadorsky (2006) reach the same conclusion for other emerging stock markets using an

international multifactor model. However, less attention has been given to smaller emerging

markets, especially in the GCC countries where share dealing is a relatively recent

phenomenon. Using VAR models and cointegration tests, Hammoudeh and Eleisa (2004)

show that there is a bidirectional relationship between Saudi stock returns and oil prices

changes. The findings also suggest that the other GCC markets are not directly linked to oil

prices and are less dependent on oil exports and more influenced by their own domestic

factors. Bashar (2006) employs VAR analyses to study the effect of oil price changes on GCC

stock markets, and shows that only Saudi and Oman markets have predictive power of oil

price increase. More recently, Hammoudeh and Choi (2006) have examined the long-run

relationship among the GCC stock markets in the presence of the US oil market, the S&P 500

index and the US treasury bill rate. They have found that the treasury bill rate has direct

impact on these markets, while oil and S&P 500 have indirect effects.

As we can see, the results of the few available works on GCC countries are too

heterogeneous. These results are puzzling because the GCC countries are strongly oil

exporters and are similar in their economic structure. Furthermore, the GCC economies are oil

dependent and thus are sensitive to oil price changes. The conclusions of previous works

could be due to the fact that the tests they rely on are not powerful enough to detect nonlinear

linkages. As we have mentioned above, recent papers argue that there is an asymmetric

relationship between oil prices and the economics activity. This suggests that asymmetric

linkages between oil prices and the stock market could be uncovered. The current article

extends the understanding of the relationship between oil prices and stock markets in GCC by

testing for asymmetric long term linkages, in addition to linear linkages.

The rest of the paper is organized as follows. Section 2 presents briefly the GCC markets

and the role of oil. The methodology is introduced in section 3. Section 4 presents the data

and discusses the empirical results. Summary conclusions and policy implications are

provided in Section 5.

2. GCC stock markets and oil

Table 1 presents some key financial indicators for the stock markets in GCC countries. The

GCC was established in 1981 and it includes six countries: Bahrain, Oman, Kuwait, Qatar,

Saudi Arabia and United Arab Emirates (UAE). GCC countries have many common patterns.

Together, they produce about 20% of all world oil, control 36% of world oil exports and

possess 47% of the world oil proven. Oil exports largely determine earnings, governments‟

budget revenues and expenditures and aggregate demand. The contributions of oil to GDP

range from 22% in Bahrain to 44% in Saudi Arabia. Moreover, Table 1 indicates that for the

three largest economies in GCC countries, Saudi Arabia, UAE, and Kuwait, the stock

market‟s liquidity indicator is positively associated with the oil importance indicator in these

economies. Table 1: Stock markets in GCC countries in 2007

Market Number of

companies*

Market

Capitalization

($ billion)

Market Capitalization

(% GDP) *

Oil

(% GDP)+

Bahrain 50 21.22 158 22

Kuwait 175 193.50 190 35

Oman 119 22.70 40 41

Qatar 40 95.50 222 42

UAE 99 240.80 177 32

S. Arabia 81 522.70 202 44 Sources: Arab Monetary Fund and Emerging Markets Database. * numbers in 2006.

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Furthermore, GCC countries are importers of manufactories products from developed and

emerging countries. Therefore, oil price fluctuations can indirectly impact GCC markets

through their influence on prices of imported products and then increases in oil prices are

often indicative of inflationary pressure in the GCC economies which in turn could indicate

the future of interest rates and investment of all types. Thus, oil price fluctuations should

affect corporate output and earnings, domestic price levels and stock market share prices in

GCC countries. However, unlike net oil importing countries where the expected link between

oil prices and stock markets is negative, the transmission mechanism of oil price shocks to

stock market returns are ambiguous and the total impact of oil price shocks on stock returns

depends on which of the positive and negative effects counterweigh the other.

Figure 1 : GCC countries oil dependency

Source: International Monetary Fund.

Saudi Arabia leads the region in terms of market capitalization. However, in comparison to

each country‟s GDP, Qatar is the leader. Stock market capitalization exceeded GDP for all

counties except Oman. In terms of the number of listed companies, Kuwait is the leading

market followed by Oman. Overall, GCC stock markets are limited by many structural and

regulatory weaknesses: relatively small numbers of listed firms, large institutional holdings,

low sectoral diversification, and several other deficiencies remain. However, in recent years a

broad range of legal, regulatory, and supervisory changes has increased market transparency.

More interestingly, GCC markets begin improving their liquidity and opening their operations

to foreign investors. Recently, in March 2006 the Saudi authorities lifted the restriction that

limited foreign residents to dealing only in mutual investment funds and the other markets

have progressively followed.1

Finally, even if GCC countries have several economic and political characteristics in

common, they have different oil dependence degrees and efforts to diversify and liberalize the

economy. In particular, each country in GCC depends on oil to a different degree. For

example, UAE and Bahrain are less oil-dependent than Saudi Arabia and Qatar (Figure 1).

Thus, comparison between GCC stock markets forms an interesting subject.

1 For interested readers, further information and discussions about market characteristics and financial sector

development of these countries can be found in Creane et al. (2004), Neaime (2005), and Naceur and Ghazouani

(2007).

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3- Methodology

The aim of this article is to test for long term relationships between oil prices and stock

markets in GCC countries. We test for both linear and asymmetric cointegration. For linear

cointegration, we use the traditional approach. To prove the presence of asymmetric

cointegration, we need to follow a three-step process [for more details about this methodology

see Schorderet (2004) and Lardic and Mignon (2006, 2008)]. In a first time, it is necessary to

decompose a time series in positive and negative parts. According to Schorderet (2004)2, by

distinguishing its positive and negative increments, any time series Ttty 0 can be broken

down into its initial value and its negative and positive cumulative sums:

ttt yyyy 0 (1)

where 0y is the initial value and

it

t

i

itt yyy1

0

01 (2)

and

it

t

i

itt yyy1

0

01 (3)

.1 is the indicator function taking 1 if the event in brackets occurs and zero otherwise.

In a second step, we consider now two nonstationary time series ty1 and ty2 . We suppose

that there is no traditional cointegration but a linear combination tz of their components such

that:

ttttt yyyyz 24231211 (4)

If there is a vector ),,,( 4321 with 0 , 21 and 43 such as tz is a

stationary process, the time series ty1 and ty2 are said to be asymmetrically cointegrated.

Other alternative, suppose that only one component of each series appears in the

cointegrating relationship (4). This may be seen as a cointegration relation which “operates”

only in one direction:

ttt zyy 121 Tt ,,1 (5)

or

ttt zyy 221 Tt ,,1 (6)

Because of nonlinear characteristics of tz , OLS estimation of (5) and (6) is likely to be

biased in finite sample and the usual techniques of statistical inference are misspecified.

Schorderet (2004) suggests estimating using OLS the auxiliary models (7) and (8). As proven

2 The asymmetric cointegration approach of Schorderet (2004) we used in this paper have been applied in several

recent studies, see for instance Lardic and Mignon (2006, 2008) and Shen et al. (2007).

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by West (1988), under fairly general conditions the OLS estimation of (7) and (8) is

asymptotically normal and statistical inference can then proceed in the usual way:

tttt yyy 1211 (7)

or

tttt yyy 2211 (8)

To test for no cointegration against asymmetric cointegration, the traditional Engle and

Granger procedure can be applied to (7) and (8) instead of (5) and (6).3 Thus, in order to test

for asymmetric cointegration between oil prices and stock prices in GCC countries, we can

estimate the following long-term relation:

tttt LOilLStockLStock 110 (9)

tttt LOilLStockLStock 210 (10)

where tLStock , tLStock , tLOil and tLOil are respectively the positive and the negative

component of logarithms of stock price indices and the oil prices as defined in (2) and (3).

Traditional cointegration tests can be applied to (9) and (10).

4- Data and Empirical Results

4.1- Data

Our goal is to investigate the existence of a long-term relationship between oil prices and

stock markets in GCC countries. Unlike previous studies which use low frequency data

(yearly, quarterly or monthly), we use weekly data which may adequately capture interaction

between oil and stock prices in the region. We do not use daily data in order to avoid time

difference problems with international markets. In fact, the equity markets are generally

closed on Thursdays and Fridays in GCC countries, while the developed and international oil

markets close for trading on Saturdays and Sundays. Furthermore, for the common open days,

the GCC markets close just before US stocks and commodity markets open. Accordingly, we

opt to use weekly data and choose Tuesday as the weekday for all variables because this day

lies in the middle of the three common trading days for all markets.

Figure 2: oil prices and stock market indices (in logarithms)

3.6

4.0

4.4

4.8

5.2

5.6

20 40 60 80 100 120 140 160

LBAHRAINLKUWAITLOMAN

LQATARLSAUDILUAE

LMSCILOIL

3 According to Schorderet (2004), various extensions of models (7) and (8) are possible. For instance, an

intercept can be added.

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Furthermore, the data used in all the analyses predate the end of 2005, and thus previous

studies missed the spectacular evolutions that took place in the GCC and oil markets in the

last three years. Therefore, our sample period goes from June 2005 to October 2008 for the

six GCC members. Stock market indices are obtained from MSCI database. For oil, we use

the weekly OPEC countries spot price weighted by estimated export volume obtained from

the Energy Information Administration (EIA). OPEC prices are often used as reference prices

for crude oil including oil produced by GCC countries.4 All prices are denominated in

American dollar. Figure 2 depicts the historical paths of oil and stock prices (in logarithms).

We may think that some long run dependencies amongst them do exist.

4.2-Unit root tests

In the first step of our analysis, we have to prove the presence of unit roots in the oil price and

stock market series in logarithm. To this aim, we apply alternatively three standard tests: the

Augmented Dickey Fuller (ADF), the Phillips Perron (PP) and the Kwiatkowski et al. (1992)

(KPSS) test. Contrary to the two first tests5, the KPSS test has the advantage to be based on

the null hypothesis of series stationnarity.

In performing an ADF test, we will face two practical issues. First, we have the choice6 to

include a constant, a constant and a linear time trend, or neither in the test regression. Indeed,

including irrelevant regressors in the regression will reduce the power of the test to reject the

null of a unit root. Then, we apply a fisher test strategy to select the optimal model. Under the

null hypothesis, the critical values of this test are not standard but they have been computed

by Dickey Fuller (1981, p.1063). Second, we will have to specify the number of lagged

difference terms of the dependent variable to be added to the test regression. The usual advice

is to include a number of lags sufficient to remove serial correlation in the residuals. In

respect of it, we retain the number of lags that minimize information criteria.

Table 2: Unit root tests

Levels First difference

ADF PP KPSS ADF PP KPSS

LOil 0.24a 0.49a 1.30***b -8.35***a -8.26***a 0.17b

LStock Bahrain -0.33a -0.42a 0.94***b -10.77***a -10.81***a 0.18b

LStock Kuwait 1.06a -1.93b 1.39***b -14.04***a -14.01***a 0.30b

LStock Oman -0.55a -0.04a 0.84***b -2.72**a -10.80***a 0.20b

LStock Qatar -0.23a -0.14a 0.38*b -11.74***a -11.89***a 0.15b

LStock Saudi -0.72a -0.72a 0.90***b -11.73***a -11.72***a 0.12b

LStock UAE -1.04a -0.95a 0.53**b -10.95***a -10.95***a 0.18b

Notes : All variables are in natural logs. ADF is the Augmented Dickey-Fuller test, PP the Phillips-Perron test and KPSS the Kwiatkowski-

Phillips-Schmidt-Shin test. (a) model without constant nor deterministic trend, (b) model with constant without deterministic trend, (c) model with constant and deterministic trend. *, ** and *** denote rejection of the null hypothesis respectively at 10%, 5% and 1%.

To specify the PP or KPSS tests, we have to select the regression form to test as in the

ADF test7. Then, we must choose the kernel and the bandwidth parameter needed for

4 Very similar results are obtained with West Texas Intermediate and Brent spot prices. Oil prices are in US

dollar per barrel. Note also that GCC currencies are pegged at fixed rates to the U.S. dollar officially since 2003. 5 The ADF and PP test are based on the null hypothesis of a unit root.

6 The specification choice in ADF or PP tests doesn‟t modify the result except for the Oman stock markets series

in first difference and test regression with a constant and a linear time trend. These results are available upon

request. 7 Two alternative test regressions are only possible in KPSS test: inclusion of a constant or a constant and a

trend. We choose the same or the close form that in ADF test regression.

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estimating the residual spectrum at frequency zero. The usual solution is to use Bartlett kernel

and Newey West (1994) data-based automatic bandwidth parameter method.

In table 2, we have reported the results of the three unit root tests. All tests give the same

conclusion that the series are integrated of order (1) and in the most of cases at the 1%

significance level.

4.3- Traditional cointegration tests

In the second step, we proceed to standard cointegration tests between the oil prices and stock

markets series. In this respect, we begin by applying the Engle and Granger (1987) testing.

This leads to estimate the following relation (11) by OLS and to test the unit root hypothesis

in the residuals:

ttt LOilLStock 10 (11)

By definition, there is cointegration if the residual sequence t is stationary. To this end,

we use the same strategy that we have applied in the unit root tests: ADF test and PP test. To

confirm our results, we have equally applied cointegration tests using the methodology

developed in Johansen (1991). The null hypothesis in Johansen test is the absence of a

cointegration relationship. Results are reported in Table 3.

Table 3: Traditional cointegration tests

ADF PP Johansen

LStock Bahrain -3.59** -3.31** 15.97

LStock Kuwait -2.09 -2.27 15.16

LStock Oman -2.08 -2.01 11.04

LStock Qatar -1.22 -1.30 6.67

LStock Saudi -1.34 -1.28 5.10

LStock UAE -1.38 -0.89 7.05

Notes: ADF is the Augmented Dickey-Fuller test, PP the Phillips-Perron test and Johansen the trace statistics. *, ** and *** denote

rejection of the null hypothesis respectively at 10%, 5% and 1%. The long-term relation is (11). We apply unit root tests on residual

series t .

For Bahrain, the cointegration hypothesis is not rejected according to ADF and PP tests.

For all the other countries, the findings indicate that residual series are nonstationary and thus

that oil prices and GCC stock markets are not cointegrated. The absence of long term

relationships between oil prices and stock markets in too oil dependent economies such as

GCC countries seems to be counterintuitive. However, as we have previously mentioned,

some recent papers have shown that the link between oil and economics activity is not entirely

linear and that there is strong evidence of asymmetric relationships between the two variables

[Hamilton (2003), Zhang (2008) and Lardic and Mignon (2006, 2008)]. Therefore, one

possible explanation for our findings is that the traditional cointegration tests are too

restrictive and cannot reproduce such asymmetric long term relationships. In the rest of the

paper, we test for asymmetric cointegration between oil prices and stock markets in GCC

countries.

4.4- Asymmetric cointegration tests

In the last stage, we are looking at whether the decomposition of a time series into its positive

or negative partial sum alters the conclusions of the traditional cointegration test. In other

words, is there an asymmetric cointegration relationship between the oil prices and stock

markets in GCC countries?

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As shown by Shorderet (2004), standard unit root and cointegration tests can be applied to

check for asymmetric cointegration. Then, we have to estimate the equations (9, 10) by OLS

and to test for the presence of unit roots in the residual sequences t1 and t2 . As before, we

follow the same methodology and equally the ADF, PP and Johansen tests. The results of

cointegration tests are reported in Table 4 for equation (9) and in Table 5 for the equation

(10). Table 4: Tests for asymmetric cointegration (tests on t1 )

ADF PP Johansen

LStock Bahrain -2.89 -3.92** 20.96**

LStock Kuwait -1.49 -2.29 20.11**

LStock Oman -2.84 -3.46** 14.17

LStock Qatar -1.16 -1.31 15.6

LStock Saudi 0.18 -0.54 20.03*

LStock UAE -1.80 -1.85 11.82

Notes: ADF is the Augmented Dickey-Fuller test, PP the Phillips-Perron test and Johansen the trace statistics. *, ** and *** denote

rejection of the null hypothesis respectively at 10%, 5% and 1%. The long-term relation is (10). We apply unit root tests on residual

series t1 .

Consider first tests on t1 . For Bahrain, the PP and Johansen tests find cointegration

between oil prices and stock markets. Little evidence of cointegration is also found for

Kuwait, Oman and Saudi Arabia based on PP or Johansen tests. For the other countries, the

used tests find no cointegration between the two variables. Next, turn to tests on t2 . Strong

evidence of asymmetric cointegration is found for Oman and Qatar based on the three tests we

use. Evidence of cointegration is also obtained for Bahrain, Saudi Arabia and UAE based on

the PP and Johansen tests and in Kuwait based on the trace statistic test.

Table 5: Tests for asymmetric cointegration (tests on t2 )

ADF PP Johansen

LStock Bahrain -2.74 -3.32** 33.88***

LStock Kuwait -1.45 -2.24 37.75****

LStock Oman -3.03* -3.58** 39.9***

LStock Qatar -3.36** -5.41*** 38.78***

LStock Saudi -2.19 -3.26** 35.47***

LStock UAE -2.41 -3.44** 37.75***

Notes: ADF is the Augmented Dickey-Fuller test, PP the Phillips-Perron test and Johansen the trace statistics. *, ** and *** denote

rejection of the null hypothesis respectively at 10%, 5% and 1%. The long-term relation is (10). We apply unit root tests on residual

series t2 .

As we expected, there is evidence of asymmetric cointegration for all GCC countries at

least in the positive or in the negative components. Thus, there are asymmetric stable long

term relationships between oil prices and stock markets in GCC countries, especially when oil

prices increase.

4.5- Long term relationships

Finally, we estimate the long term asymmetric relationships (equations 9 and 10). The results

are summarized in Tables 6 and 7. T-statistics reported are computed based on corrected

standard errors (due to the presence of serial correlation in the residuals, Schorderet (2004)).

Following West (1988), consistent estimates of the standard deviations can be obtained by

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scaling the OLS statistics by 21

2ˆs where 2 is the usual OLS standard error of regression

and s is an estimator of the spectral density at frequency zero of the regression disturbance.

As we expected, the adjusted R squared are higher for the equation (10) than for equation

(9) confirming the strong evidence of asymmetric cointegration obtained between the positive

components of oil prices and stock markets in all GCC countries.

Table 6: Long term relationships between oil prices and stock markets in GCC (equation (9))

0 1 Adjusted 2R

S.E of resid

LStock Bahrain -0.231 (-9.228)

0.651 (213.17)

0.948 0.08

LStock Kuwait -0.017 (-2.961)

0.846 (150.78)

0.968

0.08

LStock Oman -0.1783

(-28.483)

0.754 (126.01)

0.953

0.087

LStock Qatar -0.101 (-10.775)

1.193 (132.66)

0.957

0.133

LStock Saudi 0.037 (2.473)

1.852 (128.42)

0.956

0.209

LStock UAE -0.197 (-17.393)

1.304 (120.05)

0.947

0.162

Notes: t-satistics corrected as in West (1988) are reported in parentheses. S.E of resid is the residual standard error and Adjusted 2R is the

adjusted R-squared.

The coefficients are significantly positive, suggesting that, in the long term, oil price and

stock market changes move in the same direction in GCC countries. More interestingly,

1 and 1

are different. The elasticity of stock price changes to oil price decreases (Table 6)

varies from 0.65 (Bahrain) to 1.85 (Saudi Arabia). As for positive components, the

coefficients 1

(Table 7) vary from 0.58 (Bahrain) to 1.07 (Saudi Arabia). The fact that 1

are larger than 1

implies that oil price decreases have more impact on stock markets in GCC

countries than oil price increases. Taken together, our results confirm the asymmetric reaction

of GCC stock markets to oil price changes.

Table 7: Long term relationships between oil prices and stock markets in GCC (equation (10))

0

1

Adjusted 2R

S.E of resid

LStock Bahrain -0.037 (-8.923)

0.582 (206.24)

0.983

0.0563

LStock Kuwait 0.118 (15.556)

0.809 (159.83)

0.969

0.107

LStock Oman -0.011 (-2.363)

0.706 (221.79)

0.985

0.064

LStock Qatar 0.068 (13.934)

0.868 (268.03)

0.991

0.062

LStock Saudi 0.103 (11.573)

1.068 (180.43)

0.978

0.121

LStock UAE 0.214 (30.483)

0.786 (168.58)

0.975

0.094

Notes: t-satistics corrected as in West (1988) are reported in parentheses. S.E of resid is the residual standard error and Adjusted 2R is the

adjusted R-squared.

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5- Conclusion and policy implications

This paper extends the understanding of linkages between oil prices and stock markets in

GCC countries. Since GCC countries are major world energy market players, their stock

markets are likely to be susceptible to oil price shocks. We test for classic and asymmetric

cointegration in order to focus on both linear and nonlinear dependencies. In fact, recent

papers argue that there is an asymmetric relationship between oil prices and the economics

activity. This suggests asymmetric linkages between oil prices and the stock markets. The

results of our empirical analysis show that oil price shocks indeed affect the stock index

returns in an asymmetric fashion.

Our findings should be of interest to researchers, regulators, and market participants. In

particular, GCC countries as policy makers in OPEC should keep an eye on the effects of oil

price fluctuations on their own economies and stock markets. For investors, the significant

relationship between oil prices and stock markets imply some degree of predictability in the

GCC stock markets.

The findings of this article offer several avenues for future research. First, the link between

oil and stock markets in GCC countries can be expected to vary across different economic

sectors. A sectoral analysis of this link would be informative. Second, evidence from

international equity markets should be produced to examine the robustness of the findings.

Third, the methodology applied in this article can be used to examine the effects of other

energy products such as gaz. Finally, further research could compare causality between oil

and stock markets in GCC countries and other oil exporting countries.

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