1

"The Relationship between Oil Price and Stock Market Index: An Empirical

Study from Kuwait"

The Abstract:

This study attempts to empirically examine the dynamic relationship between Oil Price and

Kuwait’s Stock Market Index. The study applied Markov Switching Model to investigate the

regime shifts between Low- and High volatility regimes using monthly data from January 2005

to September 2015. The study found that Stock Market Index reacts differently to changes in Oil

Price in different regimes. During high volatility regime, there is a positive and significant

relationship between Oil Price and Stock Market Index. On the other hand, in the low volatility

regime there is no relationship between Oil Price and Stock Market Index. The study also

identified four transition episodes of high volatility during (2005M04-06M01, 2006M04-06M06,

2008M10- 09M08, 2013M05-13M09). The results of the study could be used by policy makers

to reduce the negative effect of Oil Price on the Stock Markets in the GCC region.

Keywords: Oil Price, GCC Stock Markets, Kuwait Stock Market, Markov Switching Model.

Researchers:

1. Dr. Ahmed Al Hayky, Ahlia University, Kingdom of Bahrain.

2. Mr. Nizar Naim, Ahlia University, Kingdom of Bahrain.

2

1. Introduction:

In today's world where the globalization of information and investments, the interrelation and

integration of economies and financial markets have made the world's economy as one economy

affected and effected by each other. The changes in Oil price and its effects on the world's

economy is a very clear example of the economic globalization.

Changing Oil price has an effect on the global economy as well as on the macro and micro

economic level of any country. Weather this effect in general is positive or negative depends on

the nature of the Oil-Economy relationship, means; Oil-exporting economy, Oil-refining

economy, and Oil-importing economy (Ha Le et al., 2015; Kilian and Park, 2009).

On the global level, Oil price change affects the global economic activity due to the fact that Oil

is an important component of the economy, and plays a major role in transferring wealth from oil

importing countries to oil exporting countries (Balcilar and Ozdemir, 2013).

On the macro level the Oil price change affects economic recessions, GDP growth, financial

markets, inflation, interest rate, exchange rate, employment, consumer confidence and other

economic factors in different levels and with different mechanisms in the developed and

developing countries (Davis and Haltiwanger, 2001; Hamilton and Herrera, 2002; Lee and Ni,

2002; Hooker, 2002; Cunado and Perez de Garcia, 2005; Kilian 2008; Balcilar and Ozdemir,

2013)

On the micro level Oil price change affects directly and indirectly the cost of goods and services,

the cost of production, the expected cash flows, the variance of the company's returns, the

company's profit and as a result the stock's cash dividends (Bouri, 2015; Naifar and Al

Dohaiman, 2013; Balcilar and Ozdemir, 2013; Jones et al., 2004). Moreover the stock price of a

corporation is affected because of the given effects on the expected future cash flows of the

corporation and the effects on the required rate of return based on the increased risk of the

investment in the corporation (Filis et al., 2011)

The aim of this paper is to investigate the relationship between Oil price and Kuwait stock

market index using Markov Switching Model to investigate the regime shifts between Low- and

High volatility regimes analyzing monthly data from January 2005 to September 2015, The

3

results of the study could be used by policy makers to reduce the negative effect of Oil Price on

the Stock Markets in the GCC region.

This paper is structured as follows. Section 2 provides an overview of the related literature.

Section 3 presents the methodology and the data used in the analysis. Section 4 discusses the

results and its implications. Concluding remarks are summarized in section 5.

2. Literature Review:

The relationship between Oil price and stock markets has been the focus of many researches in

the last two decades, most of these researches however, focused on the developed more than on

the developing and emerging financial markets. In this section the relationship between Oil price

and global stock markets and also the relationship between Oil price and the GCC stock markets

including the Kuwait Stock Index is investigated based on the previous research work.

2.1 Oil Price and Stock Markets Globally:

Oil price movements can be used to forecast stock market returns on a global basis (Pollet, 2005;

Driesprong et al., 2008). The scale, extent, and direction of movements of stock markets oil price

shock is completely diverse from one country to another depending on whether it is oil-exporting

or oil-importing country, and if the oil price change is caused by demand or supply (Wang et al.,

2013). An oil price shock caused by supply has higher negative effect on stock markets than

demand caused oil price shock (Cunado and Gracia, 2014). On another hand however, it is found

that the US stock returns is positively effected by an increase in oil price that is caused by a

growth of the global economy (Kilian and Park, 2009).

Most of the researches on the impact of Oil price on the stock markets in the developed countries

which are mostly oil importing countries have found that there is a negative relationship exists

between Oil price and stock markets (Driesprong et al., 2008; Miller and Ratti, 2009; Basher et

al., 2012). On other hand, according to some researchers a positive relationship between Oil

price and stock market exists in the Oil-exporting countries (Mohanty et al., 2011; Fayyad and

4

Daly, 2011; Lescaroux and Mignon, 2008; Hammoudeh and Choi, 2006). Other researchers

however, suggested that there is no relationship between Oil price and stock markets (Al Jana bi

et al., 2010). The contradicting results could be a result of a changing relationship between two

variables (Akoum et al., 2012).

It is found that Oil price affects stock market returns on the emerging stock markets both in the

short and long term (Papapetrou, 2001; Basher and Sadorsky, 2006; Maghyereh and Al Kandari,

2007). Nguyen and Bhatti (2012) found that left tail dependency between international oil prices

and Vietnam's stock market, however, Chinese market appears to have contrary results. Qinbin,

Mohammad, and Parvar (2012) indicated that 20% of US industry profits can be forecasted by

oil spot price changes. Malik and Ewing (2009), found an evidence of spillover of shocks and

volatility between oil price and some of the US equity sectors. Sadorsky (2001) asserted that oil

price increase is sensitive to stock market returns of Canadian oil and gas companies. On the

same direction, Papapetrou (2001) indicated that stock price movement in Greece is affected by

oil price and increased oil price decreases oil return. On the industry sectors level, El Sharif el al.

(2005) showed that oil price affected positively oil and gas companies return in the UK.

Similarly Balcilar and Ozdemir (2013) found that there is an asymmetric relation from oil futures

to all of S&P 500 sub-index returns.

On a different research, Faff and Brailsford (2000) indicated that oil price risk is equally

important to market risk in Australian stock market. Another study conducted in Norway by

Bjornland (2009) found that there is a significantly positive effect on stock market returns

resulted from an increase in oil prices.

5

2.2 Oil Price and the GCC Stock Markets:

The Middle East Monitor indicated that Saudi Arabia is the biggest exporter and producer of

crude oil in the world reaching 11.5 Million barrels a day of crude oil in 2013, totaling 13.1

percent of the world oil production. The GCC countries oil reserves are as follow; Saudi Arabia:

15.8 %, Kuwait: 6 %, UAE: 5.8%, Qatar 1.5%, and the rest for Oman and Bahrain1.

According to the British Petroleum (BP) Statistical Review of World Energy 2014 report; the

countries of the Gulf Cooperation Council have the largest proven oil reserves in the world

totaling 30 per cent of the world reserve, moreover the GCC states produced 24 per cent of the

world's total crude oil production in 2013 and controls 36 per cent of the world's Sovereign

Wealth, which shows the critical role that the GCC countries can play in the global energy

investment and production2.

On the other hand, the GCC countries are very heavily oil dependant economies, so their

economies are very sensitive to changes in oil price. Oil consists more than 75% of the total

exports and more than 85% of government revenues of the GCC countries (Sedik & Willams

2011). According to the Arab Monetary Fund, Kuwait has the highest number of listed

companies, followed by Oman, the UAE, Saudi Arabia, Bahrain and Qatar. The stock market

capitalization is higher than the GDP of each country except Oman.

Mohantry et al. (2011) indicated that there is a positive relationship between Oil price and stock

market in the GCC except for Kuwait and oil price has asymmetric effects on stock market return

both at the industry and country level. Malik and Hammoudeh (2007) indicated that there is a

high volatility transmission from Oil to all GCC stock markets except for Saudi market. On the

same direction, Arouri et al. (2011) found a strong volatility linkage between Oil price and all

GCC stock markets, as a result of increase of oil price because of shocks and policy changes

affecting oil supply and demand. On the other hand, however, Awartani and Maghyereh (2013)

1 For more information visit this web site: https://www.middleeastmonitor.com/articles/middle-

east/12302-the-oil-resources-of-the-gcc-states

2 For more information visit this web site: http://www.bp.com/en/global/corporate/energy-

economics/statistical-review-of-world-energy.html

6

suggested that the volatility transmission is bi-directional between Oil and GCC stock market

especially after the 2008 financial crisis. Maghayereh and Al Kandari (2007) indicated that Oil

price impact the stock market indices in the GCC countries in a nonlinear way. According to

Arouri and Fouguau (2009) the relationship between oil price and stock GCC markets is

asymmetric and regime-switching. Zarour (2006) found that GCC stock markets respond

significantly to oil price shocks; Saudi market is more sensitive to oil price movements and vice

versa. Arouri et al. (2012) stressed that stock prices respond more to negative oil price shocks

than to positive oil price shocks. Hammoudeh and Li (2008) showed that GCC stock markets

respond to global factors more than regional and local factors. Abu Zarour (2008) finds that great

oil price increase predicts GCC stock market price movements. Akoum et al., (2012) found an

evidence of a changing relationship between oil and stock prices in the GCC in the long term, on

the short term, however, the relationship is weak. In a different study, Naifar and Al Dohaiman

(2013) suggested that the relationship between GCC stock market returns and oil price volatility

is regime dependent, excluding Oman market in the low volatility state. On another hand, Jouini

and Harrathi (2014) showed that there is a bidirectional and unidirectional volatility

interdependence among GCC stock markets, moreover, there is asymmetric spillover to negative

shocks among GCC stock and oil market with the exception of the Kuwait/Qatar and Qatar/UAE

country pairs where no asymmetric spillovers.

The conflicting results of the many studies mentioned in the literature review above shows the

importance of studying each financial market separately. In the next section the particular

relationship between oil price and Kuwait Stock Market is examined using Markov Switching

Model to investigate the regime shifts between Low- and High volatility regimes analyzing

monthly data from January 2005 to September 2015

7

3. Methodology and Data:

This section use Johansen (1988) and Johansen and Juselius (1990) co-integration analysis to

determine the long-run relationship of non-stationary variables series, whether these variables are

co-integrated or not. In other words, the purpose of this test is to find out if there is long-run

relationship between oil price and Kuwait stock market. In order to apply co-integration series

and to determine the order of integration, we use Augmented Dickey Fuller (ADF) and Pillips-

Perron test.

Equation (1) shows MS-VEC model:

(1)

Where is a -vector of non-stationary I (1) variables and is the constant. p is the order of the

MS-VAR model. matrix contains the long run relationship between variables. The

information on the coefficient PP abmatrix between the levels of the is decomposed as

and where a the relevant elements the matrix are adjustment coefficients

band thematrix contains the cointegrating vectors.

is discrete random variable, which use as indicator for the state, has two possible values, i.e.,

. The unobserved variable is first order Markov-switching process described

in Hamilton (1989). The transition between regimes can be shown in the following matrix

3.1 Data:

This paper explores the dynamic relationship between oil price and Kuwait’s stock market index.

The data are constructed from Bloomberg database for the period from January 2005 to

September 2015.

8

4. Empirical Results:

4.1 Descriptive Statistics:

Table 4.1 presents the descriptive statistics of monthly KUW and OIL in level and logarithms

differences from 2005M01 to 2015M09. Thought the sample period, DLOIL has a mean of 79.44.

It records the highest price in June 2008 with a value of 133.88 synchronizing the Global

financial crisis. However, its lowest price is 39.09 which was recorded in December in the same

year. Its distribution is positively skewed with a value of (0.11) and kurtosis of (2.37). The

standard deviation for oil price is 20.73.

KUW’s average value is 8364.91. Expectedly, it records the highest value in June 2008 with a

value of 15456.20 synchronizing with highest oil price. KUW’s is positively skewed and its

kurtosis is 3.33. It should be noted that both KUW and OIL are not normally distributed.

Table 4.1: Descriptive Statistics:

KUW OIL DLKUW DLOIL

Mean 8364.91 79.44 -0.0010 0.0001

Median 7430.54 78.33 0.0067 0.0127

Maximum 15456.20 133.88 0.1620 0.2041

Minimum 5720.37 39.09 -0.2712 -0.3320

Std. Dev. 2508.80 20.73 0.0566 0.0918

Skewness 1.15 0.11 -0.9040 -1.0422

Kurtosis 3.33 2.37 6.7555 5.0105

Jarque-Bera 28.95 2.35 92.6551 44.7297

Probability 0.00 0.31 0.0000 0.0000

Sum 1079073.00 10247.40 -0.1278 0.0094

Sum Sq. Dev. 806000000.00 55031.13 0.4068 1.0710

Observations 129 129 128 128

9

4.2 Stationary analysis

We transformed all variables into natural logarithm values except NKF due to the negative

observations as the first step. Thus, we tested for unit roots in all variables to determine the order

of integration of these series. We used the Augmented Dickey-Fuller (ADF) test and Phillips-

Perron (PP) test with and without trend as recommended by Engle and Granger (1987).

Table 4.2 shows the result of Augmented Dickey Fuller (ADF) test for all variables. Schwartz

Info Criterion (SIC) is used to determine the optimal length of suitable lags for ADF test.

According to ADF test all variables in levels are either I(1) or I(0). The null hypothesis of a unit

root in the level series cannot be rejected in all variables. In order to achieve stationary, we

differenced the data as a result all variables are unit root rejected in the first differences.

Therefore, all variables are integrated of same order I (1) which we can carry out to estimate the

long run relationship.

Table 4.2 Unit Root Test Applied to Variables:

ADF- Test Phillips-Perron Test

Variables None Constant Constant

& Trend Decision None Constant

Constant

& Trend Decision

LKUW -0.25 -1.65 -3.04 I(1) -0.18 -1.60 -2.93 I(1)

LOIL -0.16 -1.93 -3.14 I(1) -0.15 -2.47 -2.32 I(1)

DLKUW -7.23* -7.20* -7.25* I(0) -7.25* -7.21* -7.28* I(0)

DLOIL -7.67* -7.64* -7.71* I(0) -7.67* -7.64* -7.65* I(0)

*,**,*** indicate significance at 1%,5%,and 10% levels.

4.3 Co-integration test results:

The first step before preforming Johansen cointegration test is to determine the optimum lag

length. Kara (2004) mentioned that there is no unique criterion to select the number of lags.

Therefore, different criteria may suggest a different number of lags. Table 4.3 presents the results

of VAR order selection criteria include Akaike Information Criterion (AIC), Schwarz Bayesian

Information Criterion (SBIC), and Hannan-Quinn (HQ). We choose optimal lag length based on

the HQIC and SBIC criteria with a maximum of two lags.

10

Table 4.3: VAR order-selection criteria

Lag 0 1 2 3 4 5 6 7

AIC 0.473539 -5.05009 -5.37237 -5.40307* -5.36346 -5.31229 -5.24887 -5.21341

HQIC 0.49221 -4.99408 -5.27901* -5.27238 -5.19542 -5.10692 -5.00615 -4.93335

SBIC 0.519507 -4.91219 -5.14253* -5.0813 -4.94975 -4.80665 -4.65129 -4.5239

Table 4.4 reports the results of the unrestricted Johansen cointegration test. According to the

results of cointegration test we conclude that there is cointegration equations existed. The trace

statistic and the maximum eigenvalue are both greater than their critical values in the first rank

which are accepted at 5 percent level of significance. The result shows cointegration the

relationship among the variables which indicate that there is evidence of long run relationship

between oil price and stock market.

Table 4.4: Unrestricted Cointegration Test:

Hypothesized Trace 0.05 Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Statistic Critical Value

None * 0.107955 15.68511 15.49471 14.39407 14.26460

At most 1 0.010194 1.291044 3.841466 1.291044 3.841466 Trace and Max-Eigen test indicates 1 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

4.4 MS-VEC model results:

Table 4.5 shows the regime properties of the model. Two regimes are used to describe the

dynamic interactions between stock and oil prices. First regime (regime 1) in MS-VEC model is

represented to the high volatility state while the second regime (regime 2) represented to high

volatility state. The average probability of long run low volatility regime is equal 0.7054, while

the average probability of long run high volatility regime equal 0.2946. This indicates that low

volatility state is the dominant economical regime. In other words, we expect the low volatility

state to take place on 91 out of 129 observations while high volatility state occurs on 38

occasions. Therefore, the average duration in the low volatility state is 23.6 months whereas 10

months in the high volatility state.

11

Table 4.5: Regime properties

Probability Observations Duration (months)

Regime 1 0.7054 91 23.671

Regime 2 0.2946 38 9.942

We estimate matrix of transition probability to identify the transfer probabilities from one regime

to another. The transition probabilities of MS-VEC model is:

This indicates that the probability of transfer from low volatility regime (regime 1) to high

volatility regime is 0.0422 and from high to low volatility is 0.1006. Therefore, low volatility

regime is more stable than high volatility regime.

Figure 4.1: Smoothed probability estimates of high volatility (regime 2)

0.0

0.2

0.4

0.6

0.8

1.0

05 06 07 08 09 10 11 12 13 14 15

Figure 4.1 presents the smoothed probability estimates of high volatility (regime 2) of the MS-

VEC model in equation (1). The smoothed probability of the high volatility (regime 2) equals or

12

exceeds 0.5 is represented by grey shaded bars. We identify four transition episodes of high

volatility during (2005M04-06M01, 2006M04-06M06, 2008M10- 09M08, 2013M05-13M09).

5. Conclusion:

This paper tries to capture the dynamic relationship between Kuwait’s Stock Market Index and

Oil Price. The study employs Markov Switching Model to inspect the regime shifts between

Low- and High volatility regimes on monthly data from January 2005 to September 2015. The

results suggest that Kuwait’s Stock Market Index reacts differently to changes in Oil Price in

different regimes. In other words, the results show a positive and significant relationship between

Oil Price and Kuwait’s Stock Market Index for the period of high volatility regime while they

show no relationship between these two variables for the period of low volatility regime.

Moreover, low volatility state is the dominant economical regime. In other words, we expect the

low volatility state to take place on 91 out of 129 observations while high volatility state occurs

on 38 occasions. The study also identified four transition episodes of high volatility during

(2005M04-06M01, 2006M04-06M06, 2008M10- 09M08, 2013M05-13M09).

The findings of this study suggests further future research on the relationship between oil price

and stock market returns of each GCC country separately, since the relationship could be

different from one country to another, which could have an important implications for the GCC

countries’ future economic policies and strategies.

13

References:

Abu Zarour, B. (2008) “Wild oil prices, but brave stock markets! The case of GCC stock

markets” Operational Research, 6, pp 145–162.

Akoum I., Graham M., Kivihaho J., Nikkinen J., and Omran M., (2012) “Co-movement of oil

price and stock prices in the GCC region: A wavelet analysis” The Quarterly Review of

Economic and Finance 52, pp 385-394.

Al Janabi,M.,Hatemi-J,A., and Irandoust, M.,(2010) “Anempiricalinvestigationofthe information

efficiency of the GCC equity markets: evidence from bootstrap simulation”

Int.Rev.Financ.Anal.19, pp 47–54.

Arouri M., Lahiani A., and Nguyen D., (2011) “Return and volatility transmission between

world oil prices and stock markets of the GCC countries” Economic Modeling 28, pp 1815-1825.

Arouri, M., and Rault, C., (2012) “Oil prices and stock markets in GCC countries: empirical

evidence from panel analysis” Int. J. Financ. Econ. 17, pp 242-253.

Arouri, M., Fouquau, J., (2009) “On the short-term influence of oil price changes on stock

markets in GCC countries: linear and nonlinear analyses” Economics Bulletin 29, pp 806–815.

Arouri, M., Jouini, J., and Nguyen, D.K., (2012) “On the relationship between world oil prices

andGCC stock markets” J. Quant. Econ. 10, pp 98–120.

Arouri, M., Lahiani, E.-H., and Nguyen, A. D. C. (2011) “Return volatility transmission between

world oil prices and stock markets of GCC countries” Economic Modelling, 28, pp 1815–1825.

Arouri, M.,Lahiani,A., and Nguyen,K.D., (2011) “Return and volatility transmission between oil

prices and stock markets of the GCC countries” Econ. Model. 28, pp1815–1825.

Awartani,B., and Maghyereh, A.I., (2013) “Dynamic spillovers between oil and stock markets in

the Gulf Cooperation Council Countries” Energy Economics 36, pp 28–42.

Balcilar M., and Ozdemir Z., (2013) “The casual nexus between oil prices and equity markets in

the US: A regime switching model” Energy Economics 39, pp 271-282.

Balcilar M., Gupta R., and Miller S., (2015) “Regime switching model of US crude oil and stock

market prices: 1859 to 2013” Energy Economics 49, pp 317-327.

Basher, S.A., Sadorsky, P., (2006) “Oil price risk and emerging stock markets” Global Finance

Journal 17, pp 224–251.

14

Basher, S.A.,Haug, A.A., and Sadorsky, P., (2012) “Oil prices, exchange rates and emerging

stock markets” Energy Economics 34, pp 227–240.

Bjørnland, H.C., (2009) “Oil price shocks and stock market booms in an oil exporting Country”

Scott. J. Polit. Econ. 56 (2), pp 232–254.

Bouri E., (2015) “A broadened casuality in variance approach to assess the risks dynamics

between crude oil prices and the Jordanian stock market” Energy Policy 85, pp 271-279.

Cunado, J., Gracia, F., (2014) “Oil price shocks and stock market returns: evidence for some

European countries” Energy Economics 42, pp 365–377.

Cunado, J., Perez de Garcia, F., (2005) “Oil prices, economic activity and inflation: evidence for

some Asian countries” Quarterly Review of Economics and Finance 45, pp 65–83.

Davis, S.J., Haltiwanger, J., (2001) “Sectoral job creation and destruction responses to oil price

changes” J. Monet. Econ. 48, pp 465–512.

Driesprong, G., Jacobsen, B., Benjiman, M., (2008) “Striking oil: another puzzle?” J. Financ.

Econ. 89, pp 307–327.

Driesprong, G.,Jacobsen,B., and Matt,B., (2008) “Striking oil: another puzzle” J. Financ. Econ. 2,

pp 307–327.

El-Sharif, I., Brown, D., Burton, B., Nixon, B., Russell, A., (2005) “Evidence on the nature and

extent of the relationship between oil prices and equity values in the UK” Energy Economics 27,

pp 819–830.

Engle, R., and C. Granger (1987), “Co-Integration and Error-Correction: Representation,

Estimation, and Testing,” Econometrica, 55, pp. 251–76.

Faff, R., Brailsford, T., (2000) “A test of a two-factor ‘market and oil’ pricing model” Pac.

Account. Rev. 12 (1), pp 61–77.

Fayyad, A., and Daly, K., (2011) “The impact of oil price shocks on stock market returns:

comparing GCC countries with the UK and USA” Emerging Markets Review 12(1), pp 61–78.

Filis, G.,Degiannakis,S., and Floros,C.,(2011) “Dynamic correlation between stock market and

oil prices: the case of oil-importing and oil-exporting countries” Int. Rev. Financ. Anal.20, pp

152–164.

Ha Le, T., Chang Y., (2015) “Effects of oil price shocks on the stock market performance: Do

nature of shocks and economies matter?” Energy Economics 51, pp 261-274.

15

Hamilton J.D., (1989), “A New Approach to the Economic Analysis of Nonstationary

Time Series and the Business Cycle” , Journal of Econometrica, 57 , pp.357-384.

Hamilton J.D., (1990), “Analysis of Time Series Subject to Changes in Regime”, Journal

of Econometrics, 45 , pp.39–70.

Hamilton, J.D., Herrera, M.A., (2002) “Oil shocks and aggregate macroeconomic behavior”,J.

Money, Credit, Bank. 36, pp 265–286.

Hammoudeh, S., and Choi, K. (2006) “Behavior of GCC stock markets and impacts of US oil

and financial markets” Research in International Business and Finance, 20, pp 22–44.

Hammoudeh, S., and Choi, K., (2006) “Behavior of GCC stock markets and impacts of US oil

and financial markets” Res. Int. Bus. Financ. 20(1), pp 22–44.

Hooker, M.A., (2002) “Are oil shocks inflationary? Asymmetric and nonlinear specifications

versus changes in regime” J. Money, Credit, Bank. 34, pp 540–561.

Johansen S. and K. Juselius, (1990) “Maximum Likelihood Estimation and Inference on

Cointegration, with Application to the Demand for Money”, Oxford Bulletin of Economics and

Statistics, Vol. 52 (may), pp. 169-210.

Johansen S. and K. Juselius, (1992) “Testing Structural Hypotheses in a Multivariate

Cointegration Analysis of the PPP and the UIP for UK,” Journal of Econometrics, Vol. 53

(July/September), pp. 211–44.

Johansen, S., (1988) “Statistical Analysis of Cointegration Vectors”, Journal of Economic

Dynamics and Control, Vol. 2 (June-September), pp. 231-54.

Jones, D.W., Leiby, P.N., Paik, I.K., (2004) “Oil price shocks and the macroeconomy: what has

been learned since 1996” Energy J. 25, pp 1–32.

Jouini J. and Harrathi N., (2014) “Revisiting the shock and volatility transmissions among GCC

stock and oil markets: A further investigation” Economic Modelling 38, pp 486-494.

Kilian, L., (2008) “Exogenous oil supply shocks: how big are they and how much do they matter

for the US economy?” Review of Economics and Statistics 90, pp 216–240.

Kilian, L., Park, C., (2009) “The impact of oil price shocks on the US stock market” Int. Econ.

Rev. 50 (4), pp 1267–1287.

Lee, K., Ni, S., (2002) “On the dynamic effects of oil shocks: a study using industry level data” J.

Monet. Econ. 49, pp 823–852.

16

Lescaroux, F., Mignon, V., (2008) “On the influence of oil prices on economic activity and other

macroeconomic and financial variables” OPEC Energy Review 32, pp 343–380.

Maghyereh, A., and Al-Kandari, A. (2007) “Oil prices and stock markets in GCC countries: New

evidence from nonlinear cointegration analysis” Managerial Finance 33(7), pp 449–460.

Maghyereh, M., Al-Kandari, A., (2007) “Oil prices and stock markets in GCC countries: new

evidence from nonlinear cointegration analysis” Managerial Finance 33, pp 449–460.

Malik, F., and Hammoudeh, S., (2007) “Shock and volatility transmission in the oil, US and Gulf

equity markets” Int. Rev. Econ. Financ. 16(3), pp 357–368.

Malik, F., Ewing, B.T., (2009) “Volatility transmission between oil prices and equity sector

Returns” International Review of Financial Analysis 18, pp 95–100.

Miller, J.I., and Ratti, R. A.,(2009) “Crude oil and stock markets: stability, instability, and

bubbles” Energy Economics 31(4), pp 559–568.

Mohanty, S. K., Nandha, M., Turkistani, A. Q., and Alaitani, M. Y. (2011) “Oil price movements

and stock market returns: Evidence from Gulf Cooperation Council (GCC) countries” Global

Finance Journal, 22, pp 42–55.

Mohanty, S.K., Nandha ,M., Turkistani, A.Q., and Alaitani,M.Y.,(2011) Oil price movements

and stock market returns: evidence from Gulf Cooperation Council (GCC) countries” Glob.

Financ. J. 22(1),pp 42–55.

Naifar N. and Al Dohaiman M., (2013) “Nonliner analysis among crude oil prices, stock

markets’ return and macroeconomic variables” International Review of Economic and Finance

27, pp 416-431.

Nguyen, C. C., and Bhatti, M. I. (2012) “Copula model dependency between oil prices and stock

markets: Evidence from China and Vietnam” Journal of International Financial Markets,

Institutions and Money, 22(4), pp 758–773.

Papapetrou, E., (2001) “Oil price shocks, stock market, economic activity and employment in

Greece” Energy Economics 23, pp 511–532.

Pollet, J.M., (2005) “Predicting asset returns with expected oil price changes”, Available at

SSRN:. http://ssrn.com/abstract=722201.

Qinbin, F., Mohammad, R., and Parvar, J. (2012) “U.S. industry-level returns and oil prices”

International Review of Economics and Finance, 22, pp 112–128.

17

Sadorsky, P., (2001) “Risk factors in stock returns of Canadian oil and gas companies” Energy

Economics 23, pp 17–28.

Sedik, T. S., and Willams, O. H. (2011) “Global and regional spillovers to GCC equity markets”

IMF working paper, W/11/138.

Wang, Y., Wu, C., Yang, L., (2013) “Oil price shocks and stock market activities: evidence from

oil-importing and oil-exporting countries” J. Comp. Econ. 41 (4), pp 1220–1239.

Zarour, B.A., (2006) “Wild oil prices, but brave stock markets! The case of GCC stock Markets”

Oper. Res. 6, pp 145–162.