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"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.
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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
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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
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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.
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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
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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
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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.
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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
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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.
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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.
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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
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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.
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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.