Transmission of Stock Returns and Volatility among GCC Stock...

Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)

An Online International Research Journal (ISSN: 2306-367X)

2015 Vol: 4 Issue: 2

1631 www.globalbizresearch.org

Transmission of Stock Returns and Volatility among GCC Stock

Markets: A Case Study (2001-2006)

Sami Alabdulwahab, Assistant Professor of Economics,

School of Business Administration,

Al Akhawayn University, Morocco.

Email: [email protected]

_____________________________________________________________________

Abstract

This study examines return volatility and the mechanism of transmission among GCC stock

markets. In all cases, there is a significant interaction between the Saudi and Kuwait stock

markets in their second moments. The Saudi stock market transmits its returns volatility to

the Bahrain and Dubai stock markets. Since these stock markets are functioning in countries

that follow well-developed financial system. Investors in these countries are assumed to be

aware about the markets’ news with clarity and they implement it through asset prices. Only

the Dubai stock market has clear direction of the volatility spillover, which is indirectly

affected by Saudi stock market news, variance, and by direct variance volatility spillover.

___________________________________________________________________________

Key Words: Stock Markets; GCC Countries; Return Spillover

JEL classifications: C31; G11; G15

mailto:[email protected]





1. Introduction

Transmission of return volatility is widely used when investors deal with different

markets1. The return volatility is important for investors to see how different markets transfer

this volatility among the others. The importance of analyzing the return volatility helps

investors make decisions about portfolio allocation, especially in regard to portfolio

diversity2.

GCC countries are aiming to integrate their economies to reach a form of currency union

thus will develop financial integration respectively. GCC countries stock markets are open at

the same time and share similar holidays3. Also, volatility in oil markets can also influence

stock volatility in GCC stock markets4. For example, political instability in Nigeria would not

affect GCC countries directly, but indirectly would affect them through oil price movements

thus affects their government budgets’ revenue that highly depends on oil exporting5.

But GCC financial markets also have important differences. Some of these countries have

liberal stock markets whereas others have closed stock markets. A closed stock market is one

in which non-citizens of these countries cannot invest directly6. On the other hand, other

GCC countries have their stock market open for both citizens and non-citizens7. Furthermore,

the Saudi stock market has two trading sessions whereas the others have one trading session8.

Also, some GCC countries heavily depend on oil revenue, which dominates the whole

economy. Furthermore, some of these stock markets are large compared to other stock

markets in GCC countries9. On the other hand, the others depend on financial development10,

1 Percival Hurditt (2006). 2 King and Wadhwani (1990), G. Andrew Karolyi (1995), and Lin, Engle, and Ito (1994). 3 Some studies try to examine time zone differences among stock markets and analyze the effect of this

difference. Lin, Engle, and Ito (1994) use two methods to analyze international transmission between

Tokyo and New York stock markets which communicate through overnight information. Specifically,

the researchers utilize aggregate shock and signal-extraction models. They estimate the implications of

these models and compare them by using inter-day overnight data. Also, they compare their results

with (GARCH) in-mean model results found by Hamoao, Masulis, and Ng (1990). These researchers

found that returns between Tokyo and New York are bi-directional. Furthermore, the researchers

found little evidence of return spillover lags from New York daytime to Tokyo daytime or vice versa. 4 Malik and Hammoudah (2005). 5 Alkhabeer report, August 2014. 6 Non-citizen investors can invest directly through mutual funds. 7 Qatar, Bahrain, and UEA (both Abu Dhabi and Dubai markets). 8 The stock market decided to have one trading session started from September 2, 2012. 9 Saudi and Kuwaiti stock markets are large compared to other GCC countries’ stock markets. Saudi’s

stock market value is 660 billion dollars, while Kuwait’s stock market value is 140 billion dollars, Abu

Dhabi & Dubai combined is 234.4 billion dollars, Qatar is 41 billion dollars, and Bahrain is 14 billion

dollars. *Middle East Online* 10 Saudi Arabia and Kuwait depend mostly on oil, Qatar depends on natural gas, and Bahrain and UAE

rely more on financial institutions.





as shown in Figure 1. These differences11 make the study more important as it seeks to

explain the effect of the movements in one stock market on the other stock markets.

The importance of this study is to show investors how stock markets in GCC countries

react with each other in order to identify which strategies can be used to hedge risks in the

same investment zone. Furthermore, I will test if there is a hedging possibility among GCC

stock markets by testing the significantly of the conditional heteroskedasticity. If the

conditional heteroskedasticity is significant this declares the possibility of hedging between

these two markets.

Figure 1: Oil Revenue as a Portion of the Total Revenue

This study will examine volatility transmission in GCC markets. Precisely, it will focus

on the return volatility transmission among GCC countries’ stock markets between 2001 and

2006. I chose this period to cover the last oil boom and that contains high trade volume. This

study will be organized as follows. Section 2 is reviewing the literature. Section 3 is brief

view on GCC countries. Section 4 is the methodology of the study. Section 5 is the data and

data statistic. Section 6 is the result. Section 7 is conclusion.

11 Open on different days. For example, the Dubai Financial Market is open on Thursday whereas the

Kuwait Exchange Market is closed on Thursday. The Saudi Stock Market (Tadawul) opens two

sessions and the rest of the GCC Financial Markets open one session. The Kuwait Exchange Market

has had a margin tool for a long time whereas the Saudi Stock market recently introduced that tool, etc.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1980

1982

1984

1986

1988

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

(OilR/TotalR)SA

(OilR/TotalR)KW

(OilR/TotalR)UAE

(OilR/TotalR)BHA

(OilR/TotalR)QAT





2. Literature review

Many studies in volatility transmission focus on emerging markets such as East Asia’s

stock markets12. Oskooe (2010) investigated the linkage between the economic growth and

stock prices fluctuations in Iran. He found that there is a long run relationship between

economic growth and stock prices. Furthermore, there is bilateral raltionship between the

economic growth and stock prices in the short run. Other studies address volatility

transmission in developed countries’ stock markets, as seen in Pan and Hsueh (1998) and

Karolyi (1995)13. Also, some studies for example, King and Wadhwani (1989) focus on the

transmission where it can be spread to other markets. U.S. stock markets have been the focus

of most of these volatility spillover studies. King and Wadhwani (1989) discuss the October

1987 crash in the U.S. stock markets. They use a contagion model to examine correlations

among the major stock markets prior to the crash. The results of their investigation, including

the finding that the U.S. markets had reached their peaks at the time of the crash, suggest that

portfolio allocation may have caused the crash14.

Eun and Shim (1989) used a VAR model to study transmission among connected stock

markets. They found that no foreign market can determine U.S. market movements.

Additionally, trade linkages between two economies make their equity markets correlated.

This correlation leads to volatility spillover between financial markets across countries.

On the other hand, many studies address the return and volatility transmissions among

stock markets from different perspectives15. These studies discuss issues of different type of

markets (i.e., future prices and options markets). Furthermore, return volatility can be

transferred between two stock indices where these indices are traded in future markets also.

Some studies produce little evidence to support the hypothesis that states that domestic

markets capably adjust to foreign information16. For example, Karolyi (1995) uses a

multivariate GARCH model to analyze the daily return for NYSE and TSE 300 stock indices.

Karolyi’s study follows the structure of short-run dynamics of return and volatility for stocks

traded on TSE 300 and the NYSE for the period from 1981 and 1989. Also, the study uses a

bivariate GARCH model to analyze the joint process governing S&P 500 and TSE 300. Fry,

Hocking and Martin (2008), analyze the domestic and foreign equity shocks on Australian

economy. They found the real value of the equity undervalued by 19% by June 2005.

Furthermore, foreign crisis explained most of the distortion the Australian economy.

12

Hamao, Masulis, and Ng (1990), King and Wadhwani (1990), and Engle and Susmel (1993). 13 Eun and Shim (1989). 14 The inventories went to short positions in the U.S. market and moved to others in long positions. 15 Kaminsky and Schmuker (2002), Islam (2003), and Karolyi and Martell (2005). 16 Bae and Karolyi (1994) and Lin, Engle, and Ito (1994).





Pan and Hsueh (1998) address the return transmission between the U.S. and Japan. They

used a two-step GARCH method and found that volatility goes from the U.S. to Japan.

Furthermore, Li and Giles (2013) study the linkage between US, Japan and six Asian

countries. They used asymmetric multivariate GARCH model and found a significant

unidirectional shock and volatility spillovers from the U.S. market to the Japanese and the

Asian emerging markets. Also, they found that volatility and spillovers between US and

Asian emerging markets are stronger and predictable during Asian financial crisis.

Dontis-Charitos , Elyasiani and Staikouras (2015) examine the interdependence among

U.S., UK, UE and Japanese banking as well as insurance industries. They used VAR-BEKK

multivariate-GARCH and found the linkage across banking sectors is strong in both spillover

and returns level especially during the financial crisis between 2007 and 2009. Furthermore,

they proved that U.S. emerge the main provider of the volatility in the banking sector. On the

other hand, the integration among U.S., UK, UE banks is stronger than to be with Japanese

banks. Also, U.S. and UK insurance markets have bidirectional return and volatility contagion

that has a stronger tie during the financial crisis.

In recent studies, like that of Hammoudeh and Chio (2004), some economists investigate

GCC countries’ stock market behavior. They discuss the volatility regime switching and

linkage among these stock markets. Hedi Arouri, Lahiani and Nguyen (2011), examine the

returns linkage and volatility transmutation between GCC stoke markets and world oil market

between the period of 2005 and 2010. They used VAR-GARCH to find that there is a

substantial return and volatility spillovers between world oil prices and GCC stock markets.

However, the literature has lake of studies that focusing on GCC stock market return

spillover among each other. I do realize the importance of studying the return volatility

among stock markets which will help in portfolio diversification especially in growing

markets like GCC stock markets.

3. GCC Countries

GCC stock markets are steadily growing, in some years experiencing growth of over

100%, in part due to the recent increase in oil prices. The performance of most markets

started increasing rapidly in 2003, as shown in Figure 2. These performance increases can be

explained by the increase in oil prices. Kuwait’s stock market increased on average 54%.

Saudi’s stock market increased 55%. Dubai’s stock market rose on average 78%. Bahrain’s

stock market rose 17%. Qatar’s stock market has increased by 59%.





Figure 2: GCC Stock Markets Performance

Table 1: Saudi Statistics

The variable 1970 1980 1990 2000 2010 2012

POP 5745000 9392000 15803000 20723150 27563000 29196000

Oil R/GDP 0.32 0.58 0.56 0.37 0.75 0.86

GDP 5.14 164.37 161.77 188.69 284.25 326.55

*The GDP value in billion dollars

*Data from SAMA and WB CD-ROM

Table 2: Kuwait Statistics

The variable 1970 1980 1990 2000 2010 2012

POP 744000 1375000 2125000 2190000 3582000 3785000

OilR/GDP 0.22 0.66 0.34 0.57 0.84 0.82

GDP 2.87 28.03 18.4.2 37.0 61.37 69.28


*Data from WB CD-ROM and IMF CD-ROM

Table 3: United Arab of Emirates Statistics

The variable 1970 1980 1990 2000 2010 2012

POP 220000 1043000 1773000 3247000 8264000 8768000

OilR/GDP N/A 0.66 0.48 0.30 0.67 0.82

GDP N/A 29.6 34.1 70.3 257.7 279.4



Table 4: Bahrain Statistics

The variable 1970 1980 1990 2000 2010 2012

POP 220000 350000 490000 670000 1107000 1151000

OilR/GDP 0.14

0.39

0.65

0.53

0.83 0.87

GDP 0.34 3.07 4.53 7.97 25.62 27.04



-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2001 2002 2003 2004 2005

KWI

SAI

DUI

BAI

QAT





Table 5: Qatar Statistics

The variable 1970 1980 1990 2000 2010 2012

POP 110000 230000 450000 610000 1637000 1837000

OilR/GDP 0.36 0.58

0.36

0.35

0.48 0.69

GDP 0.53 7.83 7.36 17.76 78.08 93.7

*The GDP value in billion dollars.


Historically, the stock markets of the GCC had not been interesting markets until oil prices

started to increase in 2002. This had a dramatic impact on the markets of the GCC,

eventually leading to record annual yields of over 120% for the years 2003 and 2004.

Additionally, there were new record highs in 2005. This study will cover the performance of

the stock markets of Saudi Arabia, Kuwait, Dubai, Bahrain, and Qatar. These five markets

were chosen in light of the enormous differences among them in size, dependence on oil, and

trade conditions. I avoided the stock market of Oman for two reasons. First, Oman’s decision

to be out of GCC Currency Union17. Second, Oman stock market’s capitalization over the

period of the study was small relatively to the rest of the GCC stock markets18.

4. Methodology

In the literature, there are many models that can be used to analyze volatility transmission.

The most frequently used model for examining volatility is the GARCH model. Extensions

of GARCH have been developed into multivariate analyses (Multivariate, or MGARCH), and

are used for analyzing volatility spillover across different stock markets. MGARCH has been

used by Kearney and Patton (2000) to study volatility transmission among different exchange

rates in the European Monetary System. Also, other researchers such as Ewing, Malik and

Ozfidan (2002) use the same model to suggest that volatility spillover exists between oil and

natural gas markets in the U.S.

Regarding GCC’s stock markets, some studies have examined shock and volatility

transmission across the U.S. and the GCC’s oil and equity markets (Malik and Hammoudah,

2005). These studies have found that there is significant transmission across these markets.

Furthermore, all of GCC’s stock markets, with the exception of Saudi Arabia’s, receive

volatility from the oil market. In performing this study, Malik and Hammoudah (2005) use a

multivariate GARCH model to estimate the mean and conditional variances simultaneously.

They use daily returns for the markets. Also, Malik and Hammoudah use Baba, Engle, Kraft

and Kroner’s (1990) specification of the multivariate GARCH model so that the model does

not impose a restriction upon the constant correlation among the variables over time. In this

study, I use the same method since it has the property that I need in order to analyze the return

17 Date of Oman decision to be out of the currency union. 18 Gulf Investment Corporation GCC Economic Statistics Tenth Edition December 2011.





volatility among the stock markets of the GCC19. Furthermore, this model can provide the

conditional heteroskedasticity which is needed in order to make decision toward the hedging

between stock markets. Alos, this model has been widely used to investigate returns’

volatility spillover among markets. Support of these models began with Engle’s (1982) use of

the ARCH model. The GARCH model has also been utilized by Bollerslev (1986) to

examine the weighted average of all past squared returns following the ARMA (1, 1) process

rather than Engle’s (1982), in which he uses weighted average returns that follow an AR ( p ).

The problem with the two models is that they do not take asymmetric effects into account,

which may cause some distortion in the results. Thus, EGARCH was utilized by Nelson

(1991) as a model that identifies asymmetric effects. In examination of volatility

transmission, there are several models that can be used that originate from the GARCH

model20. In this study I will start the analysis by testing for the existence of autoregressive

conditional heteroscedasticity (ARCH). For testing, I will use Engle’s (1982) equation:

ittiiti RR 1,, (1)

Here, tiR , is the return on index i between time 1t and ,t

i is a drift coefficient, and

it is the error term for index i at time t 21. For analyzing return volatility, there are two

well-known approaches using multivariate GARCH (MGARCH). The first approach is the

VECH model that was introduced by Bollerslev and Wooldridge (1988). The following

equation shows Bollerslev and Wooldridge’s model:

p

j

jtjtj

q

j

jtjt vechAHvechBAHvech11

0 )()()( (2)

Here, ).,0(~,2/1 IiidNH tttt The )( tXvech notation stacks the columns on and

below the diagonal of the symmetric matrices tX and tH ; this represents the conditional

variance matrix. Simplifying assumptions are needed to reduce the number of estimated

elements. The largest problem with this method is ensuring the positivity of all elements

when estimating the model so as to ensure a positive semi-definite covariance matrix.

To avoid the semi-definite covariance matrix issue, I will use Baba, Engle, Kraft and Korner’s

(1990) model (BEKK). This model uses the quadratic formula, which ensures that the

covariance matrix will be semi-definite positive.

The model of BEKK for multivariate GARCH (1, 1) is as follows:

19 The property that I am seeking here is the direct and indirect spillover among the markets. 20 The previously used constant correlation model is not appropriate here; Sheady (1997), Longin, and

Solnik (1995) contend that the assumption of constant correlation among return variables is unsteady in

these markets.

21 The test statistic is distributed as 2 with x degrees of freedom, where x equals the number of

restrictions.





BHBAACCH tttt 1 (3)

The matrices in equation (1.3) are as follows:

333231

232221

131211

aaa

aaa

aaa

A

333231

232221

131211

bbb

bbb

bbb

B

333231

2221

11

0

00

ccc

cc

c

C (4)

In this model, the C matrix is 3 X 3, and the lower triangle has six parameters. The A

matrix is a 3 X 3 matrix of parameters. The A matrix has conditional variances that are

correlated with past squared errors. Furthermore, the elements in the matrix can capture the

effects of shocks or unanticipated events on conditional variances. The B matrix is a 3 X 3

matrix as well and has parameters that can represent how current levels of conditional

variances are affected by past conditional variances. This model has 24 estimated elements

for the variance and is trivariate.

The expanded equations for the conditional variance for the trivariate GARCH (1, 1) are

as follows:

ttttt

ttttttttttt

hbhbbhbhbbhbb

hbaaaaaaaaaah

,33

2

32,233121,33

2

21,131211,121211

,11

2

11

2

,3

2

31,3,23121

2

,2

2

21,3,13111,2,11211

2

,1

2

111,11

222

222

(5)

ttttt

ttttttttttt

hbhbbhbhbbhbb

hbaaaaaaaaaah

,33

2

32,233122,22

2

22,133212,122212

,11

2

12

2

,3

2

32,3,23222

2

,2

2

22,3,13212,2,12221

2

,1

2

211,22

222

222

(6)

ttttt

ttttttttttt

hbhbbhbhbbhbb

hbaaaaaaaaaah

,33

2

33,233323,22

2

23,133313,122313

,11

2

13

2

,3

2

33,3,23323

2

,2

2

23,3,13313,2,12313

2

,1

2

131,33

222

222

(7)

These equations represent the shocks and the volatility transmission through the markets

over time22. The 2211,hh and 33h represent conditional variances for market return for the

first, second, and third asset. The other important components are2

,1 t , t,1 t,2 , tt ,3,1 ,2

,2 t ,

tt ,3,2 , and 2

,3 t . They represent the variances for each market and the effect of variances

between markets.

I will estimate three models. Each model will contain the largest two markets (i.e., Saudi

Arabia and Kuwait) and one of the smaller stock markets in the GCC (i.e., Qatar, Dubai, and

Bahrain)23.

22 To calculate the standard error of the coefficients terms, first order Taylor expansions around the

mean will be used. Kearney and Patton (2000) give details of this method. 23 The problem of estimating all the markets at one time is exceeding the limit of the multivariate

GARCH. The model contains more than three variables and will suffer a non-convergence issue. This

issue is unresolved in the literature.





The ,2

,1

2

11 ta 2

,2

2

22 ta , and 2

,3

2

33 ta represent the direct unanticipated events within a single

market on the future conditional variance of that market24. Furthermore, ,2

,2

2

21 ta ,2

,3

2

31 ta

,2

,1

2

12 ta ,2

,3

2

32 ta 2

,1

2

13 ta , and 2

,2

2

23 ta represent the direct unanticipated events of other markets

on the future conditional variance of another market25. Also, ,2 ,2,11211 ttaa ,2 ,3,13111 ttaa

,2 ,2,12212 ttaa ,2 ,3,23222 ttaa ttaa ,3,133132 , and ttaa ,3,233232 represent the unanticipated

events that can occur across two markets that have an effect on one of these two markets

directly26. On the other hand, ,2 ,3,23121 ttaa ttaa ,3,132122 , and ttaa ,2,123132 represent the

unanticipated events that can occur across two markets which can indirectly affect the other

market27. In volatility spillover, ,,11

2

11 thb thb ,22

2

22 , and thb ,33

2

33 represent the volatility spillover

between the previous conditional variance of the market and its future conditional variance.

Furthermore, ,2 ,121211 thbb ,2 ,133111 thbb ,2 ,122212 thbb ,2 ,233222 thbb thbb ,1333132 and thbb ,2333232

represent the volatility spillover of the conditional variance between two markets and the

effect of this volatility on one of their future conditional variances28. Also, ,2 ,233121 thbb

thbb ,1332122 and thbb ,1223132 represent the volatility spillover of conditional variance in two

markets that can affect the conditional variance of the other market indirectly29. In addition,

,,22

2

21 thb ,,33

2

31 thb ,,11

2

12 thb ,,33

2

32 thb thb ,11

2

13 and thb ,22

2

23 represent the volatility spillover of the

conditional variance of one market to the other market directly30. The last component is the

most important to direct volatility spillover among markets.

I assume that the error terms are normally distributed and can be represented by the

following maximum likelihood function:

ttt

T

t

t HHTL 1

1

(ln2

1)2ln()(

) (1.8)

24 For example, unanticipated events affecting Saudi Arabia’s stock market can affect its conditional

future variance. 25 For example, unanticipated events of Kuwait’s stock market can affect the conditional variance of

Saudi Arabia’s stock market. 26 For example, unanticipated events affecting both Saudi Arabia’s stock market and Bahrain’s stock

market can affect conditional variance for Saudi Arabia’s stock market. 27 For example, unanticipated events affecting Saudi Arabia’s stock market and Kuwait’s stock market

can affect Bahrain’s stock market indirectly. 28 For example, volatility of conditional variance across Saudi Arabia’s stock market and Dubai’s stock

market can affect Dubai’s stock market. 29 For example, volatility of conditional variance across Kuwait’s stock market and Dubai’s stock

market can affect conditional variance of Qatar’s stock market indirectly. 30 For example, volatility of the conditional variance for Saudi Arabia’s stock market can affect the

conditional variance of Kuwait’s stock market directly.





The represents the estimated parameter vector. T is the number of observations that is

going to be used to estimate the parameters. Furthermore, the iteration method uses a simplex

algorithm that has been recommended by Engle and Kroner (1995). The BFGS algorithm has

been used to obtain the final estimate of the variance-covariance matrix with corresponding

standard errors. When I start estimating, I will use BFGS numerical algorithm as in Engle and

Kroner (1995).

5. Data and Data statistic

5.1 Data

The data for all stock markets are daily closing data for the index of each market. The data

has been collected from the stock market database of each stock market. The data comes

from the Saudi Financial Market (Tadawul), Kuwait Stock Exchange (KSE), Dubai Financial

Market (DFM), Doha Stock Exchange (DSE), and Bahrain Stock Exchange (BSE). Data will

cover the period between 2001 and 2006.

5.2 Data Statistic

The analysis will cover first difference for the log form of the series31. Bahrain and

Kuwait stock market return volatility is very low compared to other GCC stock markets and

the oil market. Qatar and Bahrain stock markets show a more positive skewness return than

other markets in the study. The positive skewness indicates that investors are asking for small

return when the market is up due to the investors’ confidence in this market that any future

declines will not result in serious negative skewness. From the kurtosis, we can see that it is

appropriate to use the GARCH32.

The average of daily return with no exception for all markets under study is positive which

can be seen in table 6. Furthermore, the highest average daily return is Qatar stock market that

has 0.16% daily. This average over the period between 2001 and 2006. Also, Saudi stock

market has the highest return as daily return among those markets under study at rate of

8.10% with Stander Deviation 0.01.

Table 6: Descriptive statistic for daily returns in GCC stock markets and oil market

Bahrain Kuwait UAE

(Dubai)

Qatar Saudi Oil

Mean 0.000796 0.001382 0.001498 0.001631 0.001481 0.000533

Maximum 0.030712 0.038464 0.067870 0.058151 0.081045 0.103678

Minimum -0.023129 -0.038173 -0.084913 -0.047625 -0.067456 -0.223644

Std.Dev. 0.005391 0.007988 0.012435 0.012013 0.010815 0.024954

Skewness 0.562750 -0.547917 -0.388968 0.040105 -0.431963 -1.286316

Kurtosis 7.72 6.87 11.91 6.75 11.44 12.54

Jarque-Bera 866.65* 615.39* 3656.87* 532.92* 3381.72* 3235.37*

* all values are significant at 1% level

31 Taking the first different of the series in its log form indicates the return. 32 That model is suggested by Bollerslev (1986).





The correlation among the whole series is low due to lack of information among these

markets. The most interesting result here is that the Kuwait stock market return has a

negative relationship with oil market’s return even though Kuwait is important oil producer.

Overall, Bahrain’s markets return is the highest correlated in the GCC stock markets with

Kuwait and Qatar. Also, Dubai’s stock market has a negative relationship with all markets

except Saudi and the oil markets’ return. All other correlation values are stated in table 7. I

include the oil return in the analysis just to show how these markets are sensitive to oil market

behavior. I will not use oil market’s return elsewhere in this study.

Table 7: The return correlations among stock markets and oil price

Bahrain Kuwait UAE (Dubai) Qatar Saudi Oil

Bahrain 1.000

Kuwait 0.105 1.000

UAE

(Dubai)

-0.049 -0.013 1.000

Qatar 0.108 -0.017 -0.015 1.000

Saudi -0.010 0.0728 0.072 -0.010 1.000

Oil 0.050 -0.012 0.016 -0.023 0.019 1.000

5.3 Result

The multivariate GARCH model has been estimated by using BEKK parameterizations.

From table 8 to 10, all markets have been affected by their own shocks and other markets’

shocks. This result is consistent with other research in this area33. This result is a reduced

form model in which all numbers in the table are coefficients for the parameters. From table

8, we can see that the shocks or news from the Bahrain stock market indirectly affects the

Saudi market. Also, the direct shocks seem to be a little less effective than the indirect one

where the direct shock counts for 0.10 and the indirect counts for 0.41. Furthermore, the

direct effect of news from the Kuwait stock market has double the significance than the Saudi

stock market news that Bahrain stock market receives. The Saudi stock market in this model

is more sensitive to the volatility indirectly from Kuwait and Bahrain stock markets. There is

strong possibility for Bahrain investors to hedge by going short in the Kuwait stock market

and long in the Bahrain stock market since th ,23 is significant. This implies that investors can

go short in the Bahrain stock market with a portion of their investment indicted by the

model34. By using Kroner and Sultan (1993), we can minimize the risk of the portfolio

investment in Bahrain stock market by using this method22

23*

h

h . For example, if you go

33 Malik and Hammoudeh (2005) and Hammoudeh, Dibooglu and Aleisa (2004). 34 Short sell is a term widely used in a developed stock market that means that a stock investor can sell

stock that he or she does not hold by borrowing them from his or her stock dealer. This strategy can

help investors to benefit from market decaling and hedge from market decaling, too.





long by $1000 in Bahrain stock market, you should go short by $8 in Kuwait stock market

according to this model.

Table 8: Trivariate GARCH model for Saudi, Kuwait and Bahrain stock markets

Independent

variable 1,11 th 1,22 th 1,33 th

2

,1 t 0.398878 (17.14) 0.008406 (1.40) 0.00226 (3.12)

tt ,2,1 -0.00917 (-0.25) -0.01924 (-2.85) -0.00828 (-3.76)

tt ,3,1 0.41743 (8.18) -0.00849 (-2.59) 0.04841 (6.64)

2

,2 t 0.000526 (0.12) 0.11008 (11.85) 0.00759 (2.15)

tt ,3,2 -0.00480 (-0.26) 0.09712 (5.43) -0.0887 (-4.16)

2

,3 t 0.10924 (4.36) 0.02142 (3.09) 0.25956 (12.57)

th ,11 0.45446 (35.62) 0.000934 (0.98) 0.00633 (7.64)

th ,12 0.13738 (6.50) 0.01706 (1.95) -0.01219 (-7.72)

th ,13 -0.21305 (-4.48) -0.00221 (-1.66) -0.11055 (-13.29)

th ,22 0.01038 (3.38) 0.77810 (94.36) 0.00587 (4.13)

th ,23 -0.03220 (-3.88) -0.20204 (-5.90) 0.10645 (7.94)

th ,33 0.02597 (2.31) 0.01312 (2.94) 0.48277 (35.73)

Notes: h11 represents the conditional variance for Saudi stock market return, h22 represents the

conditional variance for Kuwait stock market return and h33 represents the conditional variance for

Bahrain stock market return. BEKK parameterization has been used for the multivariate GARCH. For

simplicity the estimated mean equation and constants of each variance are not reported for writing

shortness.

From table 9, we can see that the Qatar stock market is more likely affected by the Kuwait

market’s indirect news or shocks. Also, this model is different now by adding the Qatar stock

market where indirect news or shocks from the Kuwait and Qatar stock markets have an

effect on Saudi stock market’s variance volatility. The most interesting result here is that the

Qatar market seems to be a favorable market for Saudi stock market investors for hedging.

Saudi stock market investors should go short in the Qatar stock market by $31 when they go

long in the Saudi stock market by $1000.

Table 9: Trivariate GARCH model for Saudi, Kuwait and Qatar stock markets

Independent


2

,1 t 0.12119 (12.43) 0.00109 (0.97) 0.0000761 (0.04)

tt ,2,1 -0.11649 (-4.25) 0.02537 (1.95) 0.0034103 (0.09)

tt ,3,1 -0.06579 (-3.69) 0.00285 (1.67) -0.00405 (-0.09)

2

,2 t 0.02799 (2.12) 0.14714 (14.109) 0.00382 (1.28)

tt ,3,2 0.03162 (2.93) 0.03306 (3.49) -0.09069 (-2.53)

2

,3 t 0.00893 (1.85) 0.00185 (1.74) 0.53859 (18.56)





th ,11 0.64836 (105.65) 0.03480 (12.48) 0.00429 (1.64)

th ,12 -1.00290 (-23.39) 0.29905 (22.66) -0.00321 (-3.57)

th ,13 -0.11186 (-2.34) -0.01436 (-6.67) 0.09666 (3.25)

th ,22 0.38783 (12.78) 0.64243 (86.21) 0.000599 (1.19)

th ,23 0.08652 (2.35) -0.06170 (-6.89) -0.03613 (-2.38)

th ,33 0.00483 (1.16) 0.00148 (3.49) 0.54448 (44.28)


conditional variance for Kuwait stock market return, and h33 represents the conditional variance for

Qatar stock market return. BEKK parameterization has been used for the multivariate GARCH. For


shortness.

From table 10, the Dubai stock market is only affected by indirect news from the Saudi

stock market. This may due to the funds that Saudi investors transfer to Dubai since Dubai is

the financial center for GCC countries. Furthermore, the direct and indirect variance

volatility spillover from the Saudi market is significant, and that proves how important the

Saudi stock market is to Dubai stock market investors. On the other hand, Kuwait stock

market investors have the opportunity to hedge in Dubai stock market by going short $37 for

every $1000 long in the Kuwait stock market.

Table 10: Trivariate GARCH model for Saudi, Kuwait and Dubai stock markets

Independent


2

,1 t 0.29342 (23.50) 0.0007163 (0.98) 0.000559 (1.14)

tt ,2,1 0.05683 (2.71) 0.01985 (1.95) 0.000676 (0.51)

tt ,3,1 -0.02704 (-1.47) -0.000893 (-0.88) 0.01324 (2.35)

2

,2 t 0.00275 (1.41) 0.13752 (20.65) 0.000204 (0.25)

tt ,3,2 -0.00262 (-1.25) -0.01238 (-1.15) 0.00800 (0.50)

2

,3 t 0.0006230 (0.74) 0.000278 (0.58) 0.07831 (18.08)

th ,11 0.67997 (90.65) 0.000523 (0.63) 0.000654 (2.94)

th ,12 -0.35371 (-3.94) -0.04173 (-1.27) -0.000986 (-0.84)

th ,13 0.01416 (1.91) 0.00243 (1.15) -0.04792 (-5.85)

th ,22 0.04600 (1.97) 0.83228 (168.29) 0.000371 (0.46)

th ,23 -0.00368 (-2.19) -0.09679 (-2.52) 0.03610 (0.93)

th ,33 0.0000736 (0.95) 0.00281 (1.26) 0.87710 (478.39)


conditional variance for Kuwait stock market return, and h33 represents the conditional variance for

Dubai stock market return. BEKK parameterization has been used for the multivariate GARCH. For


shortness.





The result summary shows that investors in the Kuwait stock market will have an

opportunity to minimize their risk by also investing in Bahrain and Dubai stock markets. The

only common connection between Bahrain and Dubai is that both of these states have well

developed financial sectors. The Kuwait investors may see this benefit as an opportunity for

them to transfer their funds among these markets. Furthermore, Kuwait as a government has

integrated with these two states since they were British Empire colonies. On the other hand,

Saudi stock market investors have an opportunity to hedge by investing in the Qatar stock

market.

Due to the Qatar economy’s new infrastructure, this opportunity may attract Saudi stock

market investors to hedge in Qatar. Overall, volatility spillover is transferred from Saudi to

Bahrain and Dubai stock markets directly and that may help the Kuwait stock market

investors optimize their portfolio allocation.

Overall, the news seems to be important to the Bahrain stock market in contrast with the

Qatar and Dubai stock markets. News coming from the Kuwait stock market is more

important to the Bahrain stock market than news coming from Saudi stock market where the

Kuwait stock market’s news counts for 0.0075 and Saudi stock market’s news counts for

0.0022. This result may be due to the age of the Bahrain stock market compared to the Qatar

and Dubai stock markets. Furthermore, direct news from the Bahrain stock market is more

important to the Saudi stock market than to Kuwait stock market where Bahrain stock

market’s news counts for 0.10 to the Saudi stock market and 0.021 for the Kuwait stock

market. On the other hand, the direct volatility spillover is visible from the Bahrain stock

market to the Saudi and Kuwait stock markets. This case is not applicable for the Qatar and

Dubai stock markets except for the Qatar stock market’s volatility spillover to the Kuwait

stock market.

6. Conclusion

This study discusses the transmission of volatility and shocks among five GCC stock

markets, mainly Saudi, Kuwait, Bahrain, Qatar, and Dubai. Covering the period between

2001 and 2006, this study attempts to investigate the relationship among big and small market

in GCC countries.

In all cases there is a significant direct and indirect relationship between the Saudi and

Kuwait stock markets in their second moments. The Saudi stock market sends its volatility to

the Bahrain and Dubai stock markets, which are in well-developed financial systems that keep

stock market investors in these states well informed about the news and implement it through

assets prices. Only the Dubai stock market has clear direction of the volatility spillover,

which is indirectly affected by Saudi stock market news, variance, and by direct variance

volatility spillover.





This result will help both the stock market investors and market authorities. From the

investor’s prospective, this result will help stock market investors to optimize their portfolio

allocation by minimizing the risk through short and long positions in different GCC stock

markets. The Kuwait stock market investors can go short in Bahrain and Dubai stock markets

to minimize their portfolios risk. On the other hand, Saudi stock market investors can go

short in the Qatar stock market to minimize their portfolio risk. In term of market authorities,

the Bahrain and Dubai stock markets are more likely affected by Saudi stock market news and

variance volatility directly and indirectly. The authorities in the Bahrain and Dubai stock

markets should watch the Saudi stock market movements very carefully to control their

markets. Also, distribute any news coming from Saudi stock market very fast and add some

comments to clarify any unclear points. Furthermore, the authorities in these stock markets

can integrate with each other by implementing financial tools to stabilize their markets’

movements.

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