By
Mohamed Abdelaziz Eissa
1
Qatar University
In this paper, we employ a Multivariate GARCH model to study volatility spillovers between USA, UK as proxy of developed markets and GCC stock returns as proxy of emerging markets. we find strong evidence of own market shock and volatility in all countries under estimation. While there is no evidence of cross-market effects from the GCC stock markets to USA or UK, we did not find evidence of shock or volatility spillovers from USA, and UK to all GCC stock markets, while there is evidence of shock and volatility spillover among the GCC stock markets. This results may be due to the restrictions on the accessing of the foreign investors to these markets, some markets allow the foreigner to access but the other stock markets not.
Keywords: Stock returns, volatility spillovers, multivariate GARCH models, GCC region
JEL classification: C22, F31, G12, G15
Classification: Research paper
1 Economic and Finance department, College of Business and Economics, Qatar University, Doha, Qatar, P.O. 2713, Email: m.eissa@qu.edu.qa
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With the development in the liberalization of capital movements and the securitization of stock markets, international financial markets have become increasingly interdependent. Advanced computer technology and improved world-wide network processing of news have improved the possibilities for domestic stock markets to react promptly to new information from international markets
.
As a result , the shocks and volatility in the developed equity as well as commodity markets are very likely to influence the stock returns of emerging markets. For investors, the behavior and sources of market volatility have paramount importance for realization of hedging strategies and international asset diversification decisions on global financial markets. Needless to say, the financial markets of the emerging and developing economies have different characteristics compared to those of developed countries. For instance, the emerging markets are characterized by relatively high returns and low correlation compared to advanced markets Bekaert and Harvey (1995), also Emerging stock markets are recognized relatively low correlations with mature capital markets and higher volatility (Harvey 1995).
Thus, these differences make an empirical investigation of emerging stock markets is valuable to examine stock returns of emerging and developing markets within a mean and volatility spillovers framework. Also, the understanding of this phenomenon is also very important for policy makers in the emerging markets.
One of the most important emerging markets in the world is The Gulf Cooperation Council (GCC hereafter), which is an attractive location for investment and a salient consumer market for imported goods and services, and information technology to one of the youngest population that is considered to have highest powers of spending in the world. The common market of the six GCC economies (Bahrain, Kuwait, Oman, Qatar, Saudi
Arabia, and United Arab Emirates) are open to foreign capital investment and are continually working to grant national treatment to all foreign investment firms and cross country investment and services trade, The GCC economies have upheld an open system of trading, free capital movement, convertibility of currency with fixed
nominal rates, and large labor inflows- both skilled and unskilled. Additionally, the GCC's advanced financial
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systems have been an essential channel for advancing their trade integration into the global community.
Although the importance of this region, a few researches have been conducted on these countries, in this paper I am trying to fill this gab, therefore, the results of this study will be of great value to researchers and practitioners on a global level since it takes a broad approach to tackle the phenomenon under investigation 2 .
An extensive theoretical literature exists on volatility spillovers. One class of models (for example, Allen and
Gale 2000, Calvo and Mendoza 2000, Dungey and Martin 2007, Flemming et al. 1998, King and Wadhwani
1990) stresses the role of information flows resulting in portfolio rebalancing. More specifically, news arrivals in one country, or shifts in relative risk aversion, influence the value of various domestic assets, as well as the foreign ones, leading to portfolio reallocation. Pavlova and Rigobon (2007), using a two-country, two-good asset pricing model (see also Zapatero 1995), analyse how demand and supply shocks affect the linkages between domestic financial markets (stocks and bonds) and also, between domestic financial markets and the exchange rate. The focus of Pavlova and Rigobon (2007) is on the linkages between the conditional first moments. On the other hand, economic activity also affects the level of stock prices. The stock price of a firm reflects the expected future cash flows, which are influenced by the future internal and external aggregate demand. Consequently, stock prices will incorporate present and expected economic activity as measured by industrial production, real economic growth, employment rate or corporate profits (see Fama (1981), Geske and
Roll (1983)). Empirical studies have confirmed the long-run positive relationship between stock prices and economic activity (see e.g. Schwert (1990), Roll (1992) and Canova and DeNicole (1995)).
The rest of the paper is organized as follows. Section 2 briefly reviews the existing theoretical and empirical literature. Section 3 outlines the data and descriptive analysis. Section 4 describes the empirical methodology.
Section 5 presents and discusses the empirical results. Finally, section 6 offers summary and some conclusion remarks.
2 http://www.gulfbase.com/
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There is a divers literature conducted on the financial market integration and volatility Spillover, some studies have examined only the return spillover across the markets, while some other studies consider the first and the second moments spillover.
Previous literature on the volatility dynamics of individual financial asset markets indicate the existence of asymmetry in the response of conditional variances to good and bad news, with negative shocks raising volatility to a greater extent than positive ones. This phenomenon tested by Black (1976) and Christie (1982) in the context of equity returns, Pindyck (1984), French etal. (1987), Campbell and Hentschel (1992), and Wu
(2001) among others, find the time-varying risk premia, is captured by the exponential GARCH (EGARCH) model of Nelson (1991).
Since the seminal papers by Engle, Ito, and Lin (1990) and by Hamao, Masulis, and Ng (1990), volatility spillovers phenomenon have been extensively studied and, especially, different GARCH model specifications have been popular, Engle, Ito and Lin (1990) investigate the Yen/USD exchange rate. Evidence of volatilityspillover effects is found. Lin, Engle and Ito (1994) investigate the volatility spillover between the US and
Japanese stock markets. The daytime return and volatility in one market is correlated with the overnight return and volatility in the other market. Eom, Subrahmanyam and Uno (2002) find strong volatility-spillover effects from the US to the Japanese swap market, but only weak effects going the opposite direction.
The early research focus on the examination of return spillover across the markets, for instance, Elyasiani
(1998) have investigated the interdependence and dynamic linkages between the emerging capital markets of
Sri Lanka with the markets of its major trading and have found no significant interdependence between the Sri
Lankan market and the equity market of the US and other Asian countries. Janakiramanan (1998) and Hsiao
(2003) tried to examine the linkages between the stock markets in the Pacific-Basin region and the Asia-Pacific region with the US. The unidirectional linkages from the US market to the others are found to be significant in
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both studies. Gilmore and McManus (2002) have examined the short as well as long term relationships between the US stock market and three Central European markets namely Czech Republic, Hungary, and Poland, they find the markets are not cointegrated in the long-run. Leong and Felmingham (2003) have analyzed the interdependence of five East Asian stock price indices (Singapore Strait Times (SST), Korea Composite (KC),
Japanese Nikkei (JN), Taiwan weighted (TW) and Hang Seng (HS)), some of the pairs of markets are found to be cointegrated. Results of cointegration and Granger causality test by applying very high frequency (from 5 minutes to 1 day) data from US, London, Germany and some other European markets, Alexandr (2008) has revealed the faster transmission of information among the markets within one hour, not within a day or beyond a day.
Unlike only return spillover, some studies examine the spillover of information both in terms of return and volatility. Ng (2000) finds evidence of volatility-spillover effects to various pacific basin stock markets from
Japan (regional effects) and the US (global effects). Furthermore, Rockinger and Urga (2001) explore the effects from London and Frankfurt stock exchange markets to Central European stock markets over 1994-1997 periods, they revealed that although both markets drive significant volatility spillover effects, the effects from
UK stock market tends to be more substantial than German stock markets. Scheicher (2001) investigates the stock markets of Central and Eastern European (CEE) countries, namely, Czech, Hungary, and Poland in the light of regional and global financial market interdependences. they conclude that equity markets are influenced by regional and global spillover effects. Baele (2002) investigates the volatility-spillover effects from the US
(global effects) and aggregate European (regional effects) stock markets into many individual European stock markets, he finds that shock spillover intensity varies significantly through time. Furthermore, Miyakoshi
(2002) also finds that Japanese stock market is also adversely influenced by Asian Pacific-Basin countries. On the other hand, Gilmore and McManus (2002) examine the short and long run integration and bilateral relationships between the US and individual Central and Eastern Europe stock markets, and find that indication
of possible interaction is negligible. Égert and Koubaa (2004) based on GARCH model indicates that CEE
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countries are characterized higher volatility and more asymmetry than G-7 countries. Moreover, The study by
Baele (2005) investigates conditional volatility spillovers relying on regime switching models from the US and aggregate EU stock returns to thirteen individual Western European developed markets. The study reported statistically spillover from the US and EU markets. Abraham and Seyyed (2006) have examined the flow of information among the Gulf equity markets of Saudi Arabia and Bahrain and have interestingly found an asymmetric spillover of volatility from the smaller but accessible Bahraini market to the larger but less accessible Saudi market. Chuang (2007) investigate the volatility interdependence in six East Asian markets. the results revealed a strong interdependence among the conditional variance of different markets, Japanese market is found to be most influential in transmitting volatility to the other East Asian markets. G ę bka and Serwa
(2007) have also supported the fact that even if being significant both within and across the region, intraregional volatility spillover is more pronounced than the inter-region spillover. While analyzing the comovements within and across the Central, Eastern and Western European stock markets, Egert and Kocenda
(2007) have revealed the absence of any robust cointegration relationship among any of the pair of markets, but have found some short-term bidirectional information spillover among the markets both in terms of stock returns and stock return volatility. Christiansen (2007) finds that volatility in bond markets is highly influenced by regional factors for European Monetary Union (EMU) countries. In contrast, in the case of non-EMU countries the volatility spillover driven by local and global US spillover effects tend to be much larger and stronger those compared to regional European effects. Moreover, the interactions between three CEE states and developed markets such as Germany and the US are explored by Syriopoulos (2007). The author finds long run interactions between developed countries and CEE states. Contrary, in the short run US stock market returns impose more dominant effects than the one from Germany. By applying the EGARCH-M models with a generalized error distribution, Yu and Hassan (2008) have found large and predominantly positive volatility spillovers and volatility persistence in conditional volatility between MENA and world stock markets, volatility spillovers within the MENA region are found to be higher than cross-volatility spillovers for all the markets. At the same time, while examining the dynamic linkage between the MENA countries, Alkulaib (2008) have found
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some regional inconsistency in the information spillover among the markets. Morana and Beltratti (2008) in their paper have found a progressive integration of four developed stock markets namely US, the UK, Germany and Japan, and have revealed an increasing comovements in prices, returns, volatilities and correlations among all the four markets, especially the European markets. Mulyadi (2009) reported unidirectional volatility transmission from the US stock markets to Indonesia and bidirectional volatility transmission between Japan and Indonesia. Singh et al. (2010) examined both price and volatility spillovers across 15 North American,
European and Asian stock markets. They addressed the problem that a same day closing index represent data for some markets which were open simultaneously, somewhere a market close precedes a market open and vice versa. Thus it was necessary to consider whether same day data was, in fact, simultaneous, past or future data.
They conclude that the direction of both return and volatility spillover was primarily from the US market to
Japanese and Korean markets, then to Singapore and Taiwan, and then to Hong Kong and Europe before returning to the US. They also reported that the Japanese, Korean, Singapore, and Hong Kong markets were the markets with the greatest power within the Asian markets.
Although a lot of domestic and foreign investors started to transfer their investment into the Gulf area a few studies have been conducted on this region, in this paper we will try to fill this gab by investigating the volatility spillover between the USA and UK stock market as proxy of the developed markets and stock markets in the GCC region as emerging markets,
3.
:
We use weekly returns, defined as log differences of local currency stock market indices for weeks running from
Wednesday to Wednesday to minimize effects of cross-country differences in weekend market closures. we consider weekly stock market general price indexes in GCC countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and
Unite Arab Emirates(Abu Dhabi)), UK and USA; we obtain the relevant data from Bloomberg database, the
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sample period is different from country to country according to data availability. Furthermore. The daily returns on various stock markets are given by:
 1
(1) where
R
and t
R t
 1
are the closing values for the trading days t
and t
 1 , respectively.
Table 1 reports descriptive statistics for the countries under estimation. The sample means of stock returns are positive over the sample period in all countries, the exception was Bahrain. The standard deviations vary from
1.627 for Bahrain general index returns to 4.175 for Saudi Arabia stock returns. All data series of stock returns display non-zero skewness and excess kurtosis, leading to highly significant Jarque- Bera statistics, which indicates that the returns are non-normally distributed.
A MGARCH (Multivariate GARCH) model is developed to examine the joint processes relating the weekly stock returns in GCC, UK, and USA stock markets. The conditional mean equation for returns is given as follows: r
1 t
 
1
 
11 r
1 , t
 1
 
12 r
2 , t
 1
 
1 t
(2) r
2 t
 
2
 
21 r
1 , t
 1
 
22 r
2 , t
 1
 
2 t
(3) where r is an n
×1 vector of weekly return at time t for each market with t r
1
and r
2
being the returns on GCC and UK or USA stock markets, respectively, and  t
\
I t
 1
 (0,
H t
) . The n
×1 vector of random errors  t is the innovation for each market at time t with its corresponding n
× n conditional variance-covariance matrix,
Ht
. The market information available at time t
- 1 is represented by the information set
I t
 1
. The n
×1 vector,  represent
long-term drift coefficients. Following Karolyi (1995), the elements of the matrix  and  can provide
8

measures of the significance of the own and cross-mean spillovers, 
11
and 
22
capture serial autocorrelation in
GCC and UK, or USA stock returns respectively, 
12
captures the mean spillover from GCC stock returns to
USA stock market, and 
21
captures the mean spillover from USA or UK stock returns to GCC stock returns.
Engle and Kroner (1995) present various MGARCH models with variations to the conditional variancecovariance matrix of equations. For the purposes of this paper we use the BEKK (Baba, Engle, Kraft and
Kroner) model, whereby the variance-covariance matrix of equations depends on the squares and cross products of innovation  and volatility
Ht for each market lagged one period. One important feature of this specification t is that it builds in sufficient generality, allowing the conditional variances and covariances of the stock market returns to influence each other, and, at the same time, does not require the estimation of a large number of parameters (Karolyi, 1995). The model also ensures the condition of a positive semi-definite conditional variance-covariance matrix in the optimisation process, and is a necessary condition for the estimated variances to be zero or positive. The BEKK parameterisation for the MGARCH model is written as:
H t

C

C

A
  t
 1
 t
 1
A

B

H t
 1
B
(4)
where c are elements of an n × n symmetric matrix of constants C , H t is a liner function of its own lagged value, as well as a lagged value of the squared innovation, both of which allow for own-market and crossmarket influences in the conditional variance (Karolyi, 1995), specifically, the diagonal elements of matrix
A
, a
11 and a
22
, measure shock persistence in GCC stock market and stock returns in USA, or UK respectively, however, b
11
and b
22
the diagonal elements of
B
measure time persistence in conditional GCC stock returns and conditional UK, or USA stock returns volatilities. The off diagonal elements a of the symmetric n
× n matrix
A measure the degree of innovation from GCC stock returns to UK, or USA stock returns, and the
9

elements b
12 of the symmetric n
× n matrix
B indicate the persistence in conditional volatility GCC stock returns and UK, or USA stock returns. This can be expressed for the bivariate case of the BEKK as:


H
H
11 t
21 t
H
12 t
H
22 t



C

C


 a a
11
21 a
12 a
22


' 



2
1 t
2 t
 1

 1

1 t
 1

1 t
 1

2 t
 1
2
2 t
 1



 a
11 a
21 a
12 a
22




 b b
11 b
12
21 b
22


' 

H
H
11 t
 1
H
21 t
 1
H
12 t
 1
22 t
 1



 b b
11
21 b b
12
22


(5)
Equations (6)-(8) below solve for the cross effects in the variance and covariance equations implied by the
BEKK specifications. h
11 , t
 c
2
11
 a
2
11

 2 b
11 b
21 h
12 , t
 1
1
2
, t
 1
 2 a
11 a
21

1 , t
 1

2 , t
 1
 a
2
21

2
2
, t
 1
 b
2
11 h
11 , t
 b
2
21 h
22 , t
 1
(6) h
12 , t
 b

11 b
12 c
11 c
21 h
11 , t
 1

 a
11 a
12

( b
21 b
12
2
1 , t
 1
 ( a
21 a
12
 b
11 b
22
) h
12 , t
 1
 a
11 a
22
 b
21 b
)
22

1 , t
 1
 h
22 , t
 1
2 , t
 1
 a
21 a
22

2
2
, t
 1
(7) h
22 , t
 c
2
12
 c
2
22
 a
2
12
 2
1 , t
 1
 2 a
12 a
22

1 , t
 1

2 , t
 1
 a
2
22
 2
2 , t
 1
 b
2
12 h
11 , t
 2 b
12 b
22 h
12 , t
 1
 b
2
22 h
22 , t
 1
(8)
Under the assumption that the random errors are normally distributed, the log-likelihood function for the
MGARCH model is estimated using Quasi Maximum Likelihood, QML, (Bollerslev, Wooldridge (1992)):
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L
(  )  
Tn
2
 ln( 2  ) 
1
2 t
T 
 1
(ln |
H t
|   t
 |
H t
 1 |  t
) (9) where
T is the number of observations, n is the number of markets, θ is the vector of parameters to be estimated, and all other variables are as previously defined. The BFGS algorithm is used to produce the maximum likelihood parameter estimates and their corresponding asymptotic standard errors 3 .
Lastly, we use the Ljung-Box Q statistic to test for no serial correlation in the standardised residuals and in the squared standardised residuals. The Ljung-Box Q statistic is given by:
LBQ

T ( T
 2 ) j p 
 1
( T
 j )  1 r
2 ( j ) (10) where r
( j
) is the sample autocorrelation at lag j calculated from the noise terms and
T is the number of observations.
LBQ is asymptotically distributed as  2 with ( p
- k
) degrees of freedom and k is the number of explanatory variables.
In Table 2, Panel A, we report the conditional mean estimation results for Abu Dhabi. Evidence of serial autocorrelation is found for Kuwait, Qatar, Saudi Arabia, SP500, NYSE, and UK stock returns as the coefficients 
22 ( t
 1 )
are significant at 1% and 5% significance level, Qatari stock index was significant at 1% over 3 periods. There is also evidence of serial autocorrelation in Abu Dhabi stock market in its relationships with all stock markets with exception of Qatar and NYSE as the coefficient 
11
is significant at 1% and 5% level of significance over 1 period for Kuwait, Oman, Saudi Arabia, and UK and 3 periods for SP500. The empirical evidence suggests unidirectional spillover effect for the conditional mean from
3 We used the RATS program for estimating the MGARCH using QML which estimates the likelihood function under the assumption that the contemporaneous errors have a joint normal distribution. Using the robust errors and lag options we corrected the covariance matrix estimate to allow for more complex behaviour of the residuals, this option corrects the heteroscedasticity and serial correlation, this is sometimes known as the HAC (Heteroscedasticity and Autocorrelation Consistent) covariance matrix.
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Abu Dhabi to Oman given the significantly estimates of the parameter 
12
, and there is unidirectional mean relationship from Saudi Arabia, NYSE, and UK as 
21
is significant at 1% and 5% significance level, also there is bidirectional mean spillover effect between Abu Dhabi and both Kuwait and Qatar as

12
and 
21
are significant.
Panel B shows the estimates of conditional variance-covariance parameters. First, we observe evidence of persistence in the conditional shocks and conditional volatility of all the stock market indexes given the significantly estimates of the parameters a
11
, a
22
, b
11
and b
22
. Furthermore, there is evidence of shock spillover effect from Abu Dhabi to Oman as a
12
is significant at 1% significance level, more over there is evidence of bidirectional shock spillover effect between Abu Dhabi and both Kuwait and Qatar as a
12
and a
21
are significant at 5% and 10% significance levels. Finally, although there is no evidence of volatility spillover from Abu Dhabi to any stock market wither in the GCC region or developed stock markets, there is evidence of unidirectional volatility spillover from Kuwait and Saudi Arabia to Abu
Dhabi as b
21
is significant at 1% and 5%. the diagnostics (see Panel C of Table 2), the VAR -BEKK model specifications show no evidence of serial correlation in both the level and in the squared values of the standardised residuals 4 .
In Table 3, Panel A records the conditional mean estimation results for Bahrain. Evidence of serial autocorrelation is found for Abu Dhabi, Dubai, Kuwait, Oman, Qatar, Saudi Arabia, SP500, NYSE, and
UK stock returns as the coefficients 
22 ( t
 1 )
are significant at 1% and 5% significance level, Qatari stock index was significant at 1% over 4 periods. There is also evidence of serial autocorrelation in Bahrain stock market in its relationships with all stock markets with exception of Abu Dhabi and Kuwait as the coefficient 
11
is significant at 1% and 5% level of significance over 1 period for all countries included.
Although there is no empirical evidence suggests unidirectional spillover effect for the conditional mean from Bahrain to other stock markets given the insignificantly estimates of the parameter 
12
, the results points that there is unidirectional mean relationship from all Stock markets (Abu Dhabi, Dubai, Kuwait,
Oman,SP500, NYSE, and UK) to Bahraini stock market as 
21
is significant at 1% and 5% significance
4 The exception was Oman as its square value of the standardized residuals is significant, so its results should be taken with caution.
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levels, also there is bidirectional mean spillover effect between Bahrain and both Qatar and Saudi Arabia as 
12
and 
21
are significant.
Panel B reports the estimates of conditional variance- covariance parameters. The statistically significant coefficients a
22
and b
22
, reveal evidence of some degree of persistence in all stock returns.
The statically significant a
11
and b
11
parameters confirm the degree of persistence in the stock return of
Bahrain volatility. Furthermore, there is evidence of shock spillover effect from Bahrain to Kueait and
Qatar stock markets. Evidence of volatility spillover effects from Bahrain to stock returns also exists in 5 out of 9 stock markets as b
12
is significant (Abu Dhabi, Dubai, Qatar and UK being the exceptions).
Finally there is also evidence of feedback effect, first, in terms of shock spillover effects, from all stock markets with exception of SP500 and NYSE to Bahrain stock returns. Second, the statistically significant coefficient b
21
in Abu Dhabi, Dubai, Oman, Qatar, Saudi Arabia, NYSE stock markets suggests the presence of volatility spillover effects from those stock markets to the volatility of Bahrain stock market.
As for the diagnostics (see Panel C of Table 3), the rejection of the null of serial correlations in both the standardised and squared standardised residuals for stock returns and exchange rate changes, suggests that the VAR (1)-BEKK model is well specified 5 .
Panel A of Table 4 presents the conditional mean parameter estimation results for Kuwait. There is evidence of stock return serial autocorrelation since the 
22
coefficients are significant at 5% level of significance in Qatar, Saudi Arabia, SP500, and NYSE. There is evidence of serial autocorrelation in the
Kuwaiti stock return as 
11
is significant at 1% level .Limited evidence of conditional mean spillover effects exist, running from Kuwait to Oman, in the same time we have feedback mean spillover from
Oman, Qatar, Saudi Arabia, SP500, NYSE, and UK.
5
LBQ
1 and LBQ
1
2
for SP500 and NYSE are significant therefore the results of these countries should be taken with caution.
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Panel B reports the estimates of conditional variance- covariance parameters. The statistically significant coefficients a
22
and b
22
, reveal evidence of some degree of persistence in Oman, Qatar, Saudi
Arabia, SP500, NYSE, and UK stock returns. The statically significant a
11
and b
11
parameters confirm the degree of persistence in the Kuwaiti stock volatility. Furthermore, there is no evidence of shock spillover effect from Kuwait to any stock market. Evidence of volatility spillover effects from Kuwait to
Qatar exists as b
12
is significant at 5% significance level. Finally there is also evidence of feedback effect, first, in terms of shock spillover effects, from Qatar and Oman stock returns to. Second, the statistically significant coefficient b
21
in Oman and Qatar, suggests the presence of volatility spillover effects from those countries stock returns to the volatility of Kuwaiti stock return.
As for the diagnostics (see Panel C of Table 4), the rejection of the null of serial correlations in both the standardised and squared standardised residuals for stock returns suggests that the VAR -BEKK model is well specified 6 .
Panel A of Table 5 presents the conditional mean parameter estimation results for Oman. There is no evidence of stock return serial autocorrelation since the 
22
coefficients are insignificant at 5% level of significance in, SP500, NYSE and UK. There is evidence of serial autocorrelation in the Omani stock return as 
11
is significant at 1% and 5% levels . No evidence of conditional mean spillover effects exist, running from Oman, in the same time we have feedback mean spillover from SP500, NYSE, and UK to
Oman.
Panel B reports the estimates of conditional variance- covariance parameters. The statistically significant coefficients a
22
and b
22
, reveal evidence of some degree of persistence in SP500, NYSE, and
UK stock returns. The statically significant a
11
and b
11
parameters confirm the degree of persistence in the Omani stock volatility. Furthermore, there is no evidence of shock or volatility spillover effects from
Oman to any stock market. Finally there is also evidence of feedback effect in terms of shock and volatility spillover effects, from UK to Oman.
6
LBQ
1
2
for SP500 and NYSE are significant therefore the results of these countries should be taken with caution.
14

As for the diagnostics (see Panel C of Table 5), the rejection of the null of serial correlations in both the standardised and squared standardised residuals for stock returns suggests that the VAR -BEKK model is well specified 7
Panel A of Table 6 presents the conditional mean estimation results for Qatar. Limited evidence of stock return serial autocorrelation since the 
22
coefficient is significant at 5% level of significance in
Saudi Arabia only. There is evidence of serial autocorrelation in the Qatar stock return as 
11
is significant at 1% and 5% levels over 4 periods. Evidence of conditional mean spillover effects exist, running from
Qatar to Oman and Saudi Arabia, in the same time we have feedback mean spillover from Oman, Saudi
Arabia, SP500, NYSE, and UK to Qatar.
Panel B reports the estimates of conditional variance- covariance parameters. The statistically significant coefficients a
22
and b
22
, reveal evidence of some degree of persistence in Oman, Saudi
Arabia, SP500, NYSE, and UK stock returns. The statically significant a
11
and b
11
parameters confirm the degree of persistence in the Qatari stock volatility. Furthermore, there is evidence of shock spillover effects from Qatar to Oman. Finally there is also evidence of feedback effect in terms of shock from
Oman to Qatar, and in terms volatility spillover effects, from Oman and Saudi Arabia to Qatar.
As for the diagnostics (see Panel C of Table 6), the rejection of the null of serial correlations in both the standardised and squared standardised residuals for stock returns suggests that the VAR -BEKK model is well specified 8 .
Panel A of Table 7 presents the conditional mean estimation results for Saudi Arabia. Evidence of stock return serial autocorrelation since the 
22
coefficient is significant at 10% level of significance in
7
LBQ
1 and LBQ
1
2
for Oman are significant therefore the results of these countries should be taken with caution.
8
LBQ
1 and LBQ
1
2
for Oman are significant therefore the results of these countries should be taken with caution.
15

SP500 and NYSE. There is evidence of serial autocorrelation in the Saudi stock return as 
11
is significant at 1% and 10% levels. Evidence of bidirectional conditional mean spillover effects exist, between Saudi
Arabia and Oman.
Panel B reports the estimates of conditional variance- covariance parameters. The statistically significant coefficients a
22
and b
22
, reveal evidence of own market shock and volatility Oman, SP500,
NYSE, and UK stock returns. The statically significant a
11
and b
11
parameters confirm the degree of persistence in the Saudi Arabia stock volatility. Furthermore, there is no evidence of shock or volatility spillover effects from Saudi Arabia. Finally there is evidence of feedback effect in terms of both shock and volatility from SP500 and NYSE to Saudi Arabia,
As for the diagnostics (see Panel C of Table 7), the rejection of the null of serial correlations in both the standardised and squared standardised residuals for stock returns suggests that the VAR -BEKK model is well specified 9 .
To summarize, the tables from 1 to 7 show that there are strong evidence of own market volatility and own market shocks weather in the GCC region or in the developed markets as and are statistically significant across all markets. In terms of volatility and shock spillover we can note that there is bidirectional volatility and shock spillover among all GCC countries with exception of Saudi Arabia which has bidirectional volatility and shock spillover to all other GCC stock markets and receive volatility spillover from only two countries which are Abu Dhabi and Bahrain. Whereas there is limited unidirectional volatility and shock spillover from the developed markets (SP500, NYSE, and FTSE100) to the GCC stock markers, in details, there is unidirectional shock spillover from SP500 and NYSE to
Kuwait, Oman, and Saudi Arabia. However we can find only one volatility spillover runs from SP500 and
NYSE to Saudi Arabia. In the case of FTSE we can notice only unidirectional shock spillover to Abu
Dhabi, Bahrain, Kuwait, and Oman.
9
LBQ
1 and LBQ
1
2
for Oman are significant therefore the results of these countries should be taken with caution.
16

We can explain the poor global effect of the developed market on the GCC stock markets by pointing the limited access for foreign investors as the investment activity by foreign institutional investors in the GCC has remained highly limited, for example in Abu Dhabi and Kuwait the non GCC investors can own 49% max in any company, in Oman the foreign investors can own 70% maximum, while in Qatar the non GCC citizen can own 25%, in the case of Saudi Arabia there is no direct participation for the non GCC investors, they can participate only through the mutual funds, the only exception is Bahrain as the foreigner has no limit. Also the lack of market breadth, low liquidity, high volatility as well as existing ownership structures and transparency practices – more than half of the
GCC’s listed companies do not provide annual reporting in English language – but also the lack of hedging instruments have discouraged a more active engagement so far. We can say that the investors from outside the GCC whose share in stock market trading in the key markets has, as a consequence, remained below 5%.
As for the regional effect among the GCC stock markets, the degree of the market openness in the
GCC countries beside the ownership restrictions applied on the GCC citizens affect the relationship among these stock markets, for instance we can note heavily bidirectional volatility and shock spillover among four GCC stock markets namely Abu Dhabi, Bahrain, Kuwait, and Oman this is may be due to the openness of these countries to the GCC citizens without restrictions on the ownership percentage, while the case is different in Qatar and Saudi Arabia, as the stock market in those two countries is less opened to the GCC investors, in Qatar the GCC citizens can own up to 49% of any company while this percentage decreases in Saudi Arabia to reach only 25%.
6.
In this paper we examine the presence of global and regional volatility spillovers between stock returns in both developed and emerging markets, to evaluate the global effect we test for the volatility spill over between USA stock market, UK stock market as a proxy of developed stock markets and 6 GCC stock markets as a proxy of emerging markets, to measure the regional effect we test for the volatility spillover among the GCC stock markets. we use a MVGARCH model, the models for all countries appear to be well specified according to
17

LBQ
and
LBQ
2 insignificant results. On the global base the empirical evidence suggests no shock or volatility spillover from the developed markets to the GCC stock markets, while we can find such effects on 6the regional base as there is bidirectional shock and volatility spillover among Abu Dhabi, Kuwait, Bahrain, and Oman, this relationship decreases in Qatar and Saudi Arabia, this results may be due the degree of openness in each country that allow the foreigner investors to access the stock market. The closed market policy followed by the gulf countries prevent the information flows from developed markets to them, the less restrictions on the access of the GCC investors allow the shock and volatility spillover among these stock markets.
18

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23

Table 1: Descriptive statistics
Abu
Dhabi
Bahrain Kuwait Oman Qatar Saudi
Arabia
SP500 NYSE UK
Std. dev 3.130 1.627 2.210 2.878 4.145 4.175 2.55 2.595 2.563
Skewness -1.17* -0.413* -1.079* -1.026* -1.22* -1.51* -0.575* -0.699* -0.329*
Ex.
Kurtosis
JB
9.800 3.973* 4.328 14.604* 8.67* 6.585* 4.043* 4.662* 3.190*
2500* 306* 582* 7132* 1514* 978* 579* 777* 348*
The table displays summary statistics for weekly returns on the stock market returns for GCC, UK and USA stock markets. *, ** and *** indicates significance at 1%,
5% and 10% levels of significance.
24

Table 2 : VAR (1)-BEKK estimation results for general stock indices in Abu Dhabi and regional / global stock markets.
Panel A: Conditional mean estimates

1
Kuwait Oman Qatar Saudi Arabia SP500 NYSE UK

2
0.254** 0.248* 0.148 0.264** 0.229* 0.259** 0.195**

11 ( t
 1 )
0.136* 0.093 0.080** 0.049 0.049 0.102***

11 ( t
 2 )
0.0434

11 ( t
 3 )

11 ( t
 4 )
-0.011
0.020
0.068*** 0.066

21 ( t
 1 )

21 ( t
 2 )
0.0173 -0.029 0.0069 0.062** 0.041 0.054** 0.101*
0.084* -0.006 -0.005 0.0006 0.040 0.042*** 0.038

21 ( t
 3 )

21 ( t
 4 )
0.026
0.086***
0.0473 0.0464

22 ( t
 1 )
0.199* 0.063 0.118 0.226*

22 ( t
 2 )
0.109**
-0.122* -0.102** -0.057**
0.002 0.004 -0.013

22 ( t
 3 )

22 ( t
 4 )
0.120*
0.095
0.083* 0.079**

12 ( t
 1 )

12 ( t
 2 )

12 ( t
 3 )

12 ( t
 4 )
0.166*
0.062*
-0.126**
0.043
-0.009
-0.015 -0.016 0.037
0.004 c
11
25

c
12 c
22 a
11 a
12
0.050 0.248 0.129 0.535 -0.118 -0.186 -0.133
0.378* -0.430** 0.631* 0.640* 0.525*
0.288* 0.338* 0.382* 0.362* 0.382* 0.392* 0.451* a
21 a
22
0.479* 0.386* 0.488* 0.551* 0.495* 0.545* 0.415* b
11
0.941* 0.941* 0.917* 0.949* 0.924* 0.918* 0.895* b
12
0.044 -0.021 -0.023 0.005 -0.003 0.002 0.018 b
21
-0.09** -0.005 -0.002 -0.012 b
22
0.859* 0.884* 0.875* 0.760* 0.842* 0.822* 0.888*
MLBQ
1
17.24 9.99 16.50 16.42 16.12 15.92
MLBQ
2
15.54 5.83 5.15 9.76
MLBQ
1
2 4.56 6.76 6.85 6.55 7.32 6.74 3.61
MLBQ
2
2
3.28 7.14 8.68 9.26 13.64
Table 2 displays the bivariate VAR -BEKK estimation results for general indices in Abu Dhabi , USA, and UK stock markets. In panel A conditional mean parameter estimates are reported,

11
and

22
estimate serial autocorrelation in Abu Dhabi stock market and other stock returns respectively,

12
estimates mean spillovers from Abu Dhabi stock market to the other stock returns,

21
estimates mean spillovers from the other stock returns to Abu Dhabi stock market. Panel B reports the estimates of conditional variance- covariance parameters. a
11
and a
22
estimate the persistence of own market shocks in Abu Dhabi stock market and the other stock returns respectively, b
11
and b
22
estimates the persistence of own market volatility in Abu Dhabi stock markets and the other stock returns respectively. a
12
, a
21 estimate shocks spillovers from / to Abu Dhabi stock market to / from the other stock returns. b
12
, b
21
estimates volatility spillovers from / to Abu Dhabi stock returns to / from the other stock returns. Panel C displays the diagnostic tests,
LBQ
, (
LBQ
2 ) is the multivariate Ljung-Box Q statistic for serial correlation in returns
(squared returns) using 12 lags. *, ** and *** indicates significance at 1% 5% and 10% levels of significance.
26

Table 3 : VAR (1)-BEKK estimation results for general stock indices in Bahrain and regional / global stock markets.
Panel A: Conditional mean estimates
Abu
Dhabi

1
Saudi
Arabia
SP500 NYSE UK
-0.080 -0.067 0.006 -0.050 -0.077 -0.049 0.022 -0.032 -0.05

2

11 ( t
 1 )
0.079 0.084** 0.024 0.159* 0.082** 0.088** 0.149* 0.126* 0.094*

11 ( t
 2 )
0.107**

11 ( t
 3 )

11 ( t
 4 )
0.078***
-0.002

21 ( t
 1 )

21 ( t
 2 )

21 ( t
 3 )
0.104* 0.056* 0.211* 0.136* 0.030 0.067* 0.095** 0.086* 0.070*
0.008

21 ( t
 4 )
0.030**

22 ( t
 1 )
0.119*** 0.064 0.331* 0.129** 0.086 0.145* -0.140* -0.143* -0.018*

22 ( t
 2 )

22 ( t
 3 )
0.017

22 ( t
 4 )

12 ( t
 1 )

12 ( t
 2 )
0.194*
0.050 0.270 0.068 0.092 -0.045 0.091 -0.059 -0.028 -0.060

12 ( t
 3 )

12 ( t
 4 )
0.097
-0.238*
Panel B: conditional variance-covariance estimates
27

c
11 c
12 c
22
-0.0007 -0.003 0.002 0.0001 -0.0001 0.0002* -0.0004 0.001 0.470 a
11 a
12 a
21 a
22
0.440* 0.453* 0.429* 0.408* 0.390* 0.640* 0.519* 0.620* 0.464* b
11
0.930* 0.992* 0.890* 0.554* 0.965* -0.904* 0.683** 0.735* 0.941* b
12
-0.112 0.184 -0.163* -0.198** -0.046 0.562* -0.42*** -0.372* -0.043 b
21
-0.044** -0.047* 0.017 0.067*** -0.034* 0.237* 0.166 0.127** -0.021 b
22
0.892* 0.835* 0.886* 0.955* 0.897* 0.671* 0.827* 0.803* 0.871*
Panel C: Test for model fitness
MLBQ
1
22.68* 16.11
MLBQ
2
18.81 5.71 5.27 6.73 7.24 12.80
MLBQ
1
2 24.4* 9.36
MLBQ
2
2 4.57 11.58 6.16 18.66 3.08 7.82 4.51 7.61 9.14
Table 3 displays the bivariate VAR -BEKK estimation results for general indices in Bahrain, USA, and UK stock markets. In panel A conditional mean parameter estimates are reported,

11
and

22
estimate serial autocorrelation in Bahrain stock market and other stock returns respectively, from Bahrain stock market to the other stock returns,

12
estimates mean spillovers

21
estimates mean spillovers from the other stock returns to Bahrain stock market. Panel B reports the estimates of conditional variance- covariance parameters. a
11
and a
22
estimate the persistence of own market shocks in Bahrain stock market and the other stock returns respectively, b
11
and b
22
estimates the persistence of own market volatility in Bahrain stock markets and the other stock returns respectively. a
12
, a
21 estimate shocks spillovers from / to Bahrain stock market to / from the other stock returns. b
12
, b
21
estimates volatility spillovers from / to Bahrain stock returns to / from the other stock returns. Panel C displays the diagnostic tests,
LBQ
, (
LBQ
2 ) is the multivariate Ljung-Box Q statistic for serial correlation in returns (squared returns) using 12 lags. *, ** and *** indicates significance at 1% 5% and 10% levels of significance.
28

Table 4 : VAR (1)-BEKK estimation results for general stock indices in Kuwait and regional / global stock markets.
Panel A: Conditional mean estimates
Arabia

1

2
0.203** 0.184*** 0.245** 0.193** 0.169*** 0.169***
0.285* 0.343 0.376* 0.217* 0.257* 0.183**

11 ( t
 1 )
0.289* 0.282*

22 ( t
 2 )

22 ( t
 3 )

22 ( t
 4 )

12 ( t
 1 )

12 ( t
 2 )

12 ( t
 3 )

12 ( t
 4 )

11 ( t
 2 )
0.018

11 ( t
 3 )

11 ( t
 4 )
-0.0005
0.101**

21 ( t
 1 )

21 ( t
 2 )

21 ( t
 3 )
0.107* 0.044* 0.057* 0.100* 0.098* 0.132*
0.045**

21 ( t
 4 )
-0.001

22 ( t
 1 )
0.080 0.130* 0.161* -0.09** -0.028
0.057
0.071
0.160* 0.047 0.042 0.053 0.051 0.042
0.037
-0.018
29

c
11 c
12 c
22
0.476** 0.626* 0.516*** 0.367* 0.352* 0.389
0.525*8 0.590* 0.926* 0.604* 0.679* 0.574* a
11 a
12
0.407* 0.489* 0.497* 0.361* 0.338* 0.392*
0.055 0.135 0.017 0.043 -0.013 -0.048 a
21 a
22
0.393* 0.444 0.509* 0.479* 0.561* 0.467* b
11
0.878* 0.814* 0.851* 0.917* 0.926* 0.905* b
12 b
21
-0.06*** -0.056** -0.032 0.004 -0.010 -0.025 b
22
0.885* 0.852* 0.820* 0.855* 0.811* 0.862*
MLBQ
1
16.26 8.63 17.44 17.17 15.95 12.28
MLBQ
2
18.43 9.86 8.82 8.62 10.97
MLBQ
1
2 4.26 10.07 4.41 9.22 11.39 7.82
MLBQ
2
2
4.63 10.77 7.02 7.56 13.50
Table 4 displays the bivariate VAR -BEKK estimation results for general indices in Kuwait, USA, and UK stock markets. In panel A conditional mean parameter estimates are reported,

11
and

22
estimate serial autocorrelation in Kuwait stock market and other stock returns respectively,

12
estimates mean spillovers from
Kuwait stock market to the other stock returns,

21
estimates mean spillovers from the other stock returns to Kuwait stock market. Panel B reports the estimates of conditional variance- covariance parameters. a
11
and a
22
estimate the persistence of own market shocks in Kuwait stock market and the other stock returns respectively, b
11
and b
22
estimates the persistence of own market volatility in Kuwait stock markets and the other stock returns respectively. a
12
, a
21
estimate shocks spillovers from / to Kuwait stock market to / from the other stock returns. b
12
, b
21
estimates volatility spillovers from / to Kuwait stock returns to / from the other stock returns. Panel C displays the diagnostic tests,
LBQ
, (
LBQ
2 ) is the multivariate Ljung-Box Q statistic for serial correlation in returns (squared returns) using
12 lags. *, ** and *** indicates significance at 1% 5% and 10% levels of significance.
30

Table 5 : VAR (1)-BEKK estimation results for general stock indices in Oman and regional / global stock markets.
Panel A: Conditional mean estimates

1
SP500 NYSE UK
0.079 0.006 0.070

2
0.182*** 0.190 0.164***

11 ( t
 1 )
0.139* 0.162** 0.129*

11 ( t
 2 )
0.020 0.027 0.005

11 ( t
 3 )

11 ( t
 4 )
-0.001 0.012 0.052

21 ( t
 1 )

21 ( t
 2 )
0.112* 0.121** 0.118**
0.073*** 0.074*** 0.106**

21 ( t
 3 )

21 ( t
 4 )

22 ( t
 4 )
-0.009 0.011 -0.017

22 ( t
 1 )
-0.095 -0.095 -0.024

22 ( t
 2 )

22 ( t
 3 )
0.046 0.027 0.030
0.071 0.084 -0.027
0.047 0.057 -0.013

12 ( t
 1 )

12 ( t
 2 )

12 ( t
 3 )

12 ( t
 4 )
0.040 0.058 0.040
0.025 0.045 0.001
0.061 0.051 0.056
-0.060 -0.058 0.029 c
11
0.339 0.192 0.419**
31

c
12
-0.002 -0.544* 0.440 c
22
0.796* 0.687* 0.306 a
11
0.398* 0.359* 0.372* a
12
-0.176 -0.213 -0.011 a
21
0.068 0.179 0.126** a
22
0.288* 0.371* 0.352* b
11
0.924* 0.931* 0.913* b
12
0.164 0.213 0.004 b
21
-0.096 -0.141 -0.067* b
22
0.828* 0.786* 0.912*
MLBQ
1
16.05 17.78 18.83
MLBQ
2
7.67 9.31 15.91
MLBQ
1
2 25.34** 32.68* 12.43
MLBQ
2
2
15.53 12.18 11.34
Table 5 displays the bivariate VAR -BEKK estimation results for general indices in Oman, USA, and UK stock markets. In panel A conditional mean parameter estimates are reported,

11
and

22
estimate serial autocorrelation in Oman stock market and other stock returns respectively,

12
estimates mean spillovers from
Oman stock market to the other stock returns,

21
estimates mean spillovers from the other stock returns to Oman stock market. Panel B reports the estimates of conditional variance- covariance parameters. a
11
and a
22
estimate the persistence of own market shocks in Oman stock market and the other stock returns respectively, b
11
and b
22
estimates the persistence of own market volatility in Oman stock markets and the other stock returns respectively. a
12
, a
21
estimate shocks spillovers from / to Oman stock market to / from the other stock returns. b
12
, b
21
estimates volatility spillovers from / to Oman stock returns to / from the other stock returns. Panel C displays the diagnostic tests,
LBQ
, (
LBQ
2 ) is the multivariate Ljung-Box Q statistic for serial correlation in returns (squared returns) using
12 lags. *, ** and *** indicates significance at 1% 5% and 10% levels of significance.
32

Table 6 : VAR (1)-BEKK estimation results for general stock indices in Qatar and regional / global stock markets.
Panel A: Conditional mean estimates
Arabia
SP500 NYSE UK

1

2
0.134 0.166 0.149 0.114 0.114
0.127 0.089 0.224* 0.268* 0.205**

11 ( t
 1 )
0.083*** 0.046 -0.011 0.004 0.088

11 ( t
 2 )
0.056 0.062 -0.011 0.006 0.020

11 ( t
 3 )

11 ( t
 4 )
-0.008 0.005 0.011 0.019 0.025
0.142* 0.110** 0.081 0.074 0.135*

21 ( t
 1 )

21 ( t
 2 )

21 ( t
 3 )
0.150** 0.117* 0.078*** 0.082*** 0.103*
-0.091 0.014 0.049 0.031 0.068
0.044 0.041 0.066 0.049 0.033

21 ( t
 4 )
0.049 0.008 0.040 0.036 -0.018

22 ( t
 1 )
0.077 0.153*

22 ( t
 2 )

22 ( t
 3 )
-0.055 -0.004 0.022 -0.018 0.019
0.023 0.043 0.061 0.034 -0.048

22 ( t
 4 )

12 ( t
 1 )

12 ( t
 2 )
0.062 0.011 0.031 0.018 0.011
0.041 0.019 0.022 0.020 0.016

12 ( t
 3 )

12 ( t
 4 )
0.054** -0.019 0.0002 0.001 0.041
0.052*** 0.016 0.007 0.005 0.003 c
11
33

c
12 c
22
-0.00006 0.359* 0.364*** 0.271 0.00002 a
11
0.393* 0.424* 0.424* 0.411* 0.445* a
12
0.072*** 0.129 0.069 -0.014 -0.001 a
21
0.308* 0.121 -0.019 0.021 0.075 a
22
0.467* 0.615* 0.514* 0.600* 0.421* b
11
0.913* 0.898* 0.922* 0.927* 0.906* b
12
-0.018 -0.081 -0.02 -0.0001 -0.002 b
21 b
22
0.858* 0.769* 0.815* 0.795* 0.881*
MLBQ
1
12.55 12.86 12.14 12.91 13.99
MLBQ
2
19.95*** 4.85 5.26 6.53 13.55
MLBQ
1
2 8.90 3.95 4.50 4.88 5.60
MLBQ
2
2 9.31 7.44 7.93 11.30
Table 6 displays the bivariate VAR -BEKK estimation results for general indices in Qatar, USA, and UK stock markets. In panel A conditional mean parameter estimates are reported,

11
and

22
estimate serial autocorrelation in Qatar stock market and other stock returns respectively,

12
estimates mean spillovers from
Qatar stock market to the other stock returns,

21
estimates mean spillovers from the other stock returns to Qatar stock market. Panel B reports the estimates of conditional variance- covariance parameters. a
11
and a
22
estimate the persistence of own market shocks in Qatar stock market and the other stock returns respectively, b
11
and b
22
estimates the persistence of own market volatility in Qatar stock markets and the other stock returns respectively. a
12
, a
21
estimate shocks spillovers from / to Qatar stock market to / from the other stock returns. b
12
, b
21
estimates volatility spillovers from / to Qatar stock returns to / from the other stock returns. Panel C displays the diagnostic tests,
LBQ
, (
LBQ
2 ) is the multivariate Ljung-Box Q statistic for serial correlation in returns (squared returns) using
12 lags. *, ** and *** indicates significance at 1% 5% and 10% levels of significance.
34

Table 7 : VAR (1)-BEKK estimation results for general stock indices in Saudi Arabia and regional / global stock markets.
Panel A: Conditional mean estimates

1

2
0.181*** 0.134 0.179*** 0.117
0.219** 0.224* 0.315* 0.277*

11 ( t
 1 )
0.117*** 0.192* 0.197*

11 ( t
 2 )
0.016

11 ( t
 3 )

11 ( t
 4 )
0.023
-0.043

21 ( t
 1 )

21 ( t
 2 )
0.141* 0.042
0.0005
0.036 0.065

21 ( t
 3 )

21 ( t
 4 )
0.050
-0.050

22 ( t
 1 )
0.018 -0.125*** -0.063

22 ( t
 2 )

22 ( t
 3 )

22 ( t
 4 )
0.017
0.056
0.054

12 ( t
 1 )

12 ( t
 2 )

12 ( t
 3 )

12 ( t
 4 )
0.108* -0.034 -0.039 -0.029
-0.006
0.017
-0.012 c
11
1.012* 1.033* 1.105* 0.952*
35

c
12 c
22 a
11 a
12
0.526* 0.692* 0.665* 0.555*
0.016 0.030 0.027 -0.08 a
21 a
22
0.398* -0.496* -0.546* 0.417* b
11
0.807* 0.704* 0.715* 0.806* b
12
-0.016 -0.030 -0.045 0.050 b
21
-0.070 0.210* 0.165* -0.050 b
22
0.913* 0.856* 0.852* 0.875*
MLBQ
1
6.99 5.79 5.75 8.33
MLBQ
2
13.53
MLBQ
1
2 6.66 6.65 6.90 8.81
MLBQ
2
2 22.56** 8.97 9.12 17.58
Table 7 displays the bivariate VAR -BEKK estimation results for general indices in Saudi Arabia, USA, and UK stock markets. In panel A conditional mean parameter estimates are reported,

11
and

22
estimate serial autocorrelation in Saudi Arabia stock market and other stock returns respectively,

12
estimates mean spillovers from Saudi Arabia stock market to the other stock returns,

21
estimates mean spillovers from the other stock returns to Saudi Arabia stock market. Panel B reports the estimates of conditional variance- covariance parameters. a
11
and a
22
estimate the persistence of own market shocks in Saudi Arabia stock market and the other stock returns respectively, b
11
and b
22
estimates the persistence of own market volatility in Saudi Arabia stock markets and the other stock returns respectively. a
12
, a
21
estimate shocks spillovers from / to Saudi Arabia stock market to / from the other stock returns. b
12
, b
21
estimates volatility spillovers from / to Saudi
Arabia stock returns to / from the other stock returns. Panel C displays the diagnostic tests,
LBQ
, (
LBQ
2 ) is the multivariate Ljung-Box Q statistic for serial correlation in returns (squared returns) using 12 lags. *, ** and *** indicates significance at 1% 5% and 10% levels of significance.
36