Global and Regional Volatility Spillover PDF

8/11/2019 Global and Regional Volatility Spillover PDF

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Global and Regional Volatility Spillover

By

Mohamed Abdelaziz Eissa1

Qatar University

Abstract

In this paper, we employ a Multivariate GARCH model to study volatility spillovers between USA, UK asproxy 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 evidenceof cross-market effects from the GCC stock markets to USA or UK, we did not find evidence of shock orvolatility spillovers from USA, and UK to all GCC stock markets, while there is evidence of shock andvolatility spillover among the GCC stock markets. This results may be due to the restrictions on theaccessing of the foreign investors to these markets, some markets allow the foreigner to access but theother stock markets not.

Keywords: Stock returns, volatility spillovers, multivariate GARCH models, GCC region

JEL classification:C22, F31, G12, G15

Classification:Research paper

1Economic and Finance department, College of Business and Economics, Qatar University, Doha, Qatar, P.O. 2713, Email:[email protected]


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1. Introduction

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 financia

systems have been an essential channel for advancing their trade integration into the global community


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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 investigation2.

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.

Literature review

2http://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 volatility-

spillover 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. gert 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. Gbka and Serwa

(2007) have also supported the fact that even if being significant both within and across the region, intra-

regional 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. Data and Descriptive Statistics:

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:

100*)/ln( 1 ttt RRr (1)

where tR and 1tR are the closing values for the trading days tand 1t , 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.

4.

Methodology

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:

ttttrrr 11,2121,11111 (2)

ttttrrr 21,2221,12122 (3)

where tr is an n1 vector of weekly return at time t for each market with 1rand 2r being the returns on GCC

and UK or USA stock markets, respectively, and 1\ (0, )t t tI H . The n1 vector of random errors t is the

innovation for each market at time t with its corresponding nn conditional variance-covariance matrix,Ht. The

market information available at time t - 1 is represented by the information set 1tI . The n1 vector, represent

long-term drift coefficients. Following Karolyi (1995), the elements of the matrix and can provide


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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 variance-

covariance 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 t and volatilityHt for each market lagged one period. One important feature of this specification

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 o

parameters (Karolyi, 1995). The model also ensures the condition of a positive semi-definite conditiona

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:

BHBAACCH tttt 111 (4)

where ijc are elements of an nn symmetric matrix of constants C, tH 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 cross-

market influences in the conditional variance (Karolyi, 1995), specifically, the diagonal elements of matrix A

11a and 22a , measure shock persistence in GCC stock market and stock returns in USA, or UK respectively,

however, 11b and 22b 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 12a of the symmetric nn

matrix A measure the degree of innovation from GCC stock returns to UK, or USA stock returns, and the


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elements 12b of the symmetric nn 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:

(5)

Equations (6)-(8) below solve for the cross effects in the variance and covariance equations implied by the

BEKK specifications.

1,222211,122111

,112

112

1,22211,21,12111

21,1

211

211,11

2

2

tt

tttttt

hbhbb

hbaaaach (6)

(7)

1,222221,122212

,11212

21,2

2221,21,12212

21,1

212

222

212,22

2

2

tt

tttttt

hbhbb

hbaaaacch (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)):

1,2222211,12221112211,111211

21,222211,21,122111221

21,112112111,12

)(

)(

ttt

ttttt

hbbhbbbbhbb

aaaaaaaacch

2221

1211

122121

112111

'

2221

1211

2221

1211

2121112

12112

11

'

2221

1211

2221

1211

bb

bb

HH

HH

bb

bb

aa

aa

aa

aaCC

HH

HH

tt

tt

ttt

ttt

tt

tt


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T

t

tttt HHTn

L1

1 )||||(ln2

1)2ln(

2)( (9)

where T is the number of observations, n is the number of markets, 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 errors3.

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 Qstatistic is given by:

p

j

jrjTTTLBQ1

21 )()()2( (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.

5.

Empirical Results

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 )1(22 t 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 coefficient11

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

3We used the RATS program for estimating the MGARCH using QML which estimates the likelihood function under the assumptionthat the contemporaneous errors have a joint normal distribution. Using the robust errors and lag options we corrected the covariancematrix estimate to allow for more complex behaviour of the residuals, this option corrects the heteroscedasticity and serial correlationthis 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 11a , 22a , 11b and 22b . Furthermore, there is evidence of

shock spillover effect from Abu Dhabi to Oman as 12a 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

12a and 21a 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 21b 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 residuals4.

In Table 3, Panel A records the conditional mean estimation results for Bahrain. Evidence of serialautocorrelation is found for Abu Dhabi, Dubai, Kuwait, Oman, Qatar, Saudi Arabia, SP500, NYSE, and

UK stock returns as the coefficients )1(22 t 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 resultspoints 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

4The 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 22a and 22b , reveal evidence of some degree of persistence in all stock returns.The statically significant 11a and 11b 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 12b 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 21b 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 specified5.

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 1LBQ and2

1LBQ for SP500 and NYSE are significant therefore the results of these countries should be taken with caution.


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significant coefficients 22a and 22b , reveal evidence of some degree of persistence in Oman, Qatar, Saudi

Arabia, SP500, NYSE, and UK stock returns. The statically significant 11a and 11b 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 12b 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 21b in Oman and Qatar, suggests the presence of volatility spillover effects from

those countries stock returns to the volatility of Kuwaiti stock return.


both the standardised and squared standardised residuals for stock returns suggests that the VAR -BEKKmodel is well specified6.

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.


significant coefficients 22a and 22b , reveal evidence of some degree of persistence in SP500, NYSE, and

UK stock returns. The statically significant11

a and11

b 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 21LBQ for SP500 and NYSE are significant therefore the results of these countries should be taken with caution.


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both the standardised and squared standardised residuals for stock returns suggests that the VAR -BEKK

model is well specified7

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.


significant coefficients 22a and 22b , reveal evidence of some degree of persistence in Oman, Saudi

Arabia, SP500, NYSE, and UK stock returns. The statically significant 11a and 11b 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.



model is well specified8.

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 1LBQ and2

1LBQ for Oman are significant therefore the results of these countries should be taken with caution.

8 1LBQ and2



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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 statisticallysignificant coefficients 22a and 22b , reveal evidence of own market shock and volatility Oman, SP500,

NYSE, and UK stock returns. The statically significant 11a and 11b 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,



model is well specified9.

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 isbidirectional 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 1LBQ and2



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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 onlyexception 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

GCCs 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. Summary and conclusion

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


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19

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24

Table 1: Descriptive statistics

AbuDhabi

Bahrain Kuwait Oman Qatar SaudiArabia

SP500 NYSE UK

Mean 0.163 -0.076 0.210 0.0218 0.108 0.035 0.057 0.065 0.023

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

9.800 3.973* 4.328 14.604* 8.67* 6.585* 4.043* 4.662* 3.190*

JB 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.


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25

Table 2 : VAR (1)-BEKK estimation results for general stock indices in Abu Dhabi and regional / global

stock markets.

Panel A: Conditional mean estimates

Kuwait Oman Qatar Saudi Arabia SP500 NYSE UK1 0.118 0.178** 0.104 0.204** 0.145*** 0.147*** 0.123

2 0.254** 0.248* 0.148 0.264** 0.229* 0.259** 0.195**

)1(11 t 0.136* 0.162* 0.093 0.080** 0.049 0.049 0.102***

)2(11 t 0.0434 0.050 0.087 0.076*** 0.039 0.037 0.064

)3(11 t -0.011 0.068*** 0.066

)4(11 t 0.020

)1(21 t 0.0173 -0.029 0.0069 0.062** 0.041 0.054** 0.101*

)2(21 t 0.084* -0.006 -0.005 0.0006 0.040 0.042*** 0.038

)3(21 t 0.026 0.0473 0.0464

)4(21 t 0.086***

)1(22 t 0.199* 0.063 0.118 0.226* -0.122* -0.102** -0.057**

)2(22 t 0.109** -0.037 -0.031 0.020 0.002 0.004 -0.013

)3(22 t 0.120* 0.083* 0.079**

)4(22 t 0.095

)1(12 t 0.0136 0.166* 0.082 0.059 0.010 -0.00003 -0.009

)2(12 t 0.062* 0.016 0.150*** 0.013 -0.015 -0.016 0.037

)3(12 t -0.126** -0.011 0.004

)4(12 t 0.043

11c 0.362** 0.338* 0.391* 0.263*** 0.377* 0.394* 0.440***


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26

12c 0.050 0.248 0.129 0.535 -0.118 -0.186 -0.133

22c 0.378* -0.430** 0.565 0.917* 0.631* 0.640* 0.525*

11a 0.288* 0.338* 0.382* 0.362* 0.382* 0.392* 0.451*

12a -0.12** 0.101* 0.082 0.160*** 0.024 0.003 -0.031

21a 0.256** 0.048 -0.017 0.080** 0.035 0.037 0.061**

22a 0.479* 0.386* 0.488* 0.551* 0.495* 0.545* 0.415*

11b 0.941* 0.941* 0.917* 0.949* 0.924* 0.918* 0.895*

12b 0.044 -0.021 -0.023 0.005 -0.003 0.002 0.018

21b -0.09** -0.029 0.0192 -0.064* -0.005 -0.002 -0.012

22b 0.859* 0.884* 0.875* 0.760* 0.842* 0.822* 0.888*

1MLBQ 14.426 17.24 9.99 16.50 16.42 16.12 15.92

2MLBQ 15.54 17.14 15.33 8.53 5.83 5.15 9.76

21MLBQ 4.56 6.76 6.85 6.55 7.32 6.74 3.61

22MLBQ 6.68 34.04* 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. 11a and 22a estimate the persistence of own market shocks in Abu Dhabi stock market and the other stock

returns respectively, 11b and 22b estimates the persistence of own market volatility in Abu Dhabi stock markets and the other stock returns respectively. 12a , 21a

estimate shocks spillovers from / to Abu Dhabi stock market to / from the other stock returns. 12b , 21b estimates volatility spillovers from / to Abu Dhabi stock

returns to / from the other stock returns. Panel C displays the diagnostic tests, )(, 2LBQLBQ 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.


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Table 3 : VAR (1)-BEKK estimation results for general stock indices in Bahrain and regional / global

stock markets.


AbuDhabi Dubai Kuwait Oman Qatar SaudiArabia SP500 NYSE UK

1 -0.080 -0.067 0.006 -0.050 -0.077 -0.049 0.022 -0.032 -0.05

2 0.124* 0.074 0.195** 0.203** 0.037 0.199*** 0.310* 0.399* 0.209**

)1(11 t 0.079 0.084** 0.024 0.159* 0.082** 0.088** 0.149* 0.126* 0.094*

)2(11 t 0.107** 0.028 0.146* 0.137*

)3(11 t 0.078***

)4(11 t -0.002

)1(21 t 0.104* 0.056* 0.211* 0.136* 0.030 0.067* 0.095** 0.086* 0.070*

)2(21 t 0.026** 0.046** 0.014 0.086*

)3(21 t 0.008

)4(21 t 0.030**

)1(22 t 0.119*** 0.064 0.331* 0.129** 0.086 0.145* -0.140* -0.143* -0.018*

)2(22 t 0.123** 0.068 0.007 0.037

)3(22 t 0.017

)4(22 t 0.194*

)1(12 t 0.050 0.270 0.068 0.092 -0.045 0.091 -0.059 -0.028 -0.060

)2(12 t

0.097 -0.106 0.133*** -0.052

)3(12 t 0.097

)4(12 t -0.238*

Panel B: conditional variance-covariance estimates


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28

11c 0.404 0.001 0.460* 1.052* 0.250 0.246 0.976** 0.935* 0.374**

12c 0.615* -1.07** 0.341* 0.136 0.297 0.979* 0.367*** 0.300 0.255

22c -0.0007 -0.003 0.002 0.0001 -0.0001 0.0002* -0.0004 0.001 0.470

11a 0.185 0.186*** 0.278* 0.331* 0.192* 0.162* -0.126 0.077 0.217*

12a 0.163 0.082 0.237* -0.005 0.445* 0.140 0.148 -0.038 -0.017

21a 0.126* 0.073* 0.074** 0.091* 0.083* 0.096* -0.086 -0.052 0.060***

22a 0.440* 0.453* 0.429* 0.408* 0.390* 0.640* 0.519* 0.620* 0.464*

11b 0.930* 0.992* 0.890* 0.554* 0.965* -0.904* 0.683** 0.735* 0.941*

12b -0.112 0.184 -0.163* -0.198** -0.046 0.562* -0.42*** -0.372* -0.043

21b -0.044** -0.047* 0.017 0.067*** -0.034* 0.237* 0.166 0.127** -0.021

22b 0.892* 0.835* 0.886* 0.955* 0.897* 0.671* 0.827* 0.803* 0.871*

Panel C: Test for model fitness

1MLBQ 17.30 11.90 14.11 18.11 12.57 15.18 23.58** 22.68* 16.11

2MLBQ 17.98 10.43 12.22 18.81 5.71 5.27 6.73 7.24 12.80

21MLBQ 5.56 3.75 8.43 11.83 2.94 6.51 20.94*** 24.4* 9.36

2

2MLBQ 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, 12 estimates mean spillovers

from Bahrain stock market to the other stock returns, 21 estimates mean spillovers from the other stock returns to Bahrain stock market. Panel B reports the

estimates of conditional variance- covariance parameters. 11a and 22a estimate the persistence of own market shocks in Bahrain stock market and the other stock

returns respectively, 11b and 22b estimates the persistence of own market volatility in Bahrain stock markets and the other stock returns respectively. 12a , 21a

estimate shocks spillovers from / to Bahrain stock market to / from the other stock returns. 12b , 21b estimates volatility spillovers from / to Bahrain stock returns to /

from the other stock returns. Panel C displays the diagnostic tests, )(, 2LBQLBQ 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.


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29

Table 4 : VAR (1)-BEKK estimation results for general stock indices in Kuwait and regional / global

stock markets.


Oman Qatar SaudiArabia

SP500 NYSE UK

1 0.203** 0.184*** 0.245** 0.193** 0.169*** 0.169***

2 0.285* 0.343 0.376* 0.217* 0.257* 0.183**

)1(11 t 0.289* 0.282* 0.289* 0.214* 0.205* 0.245*

)2(11 t 0.018 0.111* 0.124* 0.117*

)3(11 t -0.0005

)4(11 t 0.101**

)1(21 t 0.107* 0.044* 0.057* 0.100* 0.098* 0.132*

)2(21 t 0.010 0.015 0.016 0.030

)3(21 t 0.045**

)4(21 t

-0.001

)1(22 t 0.080 0.130* 0.161* -0.09** -0.082** -0.028

)2(22 t -0.003 0.006 -0.003 -0.033

)3(22 t 0.057

)4(22 t 0.071

)1(12 t

0.160* 0.047 0.042 0.053 0.051 0.042

)2(12 t 0.019 0.010 0.026 0.032

)3(12 t 0.037

)4(12 t -0.018


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30

11c 0.476** 0.626* 0.516*** 0.367* 0.352* 0.389

12c 0.525** -0.118 -0.010 0.091 0.022 0.148

22c 0.525*8 0.590* 0.926* 0.604* 0.679* 0.574*

11a 0.407* 0.489* 0.497* 0.361* 0.338* 0.392*

12a 0.055 0.135 0.017 0.043 -0.013 -0.048

21a 0.125*** 0.097*** 0.039 -0.018 0.019 0.031

22a 0.393* 0.444 0.509* 0.479* 0.561* 0.467*

11b 0.878* 0.814* 0.851* 0.917* 0.926* 0.905*

12b 0.024 0.236** 0.078 -0.009 0.013 0.025

21b -0.06*** -0.056** -0.032 0.004 -0.010 -0.025

22b 0.885* 0.852* 0.820* 0.855* 0.811* 0.862*

1MLBQ 16.26 8.63 17.44 17.17 15.95 12.28

2MLBQ 18.81*** 18.43 9.86 8.82 8.62 10.97

21MLBQ 4.26 10.07 4.41 9.22 11.39 7.82

22MLBQ 20.61** 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. 11a and 22a estimate the persistence of own market shocks in Kuwait stock market and the other stock returns

respectively, 11b and 22b estimates the persistence of own market volatility in Kuwait stock markets and the other stock returns respectively. 12a , 21a estimate

shocks spillovers from / to Kuwait stock market to / from the other stock returns. 12b , 21b estimates volatility spillovers from / to Kuwait stock returns to / from the

other stock returns. Panel C displays the diagnostic tests, )(, 2LBQLBQ 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.


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31

Table 5 : VAR (1)-BEKK estimation results for general stock indices in Oman and regional / global stock

markets.


SP500 NYSE UK1 0.079 0.006 0.070

2 0.182*** 0.190 0.164***

)1(11 t 0.139* 0.162** 0.129*

)2(11 t 0.020 0.027 0.005

)3(11 t -0.001 0.012 0.052

)4(11 t 0.076 0.091** 0.092*

)1(21 t 0.112* 0.121** 0.118**

)2(21 t 0.073*** 0.074*** 0.106**

)3(21 t 0.119 0.116** 0.032

)4(21 t -0.009 0.011 -0.017

)1(22 t -0.095 -0.095 -0.024

)2(22 t 0.046 0.027 0.030

)3(22 t 0.071 0.084 -0.027

)4(22 t 0.047 0.057 -0.013

)1(12 t 0.040 0.058 0.040

)2(12 t 0.025 0.045 0.001

)3(12 t 0.061 0.051 0.056

)4(12 t -0.060 -0.058 0.029

11c 0.339 0.192 0.419**


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12c -0.002 -0.544* 0.440

22c 0.796* 0.687* 0.306

11a 0.398* 0.359* 0.372*

12a -0.176 -0.213 -0.011

21a 0.068 0.179 0.126**

22a 0.288* 0.371* 0.352*

11b 0.924* 0.931* 0.913*

12b 0.164 0.213 0.004

21b -0.096 -0.141 -0.067*

22b 0.828* 0.786* 0.912*

1MLBQ 16.05 17.78 18.83

2MLBQ 7.67 9.31 15.91

21MLBQ 25.34** 32.68* 12.43

22MLBQ 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 s tock 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. 11a and 22a estimate the persistence of own market shocks in Oman stock market and the other stock returns

respectively, 11b and 22b estimates the persistence of own market volatility in Oman stock markets and the other stock returns respectively. 12a , 21a estimate

shocks spillovers from / to Oman stock market to / from the other stock returns. 12b , 21b estimates volatility spillovers from / to Oman stock returns to / from the




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Table 6 : VAR (1)-BEKK estimation results for general stock indices in Qatar and regional / global stock

markets.


Oman SaudiArabia SP500 NYSE UK

1 0.134 0.166 0.149 0.114 0.114

2 0.127 0.089 0.224* 0.268* 0.205**

)1(11 t 0.083*** 0.046 -0.011 0.004 0.088

)2(11 t 0.056 0.062 -0.011 0.006 0.020

)3(11 t -0.008 0.005 0.011 0.019 0.025

)4(11 t 0.142* 0.110** 0.081 0.074 0.135*

)1(21 t 0.150** 0.117* 0.078*** 0.082*** 0.103*

)2(21 t -0.091 0.014 0.049 0.031 0.068

)3(21 t 0.044 0.041 0.066 0.049 0.033

)4(21 t 0.049 0.008 0.040 0.036 -0.018

)1(22 t 0.077 0.153* -0.079 -0.057 0.011

)2(22 t -0.055 -0.004 0.022 -0.018 0.019

)3(22 t 0.023 0.043 0.061 0.034 -0.048

)4(22 t 0.062 0.011 0.031 0.018 0.011

)1(12 t 0.070* 0.105* -0.005 -0.008 -0.013

)2(12 t

0.041 0.019 0.022 0.020 0.016

)3(12 t 0.054** -0.019 0.0002 0.001 0.041

)4(12 t 0.052*** 0.016 0.007 0.005 0.003

11c 0.482* 0.716** -0.35*** -0.370 0.500*


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34

12c 0.587* 1.127* -0.602*8 -0.669* 0.603**

22c -0.00006 0.359* 0.364*** 0.271 0.00002

11a 0.393* 0.424* 0.424* 0.411* 0.445*

12a 0.072*** 0.129 0.069 -0.014 -0.001

21a 0.308* 0.121 -0.019 0.021 0.075

22a 0.467* 0.615* 0.514* 0.600* 0.421*

11b 0.913* 0.898* 0.922* 0.927* 0.906*

12b -0.018 -0.081 -0.02 -0.0001 -0.002

21b -0.165* -0.07** -0.026 -0.042 -0.074

22b 0.858* 0.769* 0.815* 0.795* 0.881*

1MLBQ 12.55 12.86 12.14 12.91 13.99

2MLBQ 19.95*** 4.85 5.26 6.53 13.55

21MLBQ 8.90 3.95 4.50 4.88 5.60

22MLBQ 31.00* 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. 11a and 22a estimate the persistence of own market shocks in Qatar stock market and the other stock returns

respectively, 11b and 22b estimates the persistence of own market volatility in Qatar stock markets and the other stock returns respectively. 12a , 21a estimate

shocks spillovers from / to Qatar stock market to / from the other stock returns. 12b , 21b estimates volatility spillovers from / to Qatar stock returns to / from the




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Table 7 : VAR (1)-BEKK estimation results for general stock indices in Saudi Arabia and regional /

global stock markets.


Oman SP500 NYSE UK1 0.181*** 0.134 0.179*** 0.117

2 0.219** 0.224* 0.315* 0.277*

)1(11 t 0.117*** 0.192* 0.197* 0.115

)2(11 t 0.016

)3(11 t 0.023

)4(11 t -0.043

)1(21 t 0.141* 0.042 0.036 0.065

)2(21 t 0.0005

)3(21 t 0.050

)4(21 t -0.050

)1(22 t 0.018 -0.125*** -0.12*** -0.063

)2(22 t 0.017

)3(22 t 0.056

)4(22 t 0.054

)1(12 t 0.108* -0.034 -0.039 -0.029

)2(12 t -0.006

)3(12 t 0.017

)4(12 t -0.012

11c 1.012* 1.033* 1.105* 0.952*


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12c 0.304*** -0.135 -0.012 0.113

22c 0.344*** 0.659* 0.614* 0.488*

11a 0.526* 0.692* 0.665* 0.555*

12a 0.016 0.030 0.027 -0.08

21a 0.241 -0.454* -0.418* 0.183

22a 0.398* -0.496* -0.546* 0.417*

11b 0.807* 0.704* 0.715* 0.806*

12b -0.016 -0.030 -0.045 0.050

21b -0.070 0.210* 0.165* -0.050

22b 0.913* 0.856* 0.852* 0.875*

1MLBQ 6.99 5.79 5.75 8.33

2MLBQ 20.15*** 7.78 7.69 13.53

21MLBQ 6.66 6.65 6.90 8.81

22MLBQ 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. 11a and 22a estimate the persistence of own market shocks in Saudi Arabia stock market and the other

stock returns respectively, 11b and 22b estimates the persistence of own market volatility in Saudi Arabia stock markets and the other stock returns respectively.

12a , 21a estimate shocks spillovers from / to Saudi Arabia stock market to / from the other stock returns. 12b , 21b estimates volatility spillovers from / to Saudi

Arabia stock returns to / from the other stock returns. Panel C displays the diagnostic tests, )(, 2LBQLBQ 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.

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