Contagion effects of the Global Financial Crisis on GCC stock markets
By
Zhang Hengchao* & Zarinah Hamid#
Abstract
The objectives of this study are twofold: first, it is to investigate whether contagion effects of
current Global Financial Crisis present in Gulf Cooperation Council (GCC) stock markets;
second, it is to evaluate the impact of such financial crisis on the long-run and short-run dynamic
relationships between GCC stock markets and leading conventional and Islamic stock markets
around the globe, over the period from July 7, 2004 to August 3, 2012. In order to achieve the
two objectives, this study employs three major investigation techniques. First, both standard
Pearson correlation and heteroscedasticity adjusted correlation tests are used to investigate the
evidence of contagion effects in GCC stock markets. Then, Vector Autoregression (VAR) based
Johansen-Juselius (JJ) Cointegration tests are employed to investigate the existence of long-run
equilibrium relationships among the stock markets. Finally, Variance Decompositions (VDCs)
function is utilized to evaluate the dynamic interactions and strength of causal relationships
among the stock markets in the short-run. It is expected that the findings of this paper will not
only shed some light on the benefits of international portfolios diversification into GCC stock
markets, but also provide guidelines for policymakers to structure appropriate economic policies
to stabilize the economy in the wake of the international financial shocks.
Keywords: Global Financial Crisis, GCC stock markets, Correlation, Cointegration, VDCs,
Diversification benefits
* PhD Candidate, Institute of Islamic Banking and Finance, International Islamic University,
50728 Kuala Lumpur, Malaysia
#
Associate Professor, Faculty of Economics and Management Sciences, International Islamic
University, 50728 Kuala Lumpur, Malaysia
Email: zhanghengchao_919@hotmail.com ; Phone: +60-183954098
1
1. Introduction
Ever since late 1980s, emerging economies have started liberalizing their financial markets to
attract foreign investors with the hope of fueling their economic growth. However, for many of
these countries their financial sectors are lack of sound financial supervision and management.
With the deficiency of financial supervision, the vulnerability of these financial markets to the
external shocks has increased significantly as they move aggressively towards more integrated
and globalized financial markets (Khalid & Kawai, 2003). The incidents of such increased
vulnerability can be evidenced from the variety of financial crises among emerging economies
over the past twenty years, namely, the crises in Mexico in 1994, Asia in 1997, Russia in 1998,
and Brazil in 1999.
One of the most noticeable characteristics of these crises is that, these financial crises
were all initiated by the local turbulences but eventually spilled over to other markets, despite the
existence of little or no economic linkages between affected countries and the initiators. Such
phenomenon has often been described as financial contagion (Khalid & Kawai 2003; Račickas &
Vasiliauskaitė 2011). Although effects of financial contagion are critical for both international
portfolio investors and domestic financial policy makers, there have been precious few
researches on this topic. Only after Asian financial crisis, researches on this subject have been
intensified (Chung 2005; Fahami 2011; Khalid & Kawai 2003). However, most of these
researches have been devoted to explore the interdependence and contagion effects among
developed countries as well as Asian emerging economies. Despite the rapid growth and
liberalization of GCC financial markets, trivial efforts have been made to explore the contagion
effects of the financial crisis from global and regional stock markets to GCC stock markets
(Sedik & Williams 2011).
2
During the last few years, the Global Financial Crisis triggered by U.S. Subprime Crisis
has severely affected many countries all over the world. As GCC countries vigorously liberalize
their financial markets and hence integrate into world economy, researches on the contagion
effects of the Global Financial Crisis on the stock markets of the GCC member countries are
greatly needed. It is because that the study of contagion effects may shed some light on the
potential benefits of diversifying international portfolios into GCC stock markets, and hence help
financial authorities in GCC countries to structure effective policies to curb the contagion effects
of the financial crisis.
The objectives of this paper are twofold: first, it is to investigate whether current Global
Financial Crisis has contagion effects on the GCC stock markets. Second, it is to evaluate the
impact of current Global Financial Crisis on the long-run and short-run dynamic relationships
between GCC stock markets and leading conventional and Islamic stock markets around the
globe.
The structure of this paper is organized as follow: Section 2 briefs the past researches on
contagion effect. Section 3 presents the data and methodology. Section 4 discusses the empirical
results of correlation analysis, cointegration tests, and variance decomposition function tests.
Some concluding remarks and policy implications have then been presented in Section 5.
2. Literature review
To understand the contagion effects of the Global Financial Crisis comprehensively, this section
of the paper provides a brief review of past researches on both concepts as well as empirical
analysis of financial contagion.
3
As for the definition of contagion, there are various versions. In the broadest sense, some
researches described contagion as the phenomenon where the country-specific events quickly
spread to other countries around the globe (Caporale, Cipollini, & Spagnolo, 2005; Candelon
2010; Račickas & Vasiliauskaitė 2011). Specifically, some researches took into consideration the
role of the fundamental interdependence, such as the financial and trade linkages in the case of
contagion. For instance, Fraztscher (2002) considered contagion as the transmission of a crisis to
a particular country due to its real and financial interdependence with the crisis initiator. In
contrast, Pericoli & Sbracia (2003) referred contagion as the phenomenon of excess
comovements between markets which cannot be explained by macroeconomic fundamental
factors. As extension, other researchers added that such excess comovements between markets
may occur when a shock in one country is transmitted to another country through cross market
balancing, which can be commonly explained by investors’ herding behavior (Kose et al. 2007;
Kaminsky & Reinhart 2000). Last but not least, Forbes & Rigobon (2002) defined contagion
uniquely as the significant increase in the correlation between markets, after a shock hit a market
or group of markets. According to this study, contagion occurs only if the cross-market
movements increase significantly after the crisis. If there does not exist significant increase of
the comovement, these markets are only believed to be interdependent. By defining the
contagion effect as significantly increased comovements between markets, in comparison with
other definitions, it offers two advantages for the empirical analysis: a) the phenomenon can be
tested directly and simply through correlation coefficients, b) it allows one to distinguish
between crisis-contingent and non-crisis contingent (Forbes & Rigobon 2002). Have noted the
benefits of such definition, this paper have adopted the definition of contagion introduced by
Forbes & Rigobon (2002).
4
A variety of empirical methodologies can be found in the past researches to test the
effects of financial contagion. In sum, four methods have been commonly applied, namely,
correlation test, time-varying volatility models, Cointegration techniques, and Probi-Logit test.
The correlation analysis was believed to be first introduced by King & Wadhwani (1990). The
study found that the correlations between U.K., U.S. and Japan stock markets have increased
significantly after the stock market crash of 1987. Then, Calvo & C. Reinhart (1996) applied the
same approach to test the contagion effects of the Mexico crisis, and found increased
comovements across weekly equity and Brady bond returns among emerging Latin American
markets after the Mexico crisis.
However, this standard correlation test have been criticized by Forbes & Rigobon (2002)
for the presence of heteroscedasticity bias. As the paper explained when stock market volatility
increases, the market correlations will be biased upward. Such upward bias of correlations will
ultimately lead to false conclusions on the existence of contagion effects between markets. To
overcome the problem of such correlation bias, Forbes & Rigobon (2002) introduced
heteroscedasticity adjusted correlation tests, where the heteroscedasticity bias has been corrected.
After applying the heteroscedasticity adjusted correlation tests, the authors did not find
significant contagion effects in the cases of the Asian, the Mexican and the 1987 US crises. Such
findings were found to be contradicted with the results given by unadjusted correlation tests.
The heteroscedasticity adjusted correlation tests have been applied extensively in the
latter researches. For instance, Collins & Biekpe (2003) adopted the adjusted correlation test to
investigate the whether African equity markets suffered from the contagion effects of Asian
financial crisis. The study concluded that except Egypt and South Africa, all the other Africa
equity markets did not suffer contagion effects from Asian financial crisis. In addition, Hon et al.
5
(2004) applied the same technique to examine whether the terrorist attack in the U.S. on
September 11, 2011 resulted in a contagion effects. The study indicated that international stock
markets, particularly in Europe, experienced contagion effects in the three to six months after the
crisis. Furthermore, Chiang et al. (2007) used this method to examine whether contagion effects
of Asian financial crisis present in the Asian markets, and the study confirmed the existence of
the contagion effects in the region. Most recently, Lee (2012) applied this method to investigate
whether international stock markets suffer from the contagion effects of the U.S. subprime crisis.
The study concluded that out of the 20 international stock markets, only Hong Kong, Taiwan,
Australia, and New Zealand suffered from the contagion effects.
The Autoregressive Conditional Heteroscedasticity (ARCH) process proposed by Engle
(1982) and the Generalized ARCH (GARCH) developed by Bollerslev (1986) have been well
acknowledged for measuring the volatility spillover of the stock market returns. For instance,
Hamao, Masulis, & Ng (1990) applied GARCH method and confirmed the existence of
contagion effects across markets after the 1987 U.S. stock market crash. Similarity, in examining
the volatility spillover effects of 1994 Mexico crisis on bond returns across markets, Edwards
(2000) also applied GARCH method. He found that the volatility is transmitted from one country
to another but it is uncertain about the propagation mechanisms during the crisis time. Recently,
Engle (2002) introduced a multivariate GARCH model with time-varying conditional
correlations—dynamic conditional correlation (DCC) model to examine the contagion effects of
stock markets. In comparison with other estimation techniques, it is believed that DCC-GARCH
model has three advantages. First, DCC-GARCH model accounts for heteroscedasticity. Second,
additional explanatory variables can be included in the mean equation to measure the common
factor. Third, it is the multivariate form of GARCH model which can be used to examine
6
multiple asset returns without adding too many parameters (Chiang et al. 2007). In Caporale et al.
(2005), the study used DCC-GARCH to examine the contagion effects of Asian financial crisis
on East Asian region, and then they found existence of contagion within the region. Similarly,
applying the same method on a more extensive time duration, Chiang et al. (2007) echoed with
the previous research and confirmed the existence of contagion effects during the Asia crisis
period. However, since most of GARCH-modeled contagion studies measure contagion as the
excess correlation in the residuals of the model after controlling for fundamentals, it is difficult
to discriminate whether excess comovement captured by the residuals is caused by global shocks
or the omitted variables in the model (Forbes 2012)
Some studies adapt Cointegration tests to examine the long-run relationship between
markets and investigate the contagion effect through analyzing the extent of increased
comovement during the crisis period, in comparison with the tranquil period (Yang & Lim 2004).
The change of comovement can be measured by the changes in the Cointegration vectors
between markets over a long period of time. However, such approach does not specifically test
for contagion, as the increased cross-market relationship over such long time can be attributed to
number of reasons, such as greater trade integration or higher capital mobility (Forbes &
Rigobon 2002; Lee 2012). Thus, this approach could miss periods of contagion as cross market
relationships only increase briefly after a crisis.
The probability approach predicts the probability that a country will be affected by a
crisis when the crisis has occurred in another country. In Eichengreen, Rose, & Wyplosz (1996),
the study applied Probit model to test the probability of currency crisis contagion, and found that
the currency crisis occurred in elsewhere will significantly increase the probability of the
currency crisis domestically. Although such approach is relative easy to perform quantitative
7
analysis on the existence of contagion, if heteroscedasticity is not addressed, the estimate will be
biased (Xu & Liu 2010).
Empirical researches on contagion effect in Gulf Cooperation Countries have gained
attention recently. In (Moosa 2010), the study applied Structural Time Series Model of Harvey
(1989, 1997) to examine the stock market contagion from the U.S. to the GCC countries during
the period 2007-2008. The paper has shown limited evidence for the contagion effects of U.S.
stock market shocks on GCC stock prices. In addition, Suliman (2011) applied GARCH method
to investigate the evidence of contagion in GCC economies from 1960 to 2002. He found that
Saudi Arabic first suffered from the contagion effect of 1987 US stock market crash and the
1997 Thai currency crisis, and then it was propagated to other GCC nations.
3. Data and methodology
Data
This study has chosen Saudi Tadawul All Share Index (KSA), Kuwait Stock Exchange Index
(KWT), Abu Dhabi Securities Exchange General Index (UAE), Qatar Exchange Index (QAT),
Bahrain Bourse All share index (BHN), and Oman Muscat Securities 30 Index (OMN) as the
proxies of six GCC stock markets. In addition, we have chosen SP 500 (US), FTSE 100 (UK),
Nikki 225 (JAP); as well as Dow Jones US Islamic Market Total Return Index (IUS), Dow Jones
Islamic Market UK Index (IUK), and MSCI Japan Islamic Index (IJP) to represent the
conventional and Islamic stock markets of world’s three largest stock exchanges in U.S., U.K.,
and Japan (Majid et al. 2007; Kassim 2010).
The data used in this study are two-day rolling average of daily closing indices
denominated in the local currency, for the period from July 7, 2004 to August 3, 2012. Since
8
stock markets in GCC countries and U.S., U.K., and Japan are located in different time-zones,
the analysis is expected to encounter the problem of non-synchronous trading. In order to
overcome such problem, this study has adopted the method of two-day rolling average advocated
by Forbes and Rigobon (2002) and Mun (2005). According to Forbes and Rigobon (2002) and
Mun (2005), two-day rolling average data obtained from the average of two conservative daily
series will help to avoid the problem of non-synchronous trading among the stock markets. As
for the unavailable data because of weekends, national holidays, or other reasons, stock indices
were assumed to stay as same as the adjacent indices prior to the non-trading date (Chiang et al.
2007; Karim et al. 2010). Besides, since the trading activities of GCC stock markets are believed
to be dominated by GCC citizens (Bley & Chen 2006), the stock indices included in this study
have chosen to be dominated in their respective local currencies (Hon et al. 2004). In addition,
both natural logarithm and the first differenced natural logarithm value (stock market return) of
each series are calculated for the convenience of latter use.
To examine the impact of Global Financial Crisis on GCC stock markets before and after
the financial turmoil, we have divided the whole sample into two sub-periods: a) Tranquil Period,
from July 7, 2004 to July 25, 2007; and b) Crisis Period, from July 26, 2007 to August 3, 2012.
Since it is believed that the US subprime crisis debuted on July 26, 2007 (Dungey et al. 2002;
Karim et al. 2010; Majid & Kassim 2009), we use this date to be the breaking point of the entire
sample.
9
Methodology
Correlation analysis:
Since correlation analysis has been widely used to investigate the existence of financial
contagion (Chiang et al. 2007), this study has started with simple pair-wise correlation between
stock returns. The conditional correlation coefficients are measured as following:
𝜎𝑥𝑦
ρ=𝜎
(1)
𝑥 𝜎𝑦
where 𝜎𝑥𝑦 is the covariance of stock returns in markets ‘x’ and ‘y’, 𝜎𝑥 and 𝜎𝑦 are the standard
error of stock returns in market ‘x’ and ‘y’, respectively.
Due to the presence of heteroscedasticity bias in returns, Forbes & Rigobon (2002)
proposed to adjust the bias in stock index returns by manipulating the equation (1) to obtain the
unconditional correlation coefficient:
𝜌
𝜌∗ =
(2)
√1+𝛿[1−𝜌2 ]
where
𝜎ℎ
δ = 𝜎𝑥𝑥
𝑙 −1
(3)
𝑥𝑥
It measures the relative changes of variance in stock market ‘x’ between crisis and tranquil
periods.
To calculate the adjusted correlation coefficients, the crisis period is often used as the high
volatility period while the tranquil period as the low volatility period. The following hypothesis
is then tested:
10
𝐻0 :𝜌𝑐 ≤ 𝜌𝑡
𝐻1 :𝜌𝑐 > 𝜌𝑡
𝜌𝑐 is the adjusted correlation coefficient during the crisis period, and 𝜌𝑡 is the adjusted
correlation coefficient during the tranquil period. Based on the definition of contagion in Forbes
& Rigobon (2002), contagion presents when there is significant increase of markets
comovements. In order to make statistical inference on the
significance of the increased
comovements, the standard Z-test is applied (Chiang et al. 2007). Fisher’s Z transformations
convert standard coefficients to normally distributed Z-scores. Thus, the initial hypothesis is then
transformed into:
𝐻0 : 𝜌𝑐 ≤ 𝜌𝑡 → 𝐻0 : 𝑍𝑐 ≤ 𝑍𝑡
𝐻1 : 𝜌𝑐 > 𝜌𝑡 → 𝐻1 : 𝑍𝑐 ≤ 𝑍𝑡
where
1
1+𝜌
𝑍𝑐 = 2 ln (1−𝜌𝑐)
𝑐
1
1+𝜌
𝑍𝑡 = 2 ln (1−𝜌𝑡)
𝑡
Z=
𝑍𝑐 −𝑍𝑡
1
1
√𝑛 −3+𝑛 −3
𝑐
𝑡
where, 𝑍𝑐 and 𝑍𝑡 are Fisher transformations of correlation coefficients during crisis and tranquil
periods, respectively；𝑛𝑡 = 1114 and 𝑛𝑐 = 1836 are the number of observations during crisis
and tranquil periods, respectively. The critical value of right-tailed Z-test at 1%, 5%, and 10%
are 2.33, 1.65, and 1.28, respectively.
11
Unit root tests:
In order to avoid the problem of spurious results with regressions of non-stationary time series
variables, it is necessary to determine the order of integration for each variable in the model.
Thus, Unit root tests should be applied to establish whether the variables are non-stationary, and
how many times the variables needed to be differenced so that the series will be stationary. As
for the order of integration, a variable is said to be integrated of order d or I (d) if it is stationary
after differencing d times. In this study, both Augmented Dickey-Fuller (ADF) and PhillipsPerron (PP) tests are applied to investigate the non-stationarity of the series used. The two tests
differ from one another in terms of autocorrelation patterns used in the model. As for PP test, it
allows for more general autocorrelation patterns in comparison with ADF test.
Cointegration tests:
The next step of analysis is to investigate the existence of long-run equilibrium relationships
between variables. If there is a long-run equilibrium relationship between studied variables,
although the variables are individually non-stationary, they cannot drift arbitrarily far away from
each other. For a system of variables sharing the long-run equilibrium relationship, they are said
to be cointegrated. The requirements for cointegration between these variables are that they have
to be integrated of the same order and stationary in their linear combination.
Besides giving information on the long-run equilibrium relationships among variables,
the cointegration tests also provide guidance on the proper specification of the VAR models
(Ibrahim 2003). Specifically, if the variables are found to be non-stationary and not cointegrated
the first differenced VAR model should be used, while if non-stationary variables are found to be
cointegrated a Vector Error Correlation Model (VECM) should be used ( Engle & Granger 1987).
12
However, since past researches are still unclear on whether VECM outperforms the VAR at all
forecasting horizons (Ibrahim 2003), for our study we employ VAR model using variables in
levels even with the findings of cointegration among variables.
In order to test for cointegration, the Vector Autocorrelation (VAR) based JohansenJuselius (JJ) Cointegration approach is applied. By implementing JJ Cointegration technique on
VAR model, it is believed that such combination is a realistic representation of market linkages
(Dekker et al. 2001). The advantage of VAR model is that it needs not to distinguish between
exogenous and endogenous variables in the model. Thus, employing such model will be less
complicated (Gujarati 2003). Given that VAR approach models every endogenous variable in the
system as the function of the lagged values of all of the endogenous variables in the system
(Khalid & Kawai 2003); the VAR model can be specified as following:
𝑌𝑡 = δ + Π𝑖 𝑌𝑡−1 + ⋯ + Π𝑘 ∆𝑌𝑡−𝑘 + 𝜀𝑡
(4)
where 𝑌𝑡 is an n×1 vector of non-stationary variables integrated of order d, δ is an n×1 vector of
constant terms, Π𝑖 and Π𝑘 are the n×n matrices of coefficients, Δ denotes the first difference, k is
the lag length, and 𝜀𝑡 is an n×1 vector of white noise error terms. Since the appropriate lag
structure is important for the VAR model, this study has chosen the lag structure based on the
criteria that the residuals from each VAR model are white noise process. To examine the
existence of white noise process in the residuals of VAR model, the Ljung-Box Q-statistics has
been applied.
In order to determine the number of cointegration vectors, Johansen (1988) and Johansen
and Juselius (1990) developed two test statistics, which are the Trace and the Maximal
Eigenvalue statistics:
13
𝜆𝑡𝑟𝑎𝑐𝑒 = −𝑇 ∑𝑘𝑖=𝑟+1 ln(1 − 𝜆𝑖 )
(5)
𝜆𝑚𝑎𝑥 = −𝑇 ln(i − λr+1 )
(6)
The Trace statistics tests the null hypothesis that there are at most r cointegration vectors.
On the other hand, the Maximal Eigenvalue test is based on the hypothesis that the number of
cointegration vectors is r against the alternative hypothesis that it is r+1. In addition, since Trace
statistics are more robust than the Max-Eigenvalue statistics, if Trace and Max-Eigenvalue
statistics give contradictory results, the Trace statistics shall prevail (Johansen & Juselius 1990;
Cheung & Lai 1993).
VDCs:
Lastly, the Variance Decompositions (VDCs) analysis based on the VAR model in levels will be
used for the cointegrated stock markets. In particular, the VDC analysis investigates how much
of the variations in a certain market return can be explained by innovations from other markets in
the system. Besides, it also provides us insights on the relative importance of other markets in
driving the variations of particular stock market (Ravichandran & Maloain 2010). The variance
decomposition analysis in this study provides decompositions of 2-day, 5-day, and 20-day
horizons, for both tranquil and crisis periods, respectively.
4. Empirical results
Correlation analysis:
Table 1 presents the results of both unadjusted (conditional) and adjusted correlation
(unconditional) coefficient analysis for GCC stock market indices in respect of the current
Global Financial Crisis. The cross-market correlations of stock market index returns (1st
differenced natural logarithmic indices) are compared between tranquil period and crisis period.
14
Table 1: GCC stock markets contagion to the current Global Financial Crisis--unadjusted
and adjusted correlation coefficient tests
Tranquil Period
Crisis Period
Contagion
unadjusted ρ
σ
unadjusted ρ
σ
KSA
0.0021
1.2907
0.2295
0.9730
5.9808
***
Y
KWT
0.0022
0.5688
0.0762
0.5051
1.9463
**
Y
UAE
-0.0370
0.9324
0.1580
0.7940
5.1287
***
Y
QAT
-0.0361
1.0411
0.1640
1.0264
5.2628
***
Y
BHN
0.0416
0.5074
0.0799
0.8635
1.0073
N
OMN
0.0307
adjusted ρ
0.4066
σ
0.0162
adjusted ρ
0.3947
σ
-0.3814
Z-score
N
KSA
0.0028
1.2907
0.3016
0.9730
7.8574
***
Y
KWT
0.0025
0.5688
0.0867
0.5051
2.2138
**
Y
UAE
-0.0436
0.9324
0.1854
0.7940
6.0228
***
Y
QAT
-0.0370
1.0411
0.1681
1.0264
5.3962
***
Y
BHN
0.0246
0.5074
0.0473
0.8635
0.5967
Y/N
Z-score
N
N
OMN
0.0307
0.4066
0.0162
0.3947
-0.3819
Note: a) This table shows the Pearson cross-market correlation coefficients (ρ) and variance (σ) of the
correlation between the U.S. and 6 GCC countries. b) Z-score are Fisher z-transformed values testing for
the equality of tranquil period cross-market correlations with the corresponding crisis period correlations.
The null hypothesis is no increase in correlation (or no contagion). c) "Y" and "N" indicates the presence
and absence of contagion effects, respectively, d) Unadjusted correlation coefficients are conditional
correlation coefficients, using Eq. (1). e) Adjusted correlation coefficients are adjusted for changes in
variance, using Eq. (2). e) The 1%, 5%, and 10% right-tail critical Z values are 2.33, 1.65, and 1.28,
respectively. ***, **, and * indicates statistical significance at the 1%, 5%, and 10% levels, respectively.
As table 1 reveals, the cross-market correlations between U.S. and six GCC countries
during the crisis period are larger than those during the tranquil periods for both unadjusted and
adjusted correlation coefficients. The exceptions have found to be Bahrain (BHN) and Oman
(OMN) in both scenarios. When unadjusted correlation analysis is applied, contagion effects can
be observed from Saudi Arabia (KSA), Kuwait (KWT), Abu Dhabi (UAE), and Qatar (QAT). In
addition, similar findings can be evidenced from adjusted correlation analysis. Since adjusted
correlation analysis has taken into account the heteroscedasticity bias, it provides more reliable
results in comparison with unadjusted correlation analysis. Thus, it is safe to conclude that
among six GCC stock markets only Saudi Arabia, Kuwait, Abu Dhabi, and Qatar have suffered
15
contagion effects from the current Global Financial Crisis. However, our findings are found to be
contradicted with Moosa (2010), which has rejected the presence of stock market contagion from
the U.S. to the GCC markets in wake of 2007 U.S. Subprime Crisis. Two factors can be
attributed for such differences. First, different methodologies have been used in testing the
contagion hypothesis. Opposing to our correlation analysis, structural time series model has been
applied in Moosa (2010). Second, different time intervals have been applied in the study. Moosa
(2010) used a turmoil period from January 2, 2007 to December 31, 2010. As for this study, it
covers the time intervals between July 7, 2004 and August 3, 2012, which sets July 26, 2007 as
the breaking point for the crisis period. Due to these two differences, it is expected that our
investigation on the contagion effects will be different from the study of Moosa (2010).
Unit root tests:
Table 2: Unit Root Tests
Tranquil Period
KSA
KWT
UAE
QAT
BHN
OMN
US
UK
JAP
IUS
IUK
Level
(Natural Log)
ADF
PP
-1.5679 -1.3901
-1.6369 -1.4729
-1.6557 -1.8672
-1.7479 -1.9144
-1.7249 -1.5147
-2.6827 -2.1503
-2.6625 -2.9830
-3.5239** -3.4430**
First Difference
(Log difference)
ADF
PP
-6.6698*** -19.4903***
-6.7303*** -18.1323***
-7.8766*** -18.5103***
-8.3517*** -16.2133***
-8.9127*** -17.5444***
-6.9397*** -16.9439***
-9.6120*** -17.3563***
-12.0111***
Crisis Period
Level
(Natural Log)
ADF
PP
-1.5127 -1.5879
-1.9895 -1.2376
-1.4960 -1.5673
-1.8407 -1.6914
-1.8066 -1.7828
-1.5769 -1.0754
-1.7698 -1.8446
First Difference
(Log difference)
ADF
PP
-12.4450*** -21.2454***
-21.832*** -21.8411***
-13.2334*** -19.1765***
-10.0039*** -21.0635***
-7.9506*** -20.7549***
-6.9619*** -21.7009***
-11.6484*** -24.9369***
-18.3922*** -2.1713 -2.1765 -10.2573*** -22.5428***
-2.1119
-2.3983
-2.1132 -8.7842*** -18.7243*** -2.5651 -2.5004 -9.6963*** -23.0495***
-2.7863 -9.6178*** -17.2764*** -1.8978 -1.9081 -10.0528*** -25.2004***
-3.8135** -3.7759** -10.4382*** -17.7488*** -1.7420 -1.9974 -13.4841***
22.3476***
IJAP
-2.6953 -2.8750 -9.6408*** -18.1612*** -2.1014 -2.1468 -10.4694*** -25.7605***
Note: *,**, *** denote significance at the 10%, 5%, and 1% level, respectively. The above
tests of ADF and PP are based on model with constant and trend.
16
As for the stationary nature of the stock market indexes (natural logarithmic indexes), and
indexes returns (1st difference of natural logarithmic indices), Table 2 illustrates the unit root test
results for both ADF and PP methods during the two sub-periods. For the two sub-periods, both
ADF and PP statistics indicate that at level all the series are non-stationary, except for UK’s precrisis Conventional and Islamic indices. In general, this implies that the null hypothesis of the
presence of unit root cannot be rejected at all levels of significance before and during the Global
Financial Crisis, when market indices are at level. On the other hand, when the series are at first
difference, the null hypothesis of the presence of unit root can be rejected at all levels of
significance. In other words, all of these stock market returns are said to be integrated at order
one, or I (1).
Cointegration tests:
Having noted that all stock market indices are integrated at order one, or I (1), next we applies
VAR-based JJ Cointegration analysis to investigate the evidence of long-run equilibrium
relationships among markets. The cointegration tests have been employed on two models, for
both tranquil period and crisis period. For the first model, six GCC stock markets together with
world’s three largest conventional stock markets (i.e. U.S., U.K., and Japan) are included. As for
the second model, six GCC stock markets together with world’s three largest Islamic stock
markets (i.e. U.S. Islamic, U.K. Islamic, and Japan Islamic) are included. The appropriate lag
lengths for both models are determined on the criteria that the residuals from each VAR model
are white noise process. In particular, based on the Ljung-Box Q-statistics, the inclusion of 24
lags for the two models during the tranquil period are sufficient to render all the residuals of the
VAR models to be white noise. As for the crisis period, the appropriate lag length for the model
17
with conventional stock markets is 25, whereas it is 23 lags for the model with Islamic stock
markets.
Subsequently, Trace and Max-Eigenvalue statistics are applied to determine the number
of the cointegration vectors at 5% significance level. In addition, since Trace statistics are more
robust than the Max-Eigenvalue statistics, if Trace and Max-Eigenvalue statistics give
contradictory results, the Trace statistics shall prevail (Johansen & Juselius 1990; Cheung & Lai
1993).
The results of the cointegration tests are presented in Table 3. When GCC stock markets
and conventional stock markets of U.S., U.K. and Japan are included in the model, before the
crisis Trace statistics indicates that there are three cointegration relationships among them. In
contrast, Max-Eigenvalue statistics tend to reject the existence of long-run equilibrium
relationship among these markets. As for the crisis period, both Trace and Max-Eigen statistics
confirm the existence of one cointegration relationship among these markets. Since Trace
statistics prevail over Max-Eigen statistics in case of conflicting results, it is safe to conclude that
there are long-run equilibrium relationships between GCC stock markets and conventional stock
markets of US, UK, and Japan, for both of the sub-periods. This result suggests that GCC stock
markets and selected three world’s largest conventional stock markets share long-run stable
relationship regardless the occurrence of Global Financial Crisis. Thus, the opportunity of
benefiting from long-term international portfolio diversification into GCC stock markets has
diminished regardless the occurrence of the Global Financial Crisis.
As for the second model, prior to the Global Financial Crisis both Trace and MaxEigenvalue statistics have evidenced the existence of cointegration relationship between GCC
18
stock markets and Islamic stock markets of U.S., U.K., and Japan. However, for the crisis period,
only Trace statistics confirmed the presence of cointegration relationship among these markets.
Against, since Trace statistics prevail over Max-Eigen statistics in case of conflicting results, it is
rational to conclude that there is consistent cointegration relationship between GCC stock
markets and the Islamic stock markets of U.S., U.K., and Japan, regardless the occurrence Global
Financial Crisis. As implication, in the long-run, there are only marginal benefits for
international investors holding stocks in the Islamic stock markets of U.S., U.K., and Japan to
diversifying their portfolios into GCC stock markets.
Table 3. Cointegration Tests
Model
KSA, KWT , UAE,
QAT , BHN, OMN,
US, UK, JAP
KSA, KWT , UAE,
QAT , BHN, OMN,
IUS, IUK, IJAP
Tranquil Period
Crisis Period
MaxMaxH0
Trace
Trace Max 5%
Trace
Trace 5% Max 5%
Eigen.
Eigen.
S tatistic
5% CV
CV
S tatistic
CV
CV
S tatistic
S tatistic
r = 0 220.0237** 51.6528 197.3709 58.43354 209.8721** 60.0213** 197.3709 58.43354
r ≤ 1 168.3709**
38.0509
159.5297 52.36261 149.8507
42.88828
159.5297 52.36261
r ≤ 2 130.3200**
35.1866
125.6154 46.23142 106.9625
25.61891
125.6154 46.23142
r≤3
26.4947
95.75366 40.07757 81.34355
24.79998
95.75366 40.07757
r = 0 221.5914** 61.2804** 197.3709 58.43354 207.8249** 54.06407
95.13341
197.3709 58.43354
r ≤ 1 160.3110**
39.9273
159.5297 52.36261 153.7608
37.64956
159.5297 52.36261
r≤2
120.3837
37.5624
125.6154 46.23142 116.1112
35.53098
125.6154 46.23142
r≤3
82.82134
23.7097
95.75366 40.07757
27.22911
95.75366 40.07757
80.5803
Note: a) the variables included in the two models are the natural logarithm values of the stock market indices, b) **
denotes statistical significance at the 5% level.
VDCs:
In the previous section, the cointegration analysis has indicated the existence of long-run
relationships among studied stock markets. In order to gauge the relative strength of market
shock in one market on other markets in the system, VAR-based VDCs analysis has been
employed. The VDCs analysis also helps to infer on the dynamic interactions between stock
markets in the system. In particular, the results of the VDCs analysis document the extent to
19
which the variance of the stock market indices in one GCC member country can be explained by
the variance of other stock markets in the system.
Table 4 and Table 5 in Appendix 1 provides information on the VDCs for the two
previously mentioned VAR models in both tranquil and crisis periods. In addition, the reports of
the results for VDCs are at 2-day, 10-day, and 20-day horizons. As for VDCs analysis in Table 4,
it follows the Cholesky ordering of Saudi Arabia, Kuwait, U.A.E., Qatar, Bahrain, Oman, U.S.,
U.K., and Japan. On the other hand, the VDCs analysis in Table 5 follows the Cholesky ordering
of Saudi Arabia, Kuwait, U.A.E., Qatar, Bahrain, Oman, U.S. Islamic, U.K Islamic, and Japan
Islamic.
As indicated by Table 4, four interesting observations can be found by comparing the
after-crisis VDCs results with the pre-crisis ones. First, after the debut of the Global Financial
Crisis there is deceased importance of own market shocks in all GCC markets. This may indicate
the increased vulnerability of GCC market to external shocks from other markets either within or
outside the region. In particular, the stock markets of Qatar, Bahrain, and Oman have severely
subject to the external shocks after the debut of the Global Financial Crisis. Statistically, the selfexplanatory powers of their stock markets variations have dropped extensively from over 90
percent prior to the crisis to nearly 50 percent after the crisis, within the time horizon of 20 days.
Similar findings can be evidenced from (Ravichandran & Maloain 2010). Similarly, as can be
observed from Table 5, when Islamic stock markets are included in the model GCC stock
markets still tend to depend more on the variations of other stock markets both within and
outside of the region, after the debut of the financial crisis.
20
As for the second observation, the conventional U.S. stock market plays increased role in
explaining the variations of GCC stock markets after the debut of the crisis. Specifically, the
explanation power of conventional stock markets have increased handsomely from less one 1
percent prior to the crisis to nearly 20 percent after the crisis, within the time interval of 20 days.
Similar pattern of observation can be found from Table 5, where the extent of U.S. Islamic stock
market in explaining the variations of the GCC stock market have increased significant after the
debut of the financial crisis. In particular, the explanation power of U.S. Islamic stock market
have surged from an average of less than 1 percent prior to the crisis to an average of 10 percent
after the crisis.
In terms of the third interesting observation, the Table 4 reveals that the regional stock
markets play more important role than international conventional stock markets in explaining the
variations of the GCC stock markets, regardless the occurrence of the Global Financial Crisis.
The results shown from Table 5 are found to be echoed with the findings of Table 4, where the
regional stock markets also play more significant role than international Islamic stock markets in
driving the movements of GCC stock markets.
Lastly, regionally Saudi Arabia stock market has consistently acted as the major driver
of the stock markets variations with in the region, whereas internationally U.S. conventional
stock market plays a relatively more significant role than the other two leading conventional
stock markets in variations of the GCC stock markets. In particular, the conventional stock
markets of U.K., and Japan only have the average explanation power of less than 1 percent
across the two studying periods, whereas the explanation power of U.S. stock markets have
increased significantly form less 1 percent to nearly 10 percent after the debut of the crisis.
Similarly, the U.S. Islamic stock market is also found to be more significant than the other two
21
international Islamic stock markets in influencing the variations of GCC stock markets, for both
studying periods.
5. Conclusion
This article aims to achieve two major objectives: first, to investigate whether contagion effects
of current Global Financial Crisis presents in Gulf Cooperation Council (GCC) stock markets;
second, it is to evaluate the impact of the financial crisis on the long-run and short-run dynamic
relationships between GCC stock markets and international leading conventional and Islamic
stock markets around the globe.
Noted that we have defined contagion as the circumstance where the cross-market
movement increases significantly after the debut of financial crisis (Forbes & Rigobon 2002), the
investigation on the presence of contagion effects has been conducted based on the correlation
analysis with the adjustment of heteroscedasticity bias. As the correlation analysis indicates,
contagion effects of Global Financial Crisis can be only evidenced in Saudi Arabia, Kuwait, Abu
Dhabi, and Qatar stock markets.
As for the effects of the Global Financial Crisis on the long-run relationships between
studied stock markets, the VAR based JJ Cointegration tests reveal that there are stable long-run
equilibrium relationships between GCC stock markets and the conventional as well as Islamic
stock markets in U.S., U.K., and Japan, regardless the occurrence of the financial crisis. In other
words, the debut of Global Financial Crisis does not have significant influence on the long-run
relationship between GCC stock markets and the leading international stock markets around the
globe. As implication, there are diminished diversification benefits for international investors to
diversifying into GCC stock markets, both prior to and after the debut of financial crisis.
22
In addition, the VDCs analysis indicates four interesting observations pertaining to the
impact of Global Financial Crisis on the short-run relationships between GCC stock markets and
international stock markets. First, after the debut of the financial crisis, GCC stock markets have
experienced increased vulnerability to external shocks from both with and outside of the region.
Second, both conventional and Islamic stock markets of U.S. play increased role in explaining
the variations of GCC stock markets after the debut of the financial crisis. Third, GCC stock
markets seem to be more influenced by regional markets than international markets, regardless
the occurrence of the Global Financial Crisis. Last but not least, Saudi Arabia has consistently
acted as the major regional force for the stock markets variations within the region, whereas both
conventional and Islamic stock markets of U.S. have constantly acted as the major international
forces for the movements of the regional stock markets.
In sum, GCC stock markets have been consistently integrated with global stock markets,
both conventional and Islamic ones. In addition, GCC stock markets become more vulnerable to
the external shocks. In order to mitigate the negative influence of the external shock, it is
recommended for GCC countries to move towards more integrated regional markets. In addition,
GCC countries are advised to increase their diversification in production and trade. With a larger
and diversified economy, it is believed that GCC stock markets will be less vulnerable to the
external shocks (Suliman 2011).
23
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25
Appendix 1:
Table 4: Variance Decomposition Analysis
Cholesky Ordering: KSA KWT UAE QAT BHN OMN US UK JAP
Variance
Explained by innovations in:
Days
of
KSA
KWT
UAE
QAT OMN BHN
US
UK
Tranquil Period
KSA
2
99.7571 0.0000 0.0366 0.0840 0.0628 0.0136 0.0011 0.0013
5
97.6870 0.0670 0.0762 0.6060 1.1207 0.3294 0.0084 0.0271
20 93.3059 0.7058 0.2811 0.4065 3.9416 0.8310 0.0147 0.1498
KWT
2
3.3737 96.4014 0.0001 0.0031 0.0284 0.0571 0.0132 0.1091
5
9.7568 88.2016 0.6170 0.2967 0.0665 0.1840 0.0460 0.7551
20 25.9259 65.2194 4.2231 0.2886 0.1353 3.0292 0.3854 0.4231
UAE
2
3.7532 0.8545 95.2716 0.0037 0.0157 0.0940 0.0001 0.0031
5
9.3936 0.2913 89.0296 0.0798 0.5047 0.4850 0.0218 0.1165
20 13.5675 0.5475 77.3745 2.9301 1.3818 2.9927 0.2820 0.2162
QAT
2
1.3740 0.5832 1.5845 96.3843 0.0015 0.0053 0.0242 0.0274
5
4.1088 0.6851 1.4202 93.3454 0.0906 0.0044 0.2176 0.0627
20
8.6697 3.3931 0.9912 80.5101 0.1009 1.8933 0.3793 2.5094
BHN
2
0.5862 1.8593 0.0486 0.6471 96.7184 0.0652 0.0000 0.0000
5
3.1430 3.1749 0.0405 0.5299 91.6648 0.4860 0.1605 0.1312
20 11.6965 2.4377 1.6936 1.7369 72.7337 3.1943 2.5518 2.3699
OMN
2
0.1297 0.3976 0.3394 0.3558 0.1306 98.6251 0.0015 0.0191
5
0.8606 1.1256 1.5820 0.1409 0.6941 94.8446 0.1204 0.4580
20
8.7155 1.1090 13.6131 0.0535 1.1742 74.2767 0.1364 0.3276
Crisis period
KSA
2
99.1825 0.0953 0.0351 0.0014 0.0301 0.0097 0.6017 0.0001
5
91.8816 0.5405 0.2746 0.0406 0.3664 0.2425 5.8884 0.5075
20 71.5129 2.1237 0.2162 0.8803 0.3598 0.6663 20.3600 1.1788
KWT
2
4.9610 94.8266 0.0039 0.0259 0.0012 0.0100 0.0428 0.0040
5
8.8640 89.4631 0.0122 0.2109 0.1124 0.0392 0.6787 0.1436
20
5.8748 84.7977 0.2424 0.1988 0.1352 1.5398 6.7282 0.0839
UAE
2
10.7485 6.5601 81.8357 0.0124 0.0099 0.0035 0.6070 0.0058
5
16.0192 9.2680 69.4431 0.0300 0.0513 0.1299 4.1996 0.1281
20 14.0970 12.9967 59.2752 0.3492 0.5182 0.4618 11.1580 0.3592
QAT
2
10.8004 9.1385 11.5318 67.3838 0.0121 0.0293 0.6979 0.0058
5
20.4319 11.9205 8.1415 53.6892 0.0206 0.0538 4.7038 0.0366
20 15.4023 20.9310 3.2284 44.0335 0.2142 0.3983 13.4326 0.2801
BHN
2
2.5510 12.0271 2.2152 0.2006 82.9200 0.0000 0.0167 0.0043
5
9.9001 13.2625 2.6255 0.6130 72.5904 0.0117 0.1977 0.1648
20 12.0532 21.3266 5.7595 0.4715 50.5874 0.0986 8.0332 0.3678
OMN
2
8.2803 4.1118 11.9883 5.1530 1.1806 68.5862 0.3753 0.0009
5
14.6399 6.5347 14.1842 4.8021 1.2455 55.2299 2.6098 0.1569
20 12.9717 10.1200 12.1052 5.2561 1.4420 43.4624 12.4707 0.4046
26
JAP
0.0436
0.0781
0.3636
0.0139
0.0764
0.3702
0.0041
0.0777
0.7077
0.0155
0.0651
1.5531
0.0751
0.6693
1.5856
0.0011
0.1738
0.5941
0.0440
0.2578
2.7021
0.1247
0.4759
0.3992
0.2171
0.7309
0.7846
0.4004
1.0022
2.0796
0.0649
0.6344
1.3021
0.3237
0.5971
1.7673
Table 5: Variance Decomposition Analysis
Cholesky Ordering: KSA KWT UAE QAT BHN OMN IUS IUK IJAP
Variance
Explained by innovations in:
Day
of
KSA KWT
UAE
QAT BHN OMN IUS
IUK
Tranquil Period
KSA
2
99.7324 0.0003 0.0249 0.0577 0.0521 0.0121 0.0002 0.0149
5
97.3044 0.0490 0.0936 0.2861 1.3751 0.3274 0.0457 0.0585
20 88.9777 0.3861 0.3234 0.1519 5.6566 0.9167 0.3925 3.0309
KWT
2
3.2576 96.4298 0.0000 0.0047 0.0119 0.0555 0.0124 0.1969
5
9.3322 88.8851 0.5650 0.2744 0.0213 0.1285 0.0287 0.6861
20 23.5862 69.3685 3.5142 0.4862 0.1640 2.0712 0.4551 0.2226
UAE
2
4.1198 0.8446 94.9275 0.0000 0.0106 0.0914 0.0001 0.0016
5
9.6004 0.2831 88.7029 0.1414 0.6072 0.5756 0.0102 0.0738
20 11.9022 0.5780 76.4962 3.2357 2.4931 4.4130 0.3708 0.0867
QAT
2
2.0325 0.5458 1.5210 95.8719 0.0010 0.0061 0.0088 0.0037
5
5.8031 0.5637 1.0627 91.7975 0.0424 0.0041 0.0514 0.6513
20 10.4758 2.4903 0.6550 78.9637 0.0933 1.8094 0.1708 5.2960
BHN
2
0.6559 1.5324 0.0689 0.6208 96.9344 0.0565 0.0005 0.0002
5
3.4410 2.3495 0.0913 0.5101 92.2275 0.3553 0.0824 0.1018
20 11.2175 1.9392 0.4271 2.2741 76.8332 3.6266 2.5159 0.1233
OMN
2
0.1949 0.3302 0.5474 0.4197 0.1237 98.3742 0.0023 0.0031
5
1.1320 0.7444 1.9137 0.1581 0.6526 94.9685 0.0948 0.1261
20
7.5662 0.9243 13.6914 0.2304 1.0598 75.6771 0.2288 0.2911
Crisis period
KSA
2
99.1762 0.1018 0.0166 0.0029 0.0200 0.0204 0.5771 0.0009
5
92.9361 0.4834 0.2290 0.0485 0.4740 0.0600 5.3106 0.1183
20 72.9831 1.5650 0.2821 1.5047 0.4986 0.1561 20.3728 1.5212
KWT
2
4.7978 94.9181 0.0042 0.0107 0.0027 0.0239 0.0335 0.0001
5
8.6967 89.8043 0.0235 0.1355 0.2008 0.0263 0.3790 0.0364
20
5.7050 85.5736 0.5455 0.1163 0.2203 1.5959 4.7637 0.9037
UAE
2
11.2472 6.3326 81.5177 0.0085 0.0096 0.0000 0.6274 0.0121
5
17.1172 8.8846 69.5224 0.0125 0.0727 0.0099 3.1943 0.0416
20 16.3935 11.0969 59.6386 0.2868 0.1866 0.1378 10.5307 0.1204
QAT
2
11.4164 9.2873 11.8646 66.3197 0.0138 0.0400 0.6769 0.0894
5
22.1978 11.6416 8.5614 52.5135 0.0221 0.0862 3.6897 0.5957
20 17.7889 19.4956 3.5432 44.9480 0.1919 0.1798 12.4198 1.0186
BHN
2
2.7939 11.9867 2.1198 0.2721 82.7020 0.0000 0.0183 0.0000
5
11.5559 12.9386 2.3928 0.8303 71.1477 0.0049 0.1146 0.0247
20 15.7796 20.4975 3.9970 0.8134 49.3652 0.2203 6.6274 0.0366
OMN
2
9.0205 3.7445 12.3598 5.4258 1.2094 67.6364 0.2962 0.0186
5
15.9070 6.0842 15.0916 5.3099 1.0789 53.2335 2.2443 0.2046
20 14.2899 8.7138 11.1883 6.1072 0.7580 43.4035 12.6690 0.3938
27
IJAP
0.1054
0.4602
0.1642
0.0312
0.0787
0.1320
0.0044
0.0056
0.4241
0.0092
0.0238
0.0455
0.1304
0.8411
1.0431
0.0046
0.2098
0.3309
0.0840
0.3402
1.1165
0.2091
0.6974
0.5760
0.2448
1.1448
1.6087
0.2919
0.6920
0.4143
0.1072
0.9905
2.6629
0.2887
0.8460
2.4766