Does MENA Region Equity Markets is Progressing towards Higher Regional Connectedness?
Vipul Kumar Singh(Corresponding Author)
National Institute of Industrial Engineering (NITIE)Vihar Lake, P.O. NITIE, Mumbai - 400087, India
Email: [email protected] (Office): +91-22-28035200, Extn: 5547
Mobile: +91-9665840592
Shreyank NishantIndian Institute of Technology
Guwahati -781039, IndiaEmail: [email protected]
Pawan KumarDeGroote School of Business, McMaster University
1280 Main Street West, Hamilton, Ontario L8S4L8, CanadaEmail: [email protected]
Running Title: MENA Region Equity Connectedness
Does MENA Region Stock Markets is Progressing towards Higher
Regional Connectedness?
Abstract
The paper assesses the level of growth of financial integration of the Middle East and North
Africa (MENA) equity markets during the period 2004 to 2016. We utilize generalized error
variance decomposition and network study regarding the changing level of connectedness
across pre- and post-crisis centering global financial crisis and crude oil crisis 2008-09. The
study shows that the GCC nations are the primary growth drivers of MENA region acting as a
support mechanism to provide a cushion to the connected economy during times of distress.
We found very low total connectedness of 33.93 percent, highlighting the need for higher
regional integration and economic cooperation among the MENA economies.
Keywords: Economic Integration, Financial Econometrics, Financial Contagion, GCC,
Non-linear Time Series.
JEL Classification: C58, F15, F36, G15.
1. Introduction
Unlike Asia-Pacific, Europe, and Latin & North Americas, the economic, financial and
geopolitical dynamics of the Middle East and North Africa (MENA) region is highly
sensitive to its regional and domestic political stability, Shariah compliances, and crude oil
price volatility. The stock markets of the MENA region is typically much smaller,
fragmented, and illiquid as compared to the world financial markets (Lagoarde-Segot, and
Lucey, 2008). Some markets are also suffering from an inferior flow of information leading
to weak-form efficiency (Assaf, 2009). However, the recent crude oil crisis has forced the
many oil-dependent economies of the MENA region to transform their capital markets and
open the economy for foreign capital inflow. Whereas the studies focusing on the bivariate
intra- and inter-regional contagion/spillover connectedness between the equity markets of the
MENA region and with the conventional mature markets of the world is voluminous (Balcilar
et al., 2015; Ahmed, and Farooq, 2017), the literature on system-wide connectedness of
MENA region is insufficient (Maghyereh, Awartani, and Hilu, 2015; Shahzad et al., 2017).
Against this background, it is essential to monitor the system-wide volatility spillover
connectedness dynamics and assesses the progress of financial integration of this region.
Owing to the higher regionalization of the MENA equity markets an isolated spillover
examination of its selective equity markets does not seem appropriate for policy matters and
investment decisions. Predicting the system-wide connectedness can also help policymakers
undertake measures that can potentially make the equity markets of MENA region more
resilient to regional volatility overspills.
This research paper assesses the same in the context of its two significant
macroeconomic and financial conditions. First, its economic upheavals in the context of oil
economy of most of the MENA countries, especially the dominance of Gulf Cooperation
Council (GCC) nations1. Second, the economic framework of almost all the nations (except
Israel) of MENA region is based on Islamic finance Shariah principles, which disintegrates
the equity markets of this region with the rest of the world. Besides, as the volatility of
equity indices are particularly crisis-sensitive, we concentrated on connectedness before and
after the subprime and crude oil crises of 2008-09 as well. For the same, we have used the
empirical framework of generalized error variance decomposition (GEVD) and network
graphs. Due to non-availability of gauge of fear connectedness (popularly known as implied
volatility indices) for MENA economies, we have used a closed substitute, absolute volatility
for gauging the volatility spillover connectedness (Forsberg, and Ghysels, 2007).
In empirical finance, impulse response function (IRF) and forecast error variance
decomposition (FEVD) are the prominent tools in interpreting the shock spillover dynamics
of a multivariate financial system (Pesaran, Schuermann, and Weiner, 2004; Diebold and
Yilmaz, 2009; Lanne, and Nyberg, 2016). In the former, the assumption is that a shock occurs
only in one variable at a time. While in the latter, the connectedness arises not only through
the cross-variable dependence captured in VAR coefficients but also through the shock
dependence caught in the VAR disturbance covariance matrix. In such system, the reduced-
form shocks are rarely orthogonal, and results are found to be more sensitive to Cholesky
ordering which makes them unattractive for system-wide spillover analysis. Koop, Pesaran
and Potter (1996), and Pesaran, and Shin (1998), in short KPPS, brought the concept of
generalized error variance decomposition (GEVD) to mitigate the ordering issue of Cholesky
factor ordering. Recently, using KPSS, Diebold and Yilmaz (2012) derived a set of pairwise
and system-wide connectedness measures invariant of ordering built from pieces of rolling
variance decompositions. Eventually, they suggested the usefulness of network graphs in
depicting the system-wide connectedness of multivariate large-scale financial systems
1 The Gulf Cooperation Council (GCC) is a political and economic alliance of six Gulf States comprising the energy rich Gulf monarchies – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.
(Diebold and Yilmaz, 2014). The financial network studies are based on the concept of the
first and second model of Erdős and Rényi (1959), uses a threshold limit to explore the
connectedness of higher significance (Lyócsa et al., 2017). In recent times, the uses of
network map have increased vigorously to simplify the pairwise and system-wide
connectedness dynamics of large-scale systems (Tse et al., 2010). From the time Mantegna
(1999) introduced network graphs, limited progress has been made to explore the full
potential of network theory in empirical finance. The studies in this regard are mostly limited
to explaining bivariate connectedness only (Baumöhl et al., 2018; Feng et al., 2018; Shahzad
et al., 2018).
This study is expected to provide a comprehensive insight into the system-wide
connectedness of the MENA region for two prominent reasons. First, the inference in this
paper is based on a more accurate measure of error variance decomposition invariant of VAR
ordering (Lanne, and Nyberg, 2016). Second, the proposed GVD-Network framework is
more revealing regarding finding the time-varying direction of spillover paths, patterns,
clusters, and exposure to shocks which is highly helpful to identify the markets for asset
allocation and asset pricing for the portfolio managers.
The remainder of the paper is organized as follows. Section 2 provides an overview of
the existing literature on the local connectedness of the stock markets of the MENA region to
delve deeper into the region-centric integration and interdependencies. Section 3 describes
the data and descriptive statistics of the benchmark equity indices of the MENA equity
markets, segregated as GCC members (Bahrain, Kuwait, Saudi Arabia, United Arab
Emirates, Qatar, and Oman), and Non-GCC nations (Egypt, Israel, Jordan, Lebanon, and
Morocco). Section 4 provides details on the connectedness methodology employed which
includes a framework of static and rolling GVD connectedness followed by network graphs.
Section 5 discusses the dynamics of pairwise and system-wide spillover transmission for the
full sample and change in connectedness from pre to post-crisis window centering global
financial crisis & crude oil crisis 2008-09. Section 6 concludes that the GCC nations are the
significant drivers of growth of MENA region acting as a support mechanism to provide a
cushion to the connected economy during times of financial and economic distress.
2. Literature Review
There has been voluminous literature related to intra- and inter-regional integration and
interdependence of the MENA region, within, and with the developed and emerging markets
around the world. Several empirical methodologies have been adopted by researchers to
delve deeper into the MENA region-centric integration and interdependencies. The studies
mostly stress the underlying opportunity of portfolio diversification within the MENA region.
To study international transmission effects, Lee (2002) applies Haar function of the Discrete
Wavelet Transform (DWT) and regression to the stock markets of Japan, the United States,
Egypt, Germany, and Turkey. He concludes that the spillovers from developed markets of
Japan, the US and Germany impact the rising stock markets of Turkey and Egypt in the
MENA region, however not the other way around. In another research, Lagoarde-Segot and
Lucey (2007) reject the hypothesis of a stable, long-run bivariate relationship between
MENA markets and the EMU (European Monetary Union), the US, and a regional
benchmark by using cointegration methods which indicate the existence of significant
portfolio diversification opportunities for regional and global investors.
Some studies reveal that the shocks originating from the world’s stock markets impact
MENA countries heterogeneously and therefore, must not be threatened as a group. Jung-
Suk and Hassan (2008) find higher effects of own-volatility spillovers than cross-volatility
spillovers for all the MENA markets. However, there is evidence that both long-run
relationships and short-run causal linkages between the MENA and international markets
were substantially weakened after the 2008 global financial and crude oil crisis. Cheng et al.
(2010) use the static International Capital Asset Pricing Model (CAPM); the constant-
parameter intertemporal CAPM; and a Markov-switching intertemporal CAPM to study
connectedness within and outside the MENA region. They find that most of the stock markets
of the MENA region are highly segmented from global stock markets, except Turkey and
Israel which show high integration with them. The study suggests that financial market
integration decreases with an increase in oil price and vice-versa. On the contrary,
Maghyereh et al. (2015) find a weak relation with the US before the crisis and a significant
jump after that. They investigate the volatility co-movement between the US and a group of
large the Middle East and North African stock markets before and after the global financial
crisis in 2008. In an in-depth study, Balcilar et al. (2015) use alternative spillover models to
assess the impact of local, regional and global factors on the international portfolio
diversification benefits of investing in block-wide equity sectors of the oil-rich GCC
countries. They show that some GCC-wide equity sectors displaying segmentation from
global markets during periods of high and extreme market volatility can serve as safe havens
for international portfolio investors during such periods.
Darrat, Elkhal, and Hakim (2000) examine the intra-regional connectedness between
the emerging stock markets of Morocco, Egypt, and Jordan. Johansen-Juselius test employs
by them suggests segmentation of the Middle East emerging markets globally, but high
integration within the region. The Gonzalo- Granger test and error-correction models indicate
that the markets in Egypt profoundly influence other markets drew attention. Abraham and
Seyyed, (2006) use a bivariate EGARCH model to explore the cointegrating relationship
between the emerging Gulf markets of Saudi Arabia and Bahrain in conjunction with the US
market. In a recent study, using the implied correlation index of Skintzi, and Refenes (2005),
Demirer (2013) finds a strong correlation among most of the stock markets of Gulf region
except for Bahrain. For a better understanding of how the 2008 crisis affected the MENA
region, Neaime (2012) focus on the intra- and inter-regional causal patterns of the MENA
stock markets and the mature markets of the US, UK, and France. Recently, Neaime (2016)
reinvestigate the international and regional contagion vulnerability and financial linkages of
the MENA stock markets using Granger causality tests and impulse response functions. The
result verifies that except the markets of Egypt, Morocco, and Tunisia, the GCC equity
markets are relatively less vulnerable to global and regional financial crises, thus, still offers
international portfolio diversification potentials.
As the MENA region stock markets are highly sensitive to its regional and domestic
political stability, Shariah compliances, and volatility of crude oil prices, the studies explore
the volatility spillover in this context as well. Chau et al. (2014) examine the impact of Arab
Spring on the volatility of conventional and Islamic stock market indices. Ahmed and Farooq
(2017) analyze the degree of the extent to which Islamic Shariah compliances affect the
market volatility of MENA region vis-à-vis conventional markets. Shahzad et al. (2017) test
the decoupling hypothesis of the Islamic stock market with the three primary conventional
stock markets of the Americas, Europe, and Asia region (the US, the UK and Japan) from
July 1996 to June 2016. The study rejects the hypothesis, shows that the Islamic markets are
exposed to the same global risks, conventional markets are. The study concludes that the
restricted Islamic equity universe cannot be helpful in constituting a viable alternative for
hedge fund managers who wish to hedge their investments from the large-scale crisis likely to
turn into a global financial crisis. In a similar study, Rejeb (2017) also confirms that the
Islamic stock markets are not entirely immune to the global financial crisis. Unlike other
emerging markets, Islamic Emerging and Arab markets and Islamic developed markets are
too exposed to shocks originating from the traditional mature markets, thus, not provide a
cushion against economic and financial shocks that affect conventional and global markets.
Awartani and Maghyereh (2013) explore the impact of changing crude oil prices on the
volatility spillover of the equity markets of the MENA region. They all find significant
connectedness between crude oil price and stock market movements. In financial
econometrics, the studies using the confluence of multivariate econometric models and
network theory to explain the system-wide connectedness of MENA markets are rare. The
studies on other markets are limited too. Among the recent studies, Lyócsa et al. (2017) study
a sample of 40 stock market networks indices from five continents, Baumöhl et al. (2018)
analyze the network volatility spillovers among 40 developed, emerging and frontier stock
markets during 2006–2014, Shahzad et al. (2018) study the spillover structure of 58 nations
using bivariate cross-quantilogram approach,
3. Data & its Descriptive Statistics
This study uses the daily closing price of stock indices of 11 stock exchanges in the MENA
region comprising GCC nations, for the period ranging from July 5, 2004, to December 23,
2016, a total of 3250 observations. In addition to GCC members (Bahrain, Kuwait, Saudi
Arabia, United Arab Emirates, Qatar, and Oman), Egypt, Israel, Jordan, Lebanon, and
Morocco are included in the study. Due to non-availability of equity index data, Yemen,
Iraq, Iran, Algeria, and Libya are excluded from the research. Turkey and Tunisia are also
excluded from the list because of their close economic proximity to the European Union2.
The Bloomberg codes of the equity indices for each selected country are as follows with
country name in parentheses: KWSEIDX (Kuwait), SASEIDX (Saudi Arabia), MSM30
(Oman), DSM (Qatar), HERMES (Egypt), MOSENEW (Morocco), TA-25 (Israel),
2 The Turkey is linked to EU by a Customs Union agreement and Tunisia is linked by an association agreement with EU, both agreement came into force in 1995. Turkey has been a candidate country to join the European Union since 1999, and is a member of the Euro-Mediterranean partnership.
JOSMGNFF (Jordan), BHSEASI (Bahrain), DMFGI (UAE’s), and LEBANON (Lebanon).
Bloomberg financial database is the source of all the data. Data for some days is unavailable
due to the regional holidays and weekly offs, hence missing data for a date is considered as
same as that of previous day data. As the study involves countries from the same time zone,
the use of end-of-the-day closed price of the benchmark indices is not a problem for the
study. The maximum time lag between the openings of stock markets of the two countries,
Morocco and UAE, falling on the extreme time zones of the region is not more than 4 Hours.
As our approach to volatility connectedness is based on 100 days rolling decomposition
(cumulative effect) with ten days error variance decomposition predictive horizon, time-zone
is not a problem for the study.
Figure 1 depicts the daily stock market prices and returns for all 11 countries. The
return series of all indices show the clusters of low and high volatility. This indicates that the
periods of low volatility follow low and high follow high. We particularly can observe the
unsteady pattern in returns of MENA countries during the Israeli-Hezbollah war of 2006 and
2008 US financial crisis. As can be seen in Figure 2, the stock market process of all countries
follows an upward trend from 2004 to 2007, before the 2008 US Subprime financial crisis,
because of the oil boom. Other possible reasons are rapid and massive trade liberalization,
privatization schemes, considerable efforts in enhancing efficiency and integration of stock
markets in the MENA region. All these events cumulatively led to substantial growth in
market capitalization of the stock markets in the MENA region. Similar to all others, the
stock markets of MENA region also get impacted by the 2008 financial and oil crisis
profoundly, nosedives as evident from Figure 2. However, the rebound of oil prices helped
this region in a quick recovery. The European sovereign debt crisis of 2010 has impacted
little to this region. As the economy of the MENA region is highly sensitive to crude oil
prices, a sharp drop in oil prices since June 2014 has a devastating effect on all the stock
markets of the MENA region. Among them, the oil-rich nations of the GCC members are hit
worse. Incidentally, the shocks from drop-in oil prices affect the oil exporting countries such
as GCC members more in comparison to oil importing countries namely Egypt, Jordan, and
Morocco. In addition to crude oil prices, political and social unrest has jolted the economy of
MENA region from time to time.
Figure 1. Price and Return Graphs of Stock Indices of MENA Economies
500
1,000
1,500
2,000
2,500
3,000
3,500
-8
-6
-4
-2
0
2
4
04 05 06 07 08 09 10 11 12 13 14 15 16
Bahrain_r Bahrain
4,000
6,000
8,000
10,000
12,000
14,000
16,000
-8
-4
0
4
8
12
16
04 05 06 07 08 09 10 11 12 13 14 15 16
Kuwait_r Kuwait
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
-20
-15
-10
-5
0
5
10
15
04 05 06 07 08 09 10 11 12 13 14 15 16
Oman_r Oman
4,000
6,000
8,000
10,000
12,000
14,000
16,000
-15
-10
-5
0
5
10
15
04 05 06 07 08 09 10 11 12 13 14 15 16
Qatar_r Qatar
0
4,000
8,000
12,000
16,000
20,000
24,000
-30
-20
-10
0
10
20
30
04 05 06 07 08 09 10 11 12 13 14 15 16
Saudi Arabia_r Saudi Arabia
0
2,000
4,000
6,000
8,000
10,000
-20
-10
0
10
20
30
04 05 06 07 08 09 10 11 12 13 14 15 16
UAE_r UAE
0
200
400
600
800
1,000
1,200
-20
-15
-10
-5
0
5
10
04 05 06 07 08 09 10 11 12 13 14 15 16
Egypt_r Egypt
400
800
1,200
1,600
2,000
2,400
-12
-8
-4
0
4
8
04 05 06 07 08 09 10 11 12 13 14 15 16
Israel_r Israel
1,000
2,000
3,000
4,000
5,000
6,000
-10
-5
0
5
10
15
04 05 06 07 08 09 10 11 12 13 14 15 16
Jordan_r Jordan
400
800
1,200
1,600
2,000
2,400
-15
-10
-5
0
5
10
04 05 06 07 08 09 10 11 12 13 14 15 16
Lebanon_r Lebanon
0
4,000
8,000
12,000
16,000
20,000
24,000
-6
-4
-2
0
2
4
6
04 05 06 07 08 09 10 11 12 13 14 15 16
Morocco_r Morocco
Table 1 provides some descriptive statistics for stock market log returns for all the countries
under analysis. Table 1 shows that the most significant mean daily stock market return is for
Egypt at 6.4 percent, while it is smallest for Bahrain at -0.08 percent. The UAE shows the
highest standard deviation of stock market return (return volatility), while Bahrain shows the
lowest. Oman shows the highest negative skewness. On the other hand, Jarque-Bera statistic
significantly higher value indicates that the return does not follow a normal distribution. Test
statistics of LM ARCH, Ljung-Box (Q-Statistics on raw data) and Box-Pierce (Q-Statistics
on squared data) is indicating the presence of ARCH (conditional heteroscedasticity) and
strong autocorrelation in the squared return series of the equity indices. This justifies our
decision of using absolute volatility as a proxy of market volatility.
Table 2 reports the unconditional correlation statistics of MENA stock market log
returns. Although Table 2 shows that the stock market returns of the MENA region are
positively correlated with each other, the levels are weak to moderate only. While stock
market returns of Morocco and Lebanon have the lowest correlation of 0.05 percent, the
Oman and Qatar’s stock market return have the highest correlation of 43.1 percent. Morocco
shows the lowest return correlation with all stock markets, ranging from 5 percent to 10.8
percent, followed by Israel because of weak financial and trade ties with other MENA
countries. The correlational statistics is slightly better for GCC members. As the correlation
analysis is unconditional and static, it does not account for cross-market variance movements
which can have a massive impact on the system-wide spillover connectedness of the MENA
markets. Section 5 explores the same in detail.
Table 1: Descriptive Statistics of Stock Market Returns (Full Sample: July 2004–December 2016)
Bahrain Egypt Israel Jordan Kuwait Lebanon Morocco Oman Qatar Saudi Arabia UAE Mean -0.008 0.064 0.030 0.002 0.000 0.021 0.030 0.015 0.022 0.005 0.025 Median 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Maximum 3.908 9.233 7.275 7.953 8.433 8.490 4.464 10.250 11.089 16.590 13.142 Minimum -6.667 -17.188 -9.830 -7.899 -7.164 -10.688 -5.017 -16.395 -13.680 -14.323 -15.025 Std. Dev. 0.594 1.610 1.079 0.946 0.809 0.983 0.774 1.073 1.484 1.617 1.800 Skewness -0.996 -0.862 -0.750 -0.726 -0.962 -0.018 -0.255 -1.550 -0.572 -1.189 -0.525 Kurtosis 16.835 12.434 11.025 17.221 20.092 27.086 8.679 39.139 15.899 20.241 12.751
Jarque-Bera 26455.4 12455.4 9024.6 27671.5 40061.7 78557.9 4402.2 178158.7 22709.5 41020.8 13025.6
LM ARCH (Lag 10) 9.1428** 12.496** 62.608** 66.949** 54.142** 39.044** 52.137** 30.888** 51.412** 58.360** 51.309**Ljung-Box Q-(20)
38.1256** 42.8880** 15.0916 24.6114 44.2315** 37.1892* 76.8738** 19.5008** 25.3862 17.6482 31.8632*
Box-Pierce Q2-(50)
607.797** 471.981** 2941.76**
2967.26** 1705.21** 810.061** 1701.76** 1594.03**
2459.68** 2277.11** 1405.00**
Notes: a) LM ARCH Test H0: No ARCH effect.b) Robust Ljung-Box Q-Statistics on raw data H0: No serial correlation ==> Accept H0 when the probability is High [Q < Chisq(lag)]c) Box-Pierce Q-Statistics on Squared data H0: No serial correlation ==> Accept H0 when the probability is High [Q < Chisq(lag)]d) **: Reject the H0 hypothesis at 5 percent level of significance in all three casese) *: Reject the H0 hypothesis at 10 percent level of significance in all three casesf) No. of Observations: 3250
Table 2: Correlation Statistics of Stock Market Returns (Full Sample: July 2004–December 2016)
Bah
rain
Egyp
t
Isra
el
Jord
an
Kuw
ait
Leba
non
Mor
occo
Om
an
Qat
ar
Saud
i A
rabi
a
UA
E
Bahrain 1
Egypt 0.149 1
Israel 0.083 0.191 1
Jordan 0.213 0.239 0.076 1
Kuwait 0.343 0.224 0.115 0.284 1
Lebanon 0.058 0.122 0.072 0.124 0.108 1
Morocco 0.093 0.108 0.062 0.095 0.091 0.05 1
Oman 0.283 0.273 0.163 0.292 0.31 0.137 0.067 1
Qatar 0.256 0.267 0.177 0.317 0.339 0.087 0.096 0.431 1
Saudi Arabia 0.192 0.283 0.166 0.26 0.362 0.102 0.09 0.314 0.321 1
UAE 0.263 0.319 0.201 0.309 0.364 0.074 0.087 0.422 0.428 0.41 1
4. Methodology
In the last two decades, several approaches of connectedness, from simple to complex, have
been adopted and explored in the literature. The connectedness among the equity indices is
mostly derived out of linear or non-linear, bivariate or multivariate models. Basic statistical and
econometric models like Granger’s causality, Vector Autoregression, Johansen’s Cointegration,
GARCH, DCC GARCH, and Causality-in-variance have been used extensively. The models
have been used mainly to explore the bidirectional causality, linear cointegration and time-
varying dynamics of conditional correlation between the set of financial assets from same or
different classes (Shahzad et al., 2017). Since time-varying connectedness is a highly nonlinear
phenomenon, the linear models are unable to control the system-wide optimal inference of
degree of parameter variation (Tse, and Tsui, 2002). White’s theorem makes this clear that
‘linear models with time-varying parameters are very general approximations to arbitrary
nonlinear models’ (Granger, 2008). This section introduces the methodological framework and
effectiveness of non-linear GEVD connectedness and modern network theory in explaining the
system-wide connectedness of MENA markets.
4.1 GEVD Connectedness Framework
The moving average representation of the VAR is given by
X t=∑i=0
∞
A i εt−1 (1)
Where the N × N coefficient matrices Ai obey a recursion of the form
Qi=ψ1 A i−1+ψ2 Ai−2+…+ψ p Ai− p , (2)
With A0 is the N × N identity matrix and Ai = 0 for i < 0. The moving-average coefficients are
the key to understanding connectedness dynamics of MENA nations. In the VAR framework,
attempting to understand the connectedness of a large-scale system via the potentially many
hundreds of coefficients of VAR is typically fruitless. One needs a transformation of factors that
reveals better and more compact summary of system-wide and pairwise connectedness. GEVD
achieve this. For this study, we rely on N-variable error variance decomposition, introduced by
Sims (1980), which are transformations of the moving-average coefficients, and which allows
for each variable Xi to be added to the shares of its H-step-ahead error forecasting variance
coming from shocks of variable Xj, where ∀i≠j for each observation (Koop et al., 1996; Pesaran,
and Shin, 1998). The record of these cross-variance shares, provides information of spillovers
from one market to another. The aggregation of these decompositions will be subsequently used
to compute the pairwise directional connectedness of a specific market to any or to all the
markets under study (Diebold and Yilmaz, 2012). The KPPS H-step-ahead forecast error
variance decompositions, which is invariant to the ordering, can be defined for H= [1,2 , …∞ ) , as
ϑ ijg ( H )=
σ jj−1 ∑
h=0
H−1
(ei' Ah Ω e j )
2
∑h=0
H−1
(ei' Ω Ah
' ei)(3)
where Ω is the variance matrix for the error vector ε, σ jj is the standard deviation of the error
term for the jth equation and e i is the selection vector with one as the ith element and zero
otherwise.∑j=1
N
ϑ (H )≠ 100 means that the sum of the elements in each row of the variance
decomposition is not necessarily equal to 100. We normalizes each entry of the variance
decomposition matrix by the row and column sum as
~ϑ ijg ( H )=
ϑ ijg ( H )
∑j=1
N
ϑ ijg ( H )
(4)
After normalization, the sum of decompositions across any particular market (across row) is
∑j=1
N ~ϑ ijg ( H )=100, and across markets (across column) is ∑
i , j=1
N ~ϑ ijg ( H )=N . N can be greater or less
than 100. Therefore, ~ϑ ijg ( H ) can be seen as a natural measure of the pairwise directional
connectedness from market j to market i at horizon H. Equation (4) is used to determine the
‘From’ connectedness, that is, how all markets are contributing to a single market, ‘To’
connectedness, that is, how a single market is contributing to all markets, ‘Net’ connectedness,
that is, how the markets are interacting in a system, and ‘Total’ connectedness, that is, total
information flow among all markets under consideration. The H-step-ahead error variance is
used to measure these four specific variety of system-wide connectedness metrics, defined as
C ¿(i ←∎) (H )=∑j=1i≠ j
N ~ϑ ijg ( H )
∑i , j=1
N~ϑ ij
g ( H )×100=
∑j=1i ≠ j
N ~ϑ ijg ( H )
N×100 (5)
C ¿(∎← i) (H )=∑j=1i≠ j
N ~ϑ jig ( H )
∑i , j=1
N~ϑ ji
g ( H )×100=
∑j=1i≠ j
N ~ϑ jig ( H )
N×100 (6)
C i(Net )(H)=C∎← i ( H )−Ci ←∎ ( H ) (7)
CTotal ( H )=∑
i , j=1i ≠ j
N ~ϑ ijg ( H )
∑i , j=1
N~ϑ ij
g ( H )=
∑i , j=1i≠ j
N ~ϑ ijg ( H )
N(8)
The pairwise connectedness’s are used to demonstrate how much each MENA market
contributes to all the other markets, providing information about the channels of intra-regional
information transmission across the selected MENA markets, whereas the Total connectedness
shows the spillovers among all MENA markets. For details, please see Diebold and Yilmaz
(2012).
The static connectedness concept is built from pieces of rolling variance decompositions
to track the directional connectedness in real-time. This not only helps in managing the issue of
time zone but also helps in controlling the outliers resulted from the use of daily squared returns
as a proxy of absolute volatility, which causes a big problem in VAR estimation. However, in
our case, the same would help in capturing the change in connectedness structure during the
periods of crisis or high volatility. For effective connectedness measures, choice of rolling
window is analogous to bandwidth choice in density estimation. In this paper, we use a VAR (2)
approximating model with ten days error variance decomposition predictive horizon, and rolling
estimation window of 100 trading days, that is, five months average.
4.2 Network Graphs as Directional GEVD measures
In the late 1950s, Erdős–Rényi introduced two random graph models (Erdős and Rényi, 1959).
Both models found to be intuitive for creating sensible network maps. According to the first
model of Erdős–Rényi, a network can only be represented as an adjacency matrix A=[ A ij ] ,
having elements as either 1 or 0, where Aij=1 if nodes i and j are connected, and Aij=0
otherwise. The GEVD connectedness matrix [~ϑ ijg] is, however, a more refined version of the
network adjacency matrix A (Diebold and Yilmaz, 2014). The three main factors which make
connectedness matrix [~ϑ ijg] more compatible than the classical adjacency matrix A are: First, the
matrix [~ϑ ijg] doesn’t contain only 0 and 1; instead, the entries are weights. Second, the links are
directed, which means that the matrix [~ϑ ijg] is not symmetric, that is ϑ ij ≠ϑ ji ,∀ i∧ j Third, the row
sums of [~ϑ ijg] have the constraint that the summation of each row must be 100, instead of 1,
because the entries are variance shares. In particular, each row must sum up to 100, implies that
the total system-wide ‘From all others’ variance decomposition cannot exceed 100. But the
column sum of the matrix is free from any such restriction to accommodate the effect of high
intensity idiosyncratic shocks from the third country. As some of the MENA markets are more
vulnerable to external shocks, they are likely to transmit large intensity of shock received from
its strong economic or trade partner to other member of the group sharing strong economic &
trade relation with it. This way, idiosyncratic shocks sometimes creates a chain effect and turns a
mediocre crisis into a financial turmoil. This increases the degree of connectedness among the
nation pairs sharing close economic, & trade relationship. The Node degrees measue the system-
wide risk vuneribility (degree of dependency on internal and external markets) structure of the
MENA markets. Node degrees, From and To, are obtained by summing weights of ~ϑ ijg, defined
as:
δ i¿=∑
j=1j ≠i
N ~ϑ ijg
δ j¿=∑
i=1i ≠ j
N ~ϑ ijg
Also, unlike network adjacency matrix A, the diagonal elements of [~ϑ ijg] are not zero, implies that
the self-connectedness figures (diagonal elements of the matrix ~ϑ ijg) are critical too. The time-
varying variance decompositions matrix for specific sub sample windows would be used for
creating sensible network maps for exploring the changing dynamics of spillover direction,
intensity, and risk vulnerability of the MENA nations.
5. Results and Analysis
This section computes and examines the system-wide static, rolling, and network connectedness
of MENA markets. The section is divided into three sub-sections. First section analyses the static
connectedness of the full sample. The results are presented in the form of connectedness
table/matrix. Second undertakes the rolling connectedness of the net and total spillover of the
same. Third explains the change in spillover dynamics before and after the 2008 -09 financial &
oil crisis using network graphs.
5.1 Static Connectedness (Full-sample)
As can be seen from the connectedness matrix (Table 4), the diagonal elements (own
connectedness) have the highest individual values, ranging from 50.29 percent (UAE) to 91.27
percent (Morocco). When compared with total ‘To’ and ‘From’ directional connectedness, the
own connectedness is larger for all the countries, suggesting that in this region most of the
shocks come from within the country. The key substantive outcome is - refining the different
types of cross-country connectedness into a single connectedness index, called as ‘Total’
connectedness. In the case of the MENA region, it is 34.49 percent for the full sample period.
The ‘Total’ variance is relatively very small compared to other regions of the globe. It indicates
that 65.51 percent of the variation in the MENA region is because of unsystematic risks.
Regarding ‘To’ and ‘From’ connectedness, it is evident from the spillover connectedness
table that the UAE is the most connected stock markets with a ‘connectedness to others’ of 62.41
percent and ‘connectedness from others’ of 49.71 percent. It is mainly because its business
leaders and the policymakers have realized the benefits of globalization and the global cross-
border flows of capital, people, goods and services, and information well before the other
countries of the region. Moreover, the UAE is the largest trade hub in the Middle East and
Northern Africa region, thus enjoys the unique geographical location on the globe as it connects
the East and West. One of the newest uniqueness of the UAE is the establishment of Authorised
Economic Operator certification which allows it to create efficient and responsive supply chains
with other countries.
On the ‘From’ connectedness side, Oman (48.11 percent), Qatar (47.98 percent), Kuwait
(55.64 percent) and Saudi Arabia (43.09 percent) followed the UAE (49.71 percent) and are
moderately affected by the spillovers from other countries. The reason for each of these
countries to show high connectedness is that they are part of GCC. Hence they have a certain
commonality regarding the economy. On the contrary, the stock markets of Lebanon, Israel, and
Morocco affect very less from the shock arises from sample countries evident from low values
of ‘From Connectedness.’
On the ‘To connectedness’ side also, UAE leads with 62.41 percent. The next big four to
impact the rest of the MENA region are Qatar, Oman, Kuwait, and Saudi Arabia. Again, it is the
GCC region countries which dominate the other MENA economies with a cumulative market
capitalization exceeding the rest of the MENA region combined3. Oman and Kuwait have close
to 50 percent ‘connectedness to others’ showing moderate spillovers to others. The inclusion of
Qatar and UAE in MSCI Emerging Market Index4 indicates that these nations are progressing
towards the advanced economy. After the 2014-15 oil crisis, the government in the GCC region
has been extending the stock market reforms. The stock market of Morocco has lowest 6.79
percent ‘connectedness to others’ followed by Lebanon, and Israel. Low values are showing that
these markets are relatively independent of the other markets in the region.
The ‘Net connectedness’ indicates that the UAE (12.70 percent) is the most influential
market regarding net connectedness to others, followed by Saudi Arabia (3.18 percent). On the
other hand, Israel, Jordon, Lebanon, Morocco show negative net connectedness. The negative
value of net return connectedness is indicating that they are the net recipient of return shocks
from others and Bahrain in net terms is the most affected country by the shocks generated from
other countries of the MENA region. The pairwise analysis of the internal matrix of the
connectedness table shows that the directional pairwise connectedness between any pair of
3Source: http://www.visualcapitalist.com/all-of-the-worlds-stock-exchanges-by-size/ 4 Source: https://www.msci.com/emerging-markets
MENA stock market range from 0.18 percent to 10.24 percent, which means that some of the
stock markets of MENA region are relatively segmented while some are highly financially
integrated or dependent. The pairwise statistics suggest that UAE is the most influential
economy among all the nations in the sample. The stock markets of UAE and that of Oman
shows the highest pairwise connectedness as compare to all other pairs in the sample. The
pairwise directional connectedness from UAE to Oman is 10.37 percent and from Oman to UAE
is 8.99 percent. Also, the pairwise directional shocks are highest among the GCC countries.
Morocco is the least financially connected to other economies in the sample.
The total static connectedness between the MENA nations reveals the low level of
connectedness. The Gulf nations though show a high connectedness level among themselves.
However, their economy was dragged twice during subprime crisis 2008 followed by the 2014-
15 oil crisis. Noteworthy, high level of financial integration has pros of swinging together in
times of boom. However, times of distress engulfs all the financial partners too. As a result, the
onus of driving the overall growth of the MENA region by selected few GCC economies need to
assess further. The next section analyses, the benefits of MENA region integration. After that,
the pitfalls of same during times of distress have been discussed. The section tries to explore
what lies ahead in MENA region integration.
Table 4: Static Connectedness Matrix for Full Sample
To market iFrom market j Bahrain Kuwait Oman Qatar
Saudi Arabia UAE Egypt Israel Jordan Lebanon Morocco
Connectedness from others
Bahrain 65.59 8.44 5.35 4.42 3.44 5.69 1.77 0.72 2.94 0.93 0.71 34.41Kuwait 6.34 55.64 5.49 6.27 7.61 8.24 2.59 1.71 4.33 1.04 0.74 44.36Oman 3.91 5.73 51.89 9.63 5.86 10.37 3.61 2.37 4.74 1.45 0.45 48.11Qatar 2.89 6.19 9.75 52.02 6.28 10.05 3.60 2.52 5.12 0.92 0.67 47.98Saudi Arabia 2.12 7.35 5.98 6.03 56.91 9.73 4.52 2.02 3.90 0.76 0.68 43.09UAE 3.39 6.76 8.99 8.77 8.63 50.29 4.48 2.64 5.01 0.35 0.69 49.71Egypt 1.64 3.34 4.61 4.41 5.60 6.44 64.50 4.06 3.37 1.09 0.96 35.50Israel 0.68 1.60 2.81 3.18 2.46 3.69 3.35 80.47 0.71 0.64 0.41 19.53Jordan 2.67 5.14 5.20 6.68 4.31 6.39 3.92 1.25 62.34 1.17 0.92 37.66Lebanon 0.22 1.03 1.62 0.51 0.88 0.83 1.85 0.96 1.87 89.67 0.56 10.33Morocco 0.75 1.34 0.54 0.89 1.19 0.98 1.67 0.45 0.71 0.20 91.27 8.73Connectedness to others 24.61 46.93 50.33 50.78 46.27 62.41 31.35 18.68 32.71 8.54 6.79 34.49Net Connectedness -9.80 2.56 2.22 2.80 3.18 12.70 -4.14 -0.86 -4.94 -1.78 -1.94
Note: all non-diagonal values of the inner matrix is representing pairwise directional connectedness between market i and j. While Connectedness from others shows total directional spillovers from all markets j to market i, Connectedness to others shows total directional from the market i to all markets j. The Net connectedness row shows the difference between corresponding cells in the ‘connectedness to others’ row and the ‘Connectedness from others’ column.
5.2. Rolling Connectedness Analysis (Full-Sample)
The static connectedness analysis explained in the previous section is not helpful in
understanding how the level of connectedness has changed over the sample period. To get a
better understanding, we estimate the variance decompositions values using 100 days rolling
window and ten days error variance decomposition predictive horizon for the full sample. Figure
2 depicts the time-varying dynamics of ‘Net’ (difference of From and To) transmissions of
connectedness shocks. Figure 2 exhibit that the ‘Net’ connectedness for Oman and Egypt is
close to zero in almost all the years. However, Qatar and UAE show positive skewness across
zero, as they are a net transmitter of shocks across the MENA region. For Bahrain, Morocco,
and Lebanon the graph is mostly below the null line, indicating they are a net receiver of shocks.
Figure 3 depicts the dynamic plot of the system-wide ‘Total’ connectedness of the
MENA region. It illustrates that except for the crises period the ‘Total’ connectedness remained
within the range of 30 to 72. Post-crisis there is an upshot in the connectedness graph, and it
reaches an all-time high of 72. The oil crisis of 2015-16 again hikes the graph, depicting the
level of connectedness to go up during the crisis. The year 2011 marked protest in Tunisia that
spread widely to the rest of the Middle East and North Africa region, eventually become the
Arab Spring. Moreover, the sovereign debt crisis jolted the nations of the European Union;
Eurozone crisis brought stagnation across the entire world. The dip witnessed in the total
connected graph for year ranging 2011-2013 marks these two incidents primarily.
Figure 2: Net Connectedness
Figure 3: Total Connectedness
5.3 Network Connectedness (Pre-crisis vis-à-vis Post-Crisis)
The previous section explains the static and rolling connectedness of the MENA region robustly.
However, still we have not understood the sensitivity of the system-wide spillover pattern,
exposure to shocks, and vulnerability to risk during the pre- and post-crisis period. The time
frame for the pre-crisis period is July 2004 to December 2007, whereas Post-crisis time frame
ranges from April 2009 to December 2016. For the same, the section connects the network
graphs with the notion of connectedness matrix that we have found based on GEVD
connectedness measures.
The section constructs two network maps namely ‘Bidirectional Spillover Layout,’ and
‘Directional & Degree Spillover Layout.’ Each network maps consist of Nodes and Edges. Each
node represents the chosen country of the MENA region. The color of each node indicates the
degree of the total ‘Net’ connectedness, that is, the net difference of ‘To all others’ and ‘From all
others.’ Node colors Red, and Blue indicate they are the net transmitter of the shocks with color
Red being strongest and Blue the moderate. Node color Pink indicates strongest receiver of
shocks and color Green indicates a moderate receiver. The color of edges Red, Blue, and Green
represent strong, moderate and weak connectedness among the entities, respectively. In addition
to this, the thickness of an edge represents the intensity of shock. The color codes are based on a
set of threshold values inferred from the GVD connectedness matrix derived for the full sample
(Table 4) and the periods of pre and post-crisis (inferred separate connectedness matrix but not
shown in the paper due to space constraints).
Figure 4 (Panel A1 & A2) is quite informative about the bidirectional spillover
connectedness of the MENA region in the pre and post-crisis. The color of the edge arrows helps
to differentiate among the nation pairs reciprocating high, moderate or weak shocks. The
arrangement is in a counter-clockwise fashion ranging from the highest transmitter (red nodes)
to lowest transmitter (blue nodes), and then from the lowest receiver (green nodes) to highest
receiver (pink nodes). As an example, in the pre- and post-crisis window UAE is the highest
transmitter and Bahrain lowest, in fact, Bahrain is a strongest net receptor of shocks. UAE stock
market being well connected globally transmits maximum shock to rest of the MENA region.
The red color nodes are the most dominant members of GCC; hence as apparent, they transmit
maximum shock across the MENA region. Interestingly, in the pre-crisis period, none of the
countries of the MENA region were a strong receiver of shock, however, after the subprime
crisis, strong receptors peek in. In fact, UAE is the only GCC nation to remain a net transmitter
throughout the time frame. Whereas some non-GCC members of the MENA like Jordon, Egypt,
and Israel are the moderate transmitters of shocks in the pre-crisis, in the post-crisis period none
of them would able to retain the position, all the four transmitting nations are from GCC.
Lebanon and Morocco are two nations that do not receive any high-intensity spillover, neither in
pre- nor in post-crisis. Logically, this can be due to two reasons, either they are system-wide
weakly connected, or if they have high bidirectional connectedness, then the two high-intensity
shocks offsetting each other, and as a result, we witness a moderate spillover. Analysis of Figure
4-Panel B1 reveals that both the nations are weakly connected with the rest of the economy of
MENA region. Interestingly, a nation may be poorly connected and may be receiving moderate
to low levels of spillovers. However, its vulnerability to risk can be characterized by some
transmissions it is exposed too. Evidently, except the UAE-Saudi, and Saudi-Kuwait, none of
the GCC nations are sharing a high level of bidirectional connectedness with each other. The
bidirectional connectedness among non-GCC countries of the MENA region is very weak,
depicted by pink color edges with extremely low intensity.
Importantly, as the GCC nations are the primary economic drivers of the MENA region,
they do show some connectedness with remaining economies. Regional vicinity does come into
play in the level of connectedness, especially when other economic factors have a relatively low
impact in shaping the level of connectedness. Analyzing the network diagram vis-à-vis GDP
growth of MENA region nations reveal that the nation pair which is strongly connected to each
other show high levels of growth before the pre-crisis era. It seems the onus of driving growth of
MENA region lies with four major oil economies of the MENA region, namely, UAE, Saudi
Arabia, Kuwait, and Oman. The strong financial integration among them is reflected by the fact
that these four nations saw the highest growth in GDP before the pre-crisis era. However, rest of
the regions of the MENA economy still lag behind in terms of financial integration. The question
that arises is that financial integration is needed among all the MENA nation. Is it growing to
drive the overall growth of the region? Will every country prosper if the region gets highly
financially integrated? A first impression of the remarkable growth of highly connected
economies reveals the fact that the growth of one country pushes forward the growth of other
highly connected nation. As an example, a strong spillover from UAE to Oman and Saudi to
Kuwait reveals that upheaval in stock exchanges in UAE and Saudi passes it to exchanges in
Oman and Kuwait respectively.
However, does financial integration has always a good side is a matter of debate. It can
be analyzed better by post-crisis bidirectional scenario vis-à-vis growth or fall in GDP. Panel
A2 shows that the level of connectedness has increased in the post-crisis period, depicted by an
intense concentration of bold, and thick red and blue edges across the GCC cluster with Qatar a
new add-on as a strong net transmitter. Post-crisis aftermath had dwindling effects across the
global economy. The financial institutions struggled, and their resilience structure became
questionable. In that sense, the economy of the MENA region got more connected, as evident
from an increase of Red color edges across the nodes (Panel A2). UAE, Saudi Arabia, and Qatar
are among the highest transmitters and have shown strong connectedness post-crisis. Their level
of connectedness with GCC nations becomes moderate, depicted by blue color edges. However,
Morocco still falls on the lower side of connectedness. The change in the level of connectedness
can be analyzed further to correspond to a fall in average GDP post-crisis, depicted in Figure 4A.
Interesting to note that the less connected regions show more fall in GDP growth. It may signify
that the stock market connectedness acts as a cushion in case there is a fall in the stock market of
one nation. As the financial integration involves the flow of capital across different stock
markets, the GCC nations close integration acts as a support mechanism in case of the fallout of
any one nation among them. Though during the subprime crisis stock markets worldwide were
affected, however, the stock markets of the GCC nations help to mitigate the overall loss. A
system-wide analysis of bidirectional connectedness on a comparative basis for pre-crisis and
post-crisis phase reveals that the GCC nations are more connected to the MENA region and are
the sole driver of regional growth. The strong levels of connectedness payout during times of
distress, thus the connected nation act as a cushion for each other to mitigate the net loss.
Panel A1: Pre-crisis Panel A2: Post-crisis
Note: Node Threshold Level: Red > 10, 0≤Blue≤10, -5≤Green<0, Pink < -5 Edge Threshold Level (Bidirectional): Red > 6, 3≤Blue≤6, Green < 3
Figure 4: Bidirectional Spillover Network Layout (Pre versus Post-crisis)
Bahrain
KuwaitOman
Qatar
Saudi Arabia
U.A.E.Egypt
Israel
Jordan
Lebanon
Morocco
-80.00%
-70.00%
-60.00%
-50.00%
-40.00%
-30.00%
-20.00%
-10.00%
0.00%
Average GDP Growth Rate Fall
Figure 4A: Fall in the average GDP growth rate from pre-crisis to post-crisis
Figure 5-Panel B1 and B2 depict the Net pairwise directional spillover connectedness of MENA
markets. The edge between any two nodes has only one-way (equal to the net pairwise
connectedness measures between the two respective nodes). Figure 5 also provides the
vulnerability to risk via varying degree of transmitters, arranged in a counter-clockwise manner
from highest to lowest. UAE transmits a maximum number (outward edges) of shocks to rest of
the nations of the MENA region in both pre and post-crisis periods (Panel B1). If we move
counter-clockwise, the vulnerability (degree) of transmission decreases whereas the vulnerability
of reception increases. As we complete the circle and reach Bahrain, we can observe that it only
receives shocks. Interestingly, Morocco and Lebanon though having low connectedness and
receive moderate spillover, escalate high in the ‘Degree’ ranking regarding exposure to risk. The
possibility of any economic misfortune with the numerous countries they are connected to may
spread to these nations as well.
Noteworthy, Bahrain which was more vulnerable to risk in the pre-crisis period (Panel B1) turn
into the strongest net transmitter of shocks in the post-crisis period, evident from Figure 5-Panel
B2. In the post-crisis period, Jordan, Lebanon, and Morocco escalate as high as the strongest
transmitter of shocks after Bahrain. Lebanon and Jordan entered the crisis with very elevated
levels of fiscal and current account deficits in 2007 and 2008, with the 2008 current account
deficit to GDP ratios more than 14 percent, and with a fiscal deficit to GDP ratios in the 8-10
percent range. Unlike the other three countries that have energy resources, Lebanon and Jordan
have grown to depend on various forms of external financing to fund their massive current
account deficits. With higher spreads on sovereign bonds, poorer prospects for FDI and
remittances, the global meltdown shocks were more prominently felt by these nations. Contrary
to this, Egypt which was a net transmitter of shocks during the pre-crisis period became a net
receiver of shock due to an increased level of connectedness via liberalized foreign exchange
system. The crucial step of Saudi Arabia to open its stock market (largest stock market in the
Middle East) to global investors in 2015 and Qatar’s initiatives to diversify its economy from
oil-based has increased their market connectedness.
Noteworthy, the nation closely interlinked to each other oscillate together regarding
reaching extremities on returns. As the MENA region is highly sensitive to global oil demand
and crude oil prices, the rising oil prices with a prudent fiscal measures post-subprime crisis
acted as a safety valve for its financial markets, especially for the oil-driven economies.
However, America’s shale oil and gas revolution, Chinese economic slowdown and intense
internal conflict particularly after the rise of ISIS (Islamic State of Iraq and al-Sham) had
substantial economic cost on MENA region stock markets during 2014-2016. The GCC
countries carry an edge regarding economic prowess. Egypt and Jordon need more reforms in
the capital market backed up by social and political changes to be in the league of GCC nations.
Lebanon is relatively aloof from the regional connectedness, still struggles to establish itself as
an important player in the MENA region partly due to delayed reforms. The results of
bidirectional and directional graphs evident that its oil-rich economies, that is, GCC nations are
more strongly connected and dominates the region, and, are primarily responsible for
transmission of shocks to non-GCC nations.
Panel B1: Pre-crisis Panel B2: Post-crisis
Note: Edge Threshold Level (Directional): Red > 1, 0.5≤Blue≤1, Green < 0.5
Figure 5: Directional & Degree Spillover Network Layout (Pre versus Post-crisis)
6. Conclusion
In this paper, we applied the confluence of GEVD and Network graphs to deliver an improved
estimation of system-wide and pairwise spillover connectedness of the equity markets of the
MENA region. The approach allows us to quantify system-wide and pairwise volatility spillover
robust to ordering in VAR and capturing asymmetries in volatilities. The paper is comprehensive
in a sense as it provides a macro level overview regarding changing dynamics of volatility
connectedness of the equity markets of the MENA region depicted via network graphs. The
study deepens the role of network measures in building early warning models of market-wide
systemic risks. The study mainly emphasized the investigation of volatility transmission during
full sample and before and after the global financial crisis of 2008, and Oil Crisis 2008-09. For
the full sample, we found system-wide connectedness of 33.93 percent. This indicates that as a
congregation MENA region has to do a lot to increase the system-wide connectedness. However,
its oil-rich economies, GCC nations are more strongly connected and dominates the region,
hence are primarily responsible for transmission of shocks. Additionally, GCC nations are the
major drivers of growth of MENA region acting as a support mechanism to provide a cushion to
the connected economy during times of distress. As a result, Lebanon and Morocco showing the
relatively low level of connectedness bear the brunt and witness a sharp fall in their average
GDP growth during times of distress. The information plays a crucial role in portfolio
diversification. Network graphs exhibit that the investors should take a careful approach while
weighing the benefits of low connectedness against the exposure to vulnerability risk to finalize
the stock markets for investment. The study supports portfolio managers in identifying the
markets for asset allocation. It also serves the policymakers to take adequate measures to
safeguard the local economy from regional shocks proactively.
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