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: vksingh@nitie.ac.inPhone (Office): +91-22-28035200, Extn: 5547

Mobile: +91-9665840592

Shreyank NishantIndian Institute of Technology

Guwahati -781039, IndiaEmail: shreyank@iitg.ac.in

Pawan KumarDeGroote School of Business, McMaster University

1280 Main Street West, Hamilton, Ontario L8S4L8, CanadaEmail: pawanku@mcmaster.ca

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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