Transcript Inter and Intra-Regional Linkages to MENA Equity Market

Evolution of Inter and IntraRegional Linkages to MENA Equity
Market
Eric Girard
and
Eurico Ferreira
Introduction
Mellon Capital:”Our global tactical and strategic
asset allocation products…invest in developed
country markets…and treat other less liquid
markets as a block…”
An important question examined in this paper is
whether or not thin emerging capital markets such as
MENA capital markets should be treated as a block
in a global strategic or tactical portfolio.
The proximity (geographic, culture, religion, etc.) of
the countries may lead one to conclude that there is
a close connection between their economies 
hence, susceptibility to be sensitive to shocks from
neighboring countries.
MENA Markets: some issues
• Wars, political turmoil, economic instability and
institutional underdevelopment have traditionally been
powerful obstacles to an increased access to MENA capital
markets.
• Small capital markets with recent economic and financial
development geared towards an increase in openness to
foreign investors.
• During the nineties, Egypt, Israel, Jordan, Lebanon,
Morocco, Tunisia and Turkey have been progressively
lifting foreign investors’ ownership, and capital and
dividends repatriation restrictions. Even the traditionally
closed Gulf Country Council markets have become more
accessible to foreign investors through international funds
and trusts.
MENA Capital Markets overview (2000)
Country
Bahrain
Egypt
Israel
Jordan
Kuwait
Lebanon
Morocco
Oman
S.Arabia
Tunisia
Turkey
Asia
EUR
EEU
LA
NA
Market Cap.($ Billion)
6.6
28.5
66.8
4.95
19.8
1.58
10.9
3.46
73
2.80
69.5
2,607
5,879
93
250
10,088
#Stocks
41
1076
665
163
86
13
53
133
80
44
315
>8,000E
>8,000E
>800E
>1,300E
>8,000E
Financials: MENA Vs. G7 and other EM countries
Std. Dev Correlation
Country
Mean
Bahrain
-17.38%
8.63%
0.009
Egypt
3.66%
22.09%
0.003
Israel
5.01%
26.39%
0.387
Jordan
-0.80%
12.28%
0.016
Kuwait
6.07%
11.23%
0.002
Lebanon
-13.01%
15.90%
0.006
Morocco
6.35%
11.73%
0.002
Oman
-26.64%
13.38%
-0.009
14.45%
0.056
Saudi Arabia 4.32%
Turkey
-1.06%
54.05%
0.132
Tunisia
2.41%
10.88%
-0.008
EM
-0.55%
16.27%
0.494
World
6.02%
12.29%
1.000
G7
5.89%
12.78%
0.992
Lit. Review stuff
• Abraham, Seyyed and Al-Elg (2001) study Bahrain, Kuwait
and Saudi Arabia using monthly index returns from 1993 to
1998, and observe low or negative correlations between
markets5 years, 3 markets, 60 data points
• Omran and Gunduz (2001) use a multivariate cointegration
methodology and find no long term stochastic trends
between Jordan, Turkey, Egypt, Israel and Morocco from
January 1996 to June 19993.5 years, 5 markets, 42 data
points
• Darrat, Elkhal and Hakim (2000) find long-term bivariate
cointegrative relationships for Morocco-Egypt and
Morocco-Jordan, but no multivariate cointegrative
relationships between the three capital markets from
October 1996 to August 1999 2.8 years, 5 markets, 34
data points
Motivation and research questions
• Previous studies small samples, few markets
inexistence of intra-regional long-term (stochastic) price
linkages.
• Remaining questions to be answered:
– Any intra-regional spillovers (short-lived linkages)?
– Any inter-regional spillovers between MENA capital
markets and other regional blocks?
– Any spillovers from the three major international
financial crises that occurred during the 90s?
– Evolution of short-run price linkages that reveal a
globalization trend as observed with most emerging
markets during the 90s?
Data
• 11 MENA markets (Bahrain, Egypt, Israel, Jordan, Kuwait,
Lebanon, Morocco, Oman, Saudi Arabia, Tunisia, and
Turkey) and 5 regional indices (Asia AC, Europe, East
Europe, Latin America AC, and North America)
• Daily, weekly and monthly frequency Index series (MSCI,
IFC, Local Datastream); Span: 1990 to 2001; also all data
are in US Dollarscurrency risk set to zero.
• Spillover study is done using daily data:
– Capture potential short-lived interactions—I.e.capital movement
are intrinsically short-term occurrencesFinancial information
networks are capable of disseminating news instantaneously
around the world, a shock in a national stock market can be
transmitted to another market within a very short period of time.
– Many of our series have less than eight years in coverageserious
methodological issues with using too few data points.
– Test results could be affected by infrequent trading.
Methodology
• 2 Market Linkage issues:
– Global Strategic Asset Allocation (GSAA): Long horizon
Cointegration analysis
• Series are I(1)?Tests of stationarity (ADF and KPSS)
• Bilateral cointgration (long term bivariate conintegrative relationship)
• Multilateral cointegrationLong-term common stochastic trends
– Global Tactical Asset Allocation (GTAA): Short Horizon 
Spillover Issue: Pooled restricted GARCH-VAR methodology on
price differences-I(0).
• Generalized Variance decomposition function (%endogenous and
%exogenous of total variance forecast)SIZE
• Generalized Impulse Response function: forecast the effect of 1 SD shock
in ALL endogenous variable SIGN, TIMING
• Block Exogeneity Granger CausalityPREDICTABILITY
• Geweke CausalityINSTANTANEOUS SPILLOVER
• Dynamic of spillovers: Pooled methodology
Long-term Stochastic Trend (we need to go through this one
before we do the fun stuff)
k 1
xt  0 yt   i xt i  othertrend stuff   t
i 1
Bilateral Cointegration Results:
•Intra-regional long term linkages (4 out of 55)
–Bahrain-Jordan; Israel-Turkey; Morocco-Saudi Arabia; MoroccoTunisia
• Inter-regional long term linkages (6 out of 55)
–Israel-North America; Morocco-North America; Saudi-ArabiaNorth America; Tunisia-North America; Kuwait-East Europe;
Tunisia-Europe
•Multivariate cointegration analysis: No common stochastic trends;
reverse cointegration tests (Hansen and Johansen, 1999) support findings
•Cointegration tests reveal some pairwise but no common stochastic
trends to all MENA markets—i.e., no long-run co-movements
GARCH-VAR:
j  n p 1
S j ,t       i, j S j ,t i  e j ,t
j 1 i 1
 2j ,t    e 2j ,t   2j ,t 1
This is a
system, so
variables
are vectors
• “Diagonal” VAR-GARCH (see Engle and Sheppard, 2001).
By including a GARCH process for each equation of the
VAR heteroskedasticity is gone—i.e., the greatest
drawback of the VAR methodology.
• Covariances (as in an MGARCH model) are assume to be
negligible so that our (Quasi) likelihood estimator will not
die on us—i.e., an MGARCH-VAR with 16 variables would
require us to estimate, 16 AR equations, 16 variance
equations, and 120 covariance equations.
Then, What? And How?
•
•
•
•
AmplitudeGVDF
SignGIRF
TimingGIRF
Direction
– Granger Causality
(Lead-lag)
– Geweke Causality
(contemporaneous)
Dynamic process
VARGARCH is
pooled forward
multitude of
GARCH-VAR…over
time.
multitude of GVDF,
GIRF, GC and Geweke
stuff…over time.
Generalized Variance Decomposition Summary
Jordan with MENA
Turkey with MENA
Israel with MENA
Egypt with MENA
16.00
Kuwait with MENA
Morocco with MENA
Lebanon with MENA
Saudi Arabia with MENA
14.00
Tunisia with MENA
Bahrain with MENA
Oman with MENA
12.00
10.00
8.00
Proportion of exogenous
variance coming from MENA
6.00
4.00
2.00
0.00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Jordan with Regional
Turkey with Regional
Israel with Regional
Egypt with Regional
Kuwait with Regional
Morocco with Regional
Lebanon with Regional
Saudi Arabia with Regional
Tunisia with Regional
Bahrain with Regional
Oman with Regional
2000
2001
2000
2001
14.00
12.00
Proportion of exogenous
variance coming from Blocks
10.00
8.00
6.00
4.00
2.00
0.00
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
GIR Intra-regional Exogenous Shocks:
Israel
2.5
D(BA)
D(EG)
D(IS)
D(JO)
D(KU)
D(LE)
D(MO)
D(OM)
D(SA)
D(TK)
D(TU)
2
1.5
1
0.5
0
1
-0.5
-1
3
5
7
9
11
13
15
17
19
GIR  Intra-regional Exogenous Shocks:
Morocco
D(BA)
D(EG)
D(IS)
D(JO)
D(KU)
D(LE)
D(MO)
D(OM)
D(SA)
D(TK)
D(TU)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
-0.2
-0.4
3
5
7
9
11
13
15
17
19
GIR  Intra-regional Exogenous Shocks:
Jordan
0.7
D(BA)
D(EG)
D(IS)
D(JO)
D(KU)
D(LE)
D(MO)
D(OM)
D(SA)
D(TK)
D(TU)
0.6
0.5
0.4
0.3
0.2
0.1
0
1
-0.1
3
5
7
9
11
13
15
17
19
GIR  Inter-regional Exogenous Shocks:
Israel
2.5
D(IS)
D(AS)
D(EU)
D(EAST)
D(LA)
D(NAM)
2
1.5
1
0.5
0
1
-0.5
3
5
7
9
11
13
15
17
19
GIR  Inter-regional Exogenous Shocks:
Morocco
D(MO)
D(AS)
D(EU)
D(EAST)
D(LA)
D(NAM)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
-0.2
-0.4
3
5
7
9
11
13
15
17
19
GIR  Inter-regional Exogenous Shocks:
Jordan
0.7
D(JO)
D(AS)
D(EU)
D(EAST)
D(LA)
D(NAM)
0.6
0.5
0.4
0.3
0.2
0.1
0
1
-0.1
3
5
7
9
11
13
15
17
19
Summary of shocks: GIRF
The more integrated economies of Israel and Turkey seem to process information flows
from global markets and act as conduits to other, smaller, MENA markets.
Intra-Regional (Exogenous)
Size
Timing
Israel, Turkey
Large
High persistence
Morocco, Bahrain, Egypt, Kuwait, Lebanon,
Oman, Tunisia
Large
Rapid decay
Jordan, Saudi Arabia
Inter-regional (Exogenous)
Small
Size
Rapid decay
Timing
Israel, Turkey
Large
Rapid Decay
Morocco, Bahrain, Egypt, Kuwait, Lebanon,
Oman, Tunisia
Small
Rapid Decay
Jordan, Saudi Arabia
Inexistent
Rapid Decay
Endogenous shocks
Size
Timing
Israel, Turkey
Large
Rapid Decay
Morocco, Bahrain, Egypt, Kuwait, Lebanon,
Oman, Tunisia, Jordan, Saudi Arabia
Large
High persistence
Causality Analysis
• Granger causality(lead-lag): Consistent with GVDF, No feed back
relationships
• Geweke causalitycontemporaneous relationship
– Most of the residuals correlation coefficients are positive 
investors view other regional economies as prone to different
events.
– Residuals correlation are negative for four GCC market, indicating
that capital tend to flow naturally from one market to another.
Similar findings by Hassan (2003) who examines linkages among
Bahrain, Kuwait and Oman stock markets from October 1994 and
August 2001.
– Increase in contemporaneous spillover from other regional blocks
– Interesting case of Turkey and Israel increasing amount of
contemporaneous spillovers between Israel and the five regional
indices lead-lag spillover analysis fails to capture existing
linkages that have become increasingly contemporaneous. The
same conclusions can be drawn from Turkey and to a lesser extent
for Morocco and Tunisia.
Conclusion
Results from using the IR and VD functions illustrate a
striking feature of MENA markets, namely the slow and
small transmission of shocks during any period of this
study. Results of our four spillover tests (Granger, GIRF,
GVDF, residuals correlation, and Geweke) provide
evidence that MENA markets are gradually opening to
other regional and trans-continental economies, but
remaining highly segmented (to the exception of Turkey
and Israel) and perhaps predictable. In this case, tactical
asset allocation strategies across MENA markets can be
beneficial.