On the Dynamic Transmission of Mean and Volatility across the Arab Stock Markets

Abstract

This article applies a multivariate model to uncover the dynamic mean and volatility interdependence across the markets of Morocco, Tunisia, Egypt, Lebanon, Jordan, Kuwait, Bahrain, Qatar, United Arabic Emirates (UAE), Saudi Arabia and Oman from June 2005 to January 2012. Results show that the Arab Middle East and North African equity markets are interconnected by their volatilities and not by their returns, which makes risk reduction possible. Volatility persistence and innovations in one market enclose figures that are valuable to investors and risk managers seeking to predict volatility in other markets. Surprisingly, we find evidence of significant volatility spillover from small to larger markets.

JEL Classification: G1, G15, G17, C22, C32

Keywords

Equity returns MENA multivariate GARCH mean spillover volatility spillover conditional correlations

Introduction

Quantifying the risk of equity markets is an important component of financial decision making. With much of the information can be revealed in the volatility of stock price rather than in the price itself, the interest to study and comprehend this volatility is always an issue that represents a central need for investors. In particular, the degree of interdependence between the volatility of equity markets is a key variable to risk and portfolios managers. As crises and shocks modify this interdependence, international and regional investors should take into account the volatility dynamics in their investment strategies to reduce risk and optimise returns.

A large body of past literature attempts to quantify developed market interrelationships, return co-movements and volatility spillovers. However, less is known about these issues in the emerging markets of the Middle East and North African (MENA) region.

Several studies have focused on the issue of efficiency as well as on international integration of MENA stock markets (Abraham et al. 2001; Assaf 2003; Bailey et al. 2005; Bley and Chen 2006; Hakim and Neaime 2000; Lagoarde-Segot and Lucey 2008; Neaime and Colton 2005; Yu and Hassan 2008).

Others have examined return and univariate analysis of its volatility in selected MENA stock markets (Alsybaie and Najad 2009; Bekaert and Harvey 1997; Brooks 2007; Hammoudeh and Choi 2007; Khedhiri and Muhammad 2008; Neaime 2006; Nikkinen et al. 2008).

Not many studies have explored the interdependence between volatility of returns in selective MENA markets and with that of the developed markets or with that of the oil market (Hammoudeh and Li 2008; Haque et al. 2004; Maghyereh and AL-Kandari 2007; Malik and Hammodeh 2007; Rao 2008; Yu and Hassan 2008). While, Hammoudeh et al. (2009) have examined the volatility spillovers between three equity sectors (service, banking and industrial) in Kuwait, Qatar, Saudi Arabia and UAE. On the other hand, Abdelaziz Eissa et al. (2010) examined the presence of volatility spillovers between sectors’ stock returns and nominal effective exchange rates’ changes in Egypt, Morocco and Turkey.

The transmission of mean and volatility across the 11 stock markets in the MENA region is not fully explored. Thus, we seek to close this research gap with recent data and a large sample composition. In term of econometric methodology, we seek to contribute to the literature by building upon the work of Lee (2009) in an extension of Engle and Kroner (1995) model, and by using the Multivariate Threshold Generalised Autoregressive Conditional Heteroskedasticity (MTGARCH) model that can capture the asymmetric impact of information on returns volatility. The choice of the stock markets in the MENA countries is motivated by the aim to study their volatilities co-movements giving the unrest in this politically troubled and unstable region of the world. As volatility needs monitoring and presents a major risk to investors and portfolio managers, the question of whether the interdependence of MENA markets can help us to predict the volatility in a given market remains an interesting topic for academicians and practitioners. Furthermore, uncovering volatility transmitters is important for regulators and policymakers seeking the stability of the financial markets through a timely responses to shocks.

Our main findings indicate that MENA stock markets are only connected by their volatilities and not by their returns.

The rest of the article is structured as follows. The second section presents the data and their statistical properties. The third section discusses the econometric methodology and provides the model. The fourth section reports and analyses the empirical results. Finally, the last section concludes the article.

Preliminary Statistical Analysis of the Data

We select our sample to include the daily closing prices of the Morgan Stanley Capital International (MSCI) equity indices in Morocco, Tunisia, Egypt, Lebanon, Jordan, Kuwait, Bahrain, Qatar, UAE, Saudi Arabia and Oman. Because the MSCI ceased to cover the Saudi equity market, we use instead the Standard and Poor’s Saudi Arabia Broad Market Index. For the sake of consistency, the data only covers the period from 1 June 2005 to 3 January 2012, as several MENA MSCI indices are not available before this date. The choice of the domestic type of MSCI indices aims to minimise the problem of autocorrelation in returns resulting from the high levels of company cross listing among the MENA equity markets. The values of MENA equity market indices are expressed in USD in order to eliminate the currency effects. This implies that investors are hedging against currency risk. All indices are also value weighted and measure the price performance of equity markets without including dividends.

Given that MENA equity markets do not share the same weekend and public holidays, we can only select a total of 1,310 observations per index from the common trading days. The dataset is obtained from the Data Stream base. We calculate the daily returns defined as the difference of the logarithm of the price index, scaled by 100. Figures 1 and 2 present levels and first differences of the series, respectively.

Volatility clustering which is typically observed in financial time series emerges from Figure 2. The latter shows that a high return (positive or negative) is more likely followed by another high return, or when a low return (positive or negative) is more likely to be followed by another low return.

Figure 1.

Source: Reuters Datastream.

Figure 2.

Source: Reuters Datastream.

Table 1 summarises key stock market figures, including the official trading hours and capitalisation of MENA equity markets. The official trading hours of the 11 markets are synchronous. The Saudi market is the largest equity market; Tunisia, on the contrary, is seen to be the smallest MENA stock market in the sample under study.

Table 1.

Official Trading Hours and Size of MENA Stock Markets

	Morocco	Tunisia	Egypt	Lebanon	Jordan	Kuwait	Bahrain	Qatar	UAE	Saudi	Oman
Trading hours	10:30–14:30	08:00–13:10	08:30–12:30	07:30–10:30	08:30–11:00	06:00–09:30	06:45–09:30	06:30–10:00	06:00–10:00	08:00–12:30	08:00–11:00
MC	69.15252	10.68170	86.61641	12.58559	30.86441	119.62095	20.42931	123.59180	104.66888	353.41405	20.26720
MC/GDP %	75.81247	24.12092	37.73405	32.11728	111.91801	87.79159	82.29074	89.39705	47.71125	81.32219	36.91293

Source: Reuters Datastream.

Note: Trading hours are in GMT, other data in the table are end of year figures for 2010, MC (market capitalisation of listed companies in USD), MC/GDP (market capitalisation of listed companies as a percent of gross domestic product).

Table 2.

Descriptive Statistics of the Daily Returns

	Morocco	Tunisia	Egypt	Lebanon	Jordan	Kuwait	Bahrain	Qatar	UAE	Saudi	Oman
Mean	0.04218	0.03154	−0.00738	0.02428	−0.05305	−0.00814	−0.11435	0.00012	−0.075197	−0.03629	−0.01945
Maximum	5.49595	9.04171	8.49659	10.3295	7.92759	8.75073	11.4989	11.27048	18.33405	16.01684	10.75057
Minimum	−7.69834	−7.16228	−17.14687	−16.3538	−9.75109	−11.89381	−17.99502	−12.95479	−15.50027	−13.53412	−17.65108
SD	1.24229	1.07581	1.84617	1.62107	1.35205	1.58918	1.39551	1.73846	1.94652	1.89902	1.44586
Skewness	−0.34632	0.11654	−1.15858	−0.03091	−0.69930	−1.00193	−2.03681	−0.77292	−0.60579	−1.02128	−1.47248
Kurtosis	6.29561	9.43349	11.47941	17.00118	10.92041	12.34981	31.90331	13.03834	15.54756	16.28325	27.59244
Jarque-Bera	809.56341^a	2960.30581^a	5464.08909^a	14010.89135^a	4612.75030^a	6560.59234^a	60850.14508^a	7349.80173^a	11345.48159^a	12766.43992^a	43566.94805^a
LB-Q (10)	109.18901^a	52.65897^a	41.25815^a	34.07792^a	15.19257^a	32.85015^a	22.46405^a	25.42387^a	42.08101^a	19.08903^b	29.57484^a
ARCH-LM (10)	147.64535^a	91.50797^a	85.01901^a	104.51707^a	254.01738^a	341.91312^a	83.09541^a	230.01514^a	224.23955^a	282.56913^a	125.20357^a

Source: Reuters Datastream.

Note: SD (standard deviation), LB-Q (Ljung and Box Q-statistics), ARCH-LM (Engle Lagrange multiplier). For Jarque-Bera, Ljung-Box, and Engle LM tests, ^a, ^b, ^c indicate statistical significance at 1%, 5% and 10% levels respectively.

Descriptive Statistics

At an initial stage, Table 2 presents the statistical characteristics of the sample data.

Morocco has the highest mean return (0.04218), while Bahrain the lowest (−0.11435). In term of volatility, measured by the unconditional standard deviation, the UAE has the highest volatility (1.94652), while Tunisia the lowest (1.07581). The findings of Jarque Bera (1980) test indicate that daily equity returns do not have normal distribution. For all return series, the Jarque–Bera statistics reject the null hypothesis of normality of returns at 1 per cent significance level. All the MENA equity returns are in a leptokurtic distribution which is a common characteristic of financial variables (Claessens et al. 1995; Harvey 1995). Except for Tunisia, all remaining MENA indices have negative skewness which means that large negative returns are more common than large positive returns. The results of the Ljung–Box (1979) Q-statistics tests indicate that all return series display significant serial dependence. Also, the results of Engle (1982) Autoregressive Conditional Heteroskedasticity Lagrange Multiplier (ARCH-LM) test point to the presence of high persistence and time varying volatility.

An appropriately specified multivariate GARCH model with a non-normal conditional density for the residuals is suitable for modelling MENA daily equity return series to capture the significant levels of excess kurtosis exhibited in the standardised residuals.

Unconditional Correlations

Table 3 yields the unconditional correlation of returns among the MENA indices. This is a simple approach to measure the cross-market correlations of returns. The MENA equity market returns exhibit relatively moderate but positive pairwise correlations.

The correlation coefficient between Lebanon and Tunisia (0.06097) is the lowest; while the highest inter-stock market correlations are between the Arab Gulf markets (Kuwait, Bahrain, Qatar, UAE, Saudi Arabia and Oman), especially between Qatar and the UAE (0.50893). Such outcome is not surprising given the efforts spent among the Gulf countries to reach monetary unification.

Table 3.

Correlations of Daily Returns

	Morocco	Tunisia	Egypt	Lebanon	Jordan	Kuwait	Bahrain	Qatar	UAE	Saudi	Oman
Morocco	1.00000	0.23678	0.18129	0.06895	0.11273	0.09610	0.07108	0.12657	0.12459	0.13079	0.10831
Tunisia		1.00000	0.08650	0.06097	0.10554	0.11723	0.08138	0.09345	0.10938	0.08779	0.13581
Egypt			1.00000	0.15499	0.27696	0.18834	0.13791	0.30382	0.37498	0.29488	0.27809
Lebanon				1.00000	0.16829	0.11588	0.13051	0.10097	0.13682	0.13797	0.15072
Jordan					1.00000	0.24543	0.23738	0.33570	0.35936	0.29837	0.30404
Kuwait						1.00000	0.38542	0.31978	0.31809	0.27563	0.25905
Bahrain							1.00000	0.29180	0.30581	0.19072	0.29079
Qatar								1.00000	0.50893	0.31302	0.46891
UAE									1.00000	0.39824	0.47930
Saudi										1.00000	0.31348
Oman											1.00000

Source: Reuters Datastream.

Stationarity Tests

The first step prior to the application of a multivariate GARCH model is to check for stationarity of the return series. To this end, we perform an augmented Dickey–Fuller (Dickey and Fuller 1979) and Phillips–Peron (Philips and Perron 1988) unit root tests. For both tests, the optimal lag length (n) is chosen on the basis of the Akaike Information Criterion (Akaike 1987). The results are shown in Table 4. The ADF and PP t-statistics are statistically significant at the 1 per cent significance level, implying that both series follow a covariance-stationary process.

The non-normality, the volatility clustering and the positive correlations among the series returns lead us to select the multivariate GARCH framework which can model the interdependence of mean and conditional variance across the MENA stock markets.

The Return and Volatility Spillover Model

This article employs a simple and straightforward methodology that can be applied to any sets of data. Spillover effects in mean (or variance) occur when a change in return (or volatility of returns) in one market has a lagged impact (one day) on return (or volatility of returns) in one or several other markets. The effect of squared residuals in one country on the others is interpreted as volatility shock. However, the operating hours of the 11 markets are synchronous, that is, they open and close at about the same time which imply that these markets affect each other contemporaneously. As a result, there is no need to incorporate the squared residuals as lagged variables into the econometric model.

Table 4.
Tests for Unit Roots

Morocco Tunisia Egypt Lebanon Jordan Kuwait Bahrain Qatar UAE Saudi Oman

ADF −33.32210^a −24.55917^a −26.97941^a −12.64030^a −48.20582^a −8.52152^a −6.64045^a −7.88110^a −25.91963^a −23.74027^a −7.40954^a

PP −41.86495^a −39.46318^a −46.34183^a −45.01022^a −48.26880^a −40.37385^a −39.29020^a −38.74051^a −36.53521^a −48.53145^a −37.77518^a

Lag 1 2 2 12 0 14 19 24 1 3 19

Source: Reuters Datastream.

Note: ^a, ^b, ^c indicate statistical significance at 1%, 5% and 10% levels respectively.

The ultimate approach to analyse volatility co-movement across a set of markets is to model all the data series at once (Bala and Premaratne 2004). This simultaneous methodology has several advantages over the Vector Auto Regressive (VAR), the causality, and the univariate GARCH approaches. The VAR method overlooks nonlinearities, conditional heteroskedasticity and embeds some problems associated with estimated regressors (Stock and Watson 2001); the causality tests do not capture the sign and the magnitude of cross mean and volatility spillovers effects, but only displays their sources; while the univariate GARCH type method manifests deficiency in seizing at once the co-movements of variances among two or more time series. Therefore, we apply the Baba–Engle–Kraft–Kroner (BEKK) multivariate GARCH defined in Engle and Kroner (1995) to examine spillover effects into mean and volatility. This model has positive conditional covariance matrices by construction and allows the conditional variances and co-variances of markets to influence each other. Nevertheless, its symmetric characteristic treats bad news, expressed by negative signs, with the same influence on the volatility as good news, expressed with positive signs. In fact, bad news gives a greater impact on the volatility of returns in the stock market than good news. In order to seize the phenomenon of asymmetries of returns, also known as leverage effect (Black 1976), we will add an asymmetric term to the conditional variance equation. The model is specified as follows:
$R_{t} {MR}_{t 1}_{t}$ $_{t} GED (0, H_{t})$
(1)

whereR_t is a 11 × 1 vector of daily returns at time t for each index, φ is a 11 × 1 vector that denotes the constants, M is a 11 × 11 matrix of parameters m_ij that measures the effects of own lagged and cross mean spillovers from market i to market j among the 11 MENA markets, and the error ε_t is a 11 × 1 vector of the innovation for each market at time t and has a 11 × 11 conditional variance-covariance matrix, H_t. The variance can be specified as the following:
$H_{t} {CC}^{} A^{} (_{t 1} {^{}}_{t 1}) A {GH}^{}_{it 1} G D^{} (_{t 1} {^{}}_{t 1} d_{t 1}) D$
(2)

where $H_{t} (_{h_{11 1, t}}^{h_{11, t}}_{}^{}_{h_{11 11, t}}^{h_{1 11, t}}), C (_{c_{11 1}}^{c_{11}}_{}^{}_{c_{11 11}}^{c_{1 11}}),$ $A (_{a_{11 1}}^{a_{11}}_{}^{}_{a_{11 11}}^{a_{1 11}}), G (_{g_{11 1}}^{g_{11}}_{}^{}_{g_{11 11}}^{g_{1 11}}), D (_{d_{11 1}}^{d_{11}}_{}^{}_{d_{11 11}}^{d_{1 11}}) .$

C_t is a matrix of constants with 11 × 11 symmetric elements c_ij, A is a matrix with a 11 × 11 symmetric elements a_ij that measures the effects of lagged and cross innovation (squared residuals) from market i to market j, G is a matrix with a 11 × 11 symmetric elements g_ij that measures the persistence of conditional volatility between market i and j, d_t–₁ is a dummy variable equal to 1 if ε_t–₁ < 0 and 0 otherwise, and D is a matrix with a 11 × 11 symmetric elements d_ij that measures lagged and cross asymmetric effects from market i to market j.

To capture the characteristics that are associated with MENA equity series, we suggest the estimation of our models assuming multivariate General Errors Distribution (GED) of the residuals term. We also use the Berndt–Hall–Hall–Hausman (1974) algorithm to produce the maximum likelihood parameter estimates. Finally, we assess the robustness of our results using the LB-Q tests.

Empirical Analysis

This section reports the empirical results of mean and volatility dynamics across the MENA stock markets and analyses them at the 1 per cent significance level.

Analysis of Mean Spillover

Table 5 reports the estimated parameters for the conditional mean return in equation (1).

The coefficients of own mean spillover effects represented by the diagonal parameters m_ij in the matrix M are statistically significant indicating that the returns in every MENA market depend on their first own lags. Own mean spillover vary from 0.20941 for Bahrain to 0.08129 for Morocco; whereas, the second-largest own mean spillover can be seen for Qatar (0.19083) followed by Saudi Arabia (0.17792), UAE (0.17595), Egypt (0.16834), Oman (0.14504), Kuwait (0.12195), Lebanon (0.11779), Tunisia (0.09542) and Jordan (0.08057).

In contrast, the coefficients of cross mean spillovers effects, represented by the off-diagonal parameters m_ij of matrix M, are insignificant. We find no specific mean transmitter among the MENA markets. This finding implies that investors are concentrating on their own domestic returns and that regional diversification opportunities subsist.

Analysis of Volatility Spillover

The constant elements c_ij are the parameters of matrix C. The diagonal parameters a_ij in the matrix A measure the own lagged volatility spillover. Whereas, the off-diagonal parameters a_ij in the same matrix measure the cross volatility spillover impacts from market i to market j. Table 6 presents the estimated coefficients for MTGARCH conditional variance covariance equations.

Table 5.
Estimations Outputs of the MTGARCH Conditional Mean Equations

Country Morocco(i = 1) Tunisia(i = 2) Egypt(i = 3) Lebanon(i = 4) Jordan(i = 5) Kuwait(i = 6) Bahrain(i = 7) Qatar(i = 8) UAE(i = 9) Saudi(i = 10) Oman(i = 11)

φ 0.04364^b 0.01824 0.07371^b –0.00244 –0.02386 0.05394^c –0.02782 0.06119^b 0.01774 0.01577 0.01632

[0.01037] [0.01560] [0.02730] [0.02164] [0.02464] [0.02850] [0.02418] [0.02767] [0.03386] [0.00195] [0.02097]

m_i1 0.08129^a 0.09695 –0.03019 0.00369 0.00819 0.02195 0.01543 0.00809 0.00721 0.00993 0.00438

[0.00195] [0.00378] [0.00305] [0.00495] [0.00502] [0.00107] [0.00633] [0.00394] [0.00233] [0.00789] [0.00872]

m_i2 –0.05593 0.11327^a –0.05004 –0.02593 0.02395 0.02775 0.00393 0.02259 0.01621 0.01693 0.00728

[0.00429] [0.09542] [0.00721] [0.00208] [0.00514] [0.00270] [00.00175] [0.00102] [0.00599] [0.00775] [0.00625]

m_i3 –0.04491 0.05769 0.16834^a 0.05919 0.03174 0.01355 0.02243 0.01709 0.03721 0.02993 0.01018

[0.00301] [0.00713] [0.00398] [0.00595] [0.00793] [0.00598] [0.00112] [0.00188] [0.00574] [0.00891] [0.00687]

m_i4 0.00382 0.00806 0.00721 0.11779^a 0.00989 –0.00775 –0.01393 0.02159 –0.03179 0.02293 0.01308

[0.00184] [0.00407] [0.00603] [0.00280] [0.00198] [0.00380] [0.00651] [0.00775] [0.00834] [0.00534] [0.00205]

m_i5 0.00595 –0.00781 0.00584 –0.05068 0.08057^a 0.02195 0.04543 0.02609 –0.01079 –0.02407 0.03598

[0.00285] [0.00418] [0.00769] [0.00579] [0.00093] [0.00056] [0.00854] [0.00579] [0.00402] [0.00613] [0.00789]

m_i6 0.05938 0.08693 0.09128 0.05758 0.05149 0.16877^a –0.08037 0.01759 –0.02629 –0.01307 0.03898

[0.00512] [0.00292] [0.00137] [0.00921] [0.00869] [0.00291] [0.00318] [0.00790] [0.00261] [0.00432] [0.00491]

m_i7 –0.02718 0.01850 0.08297 –0.03351 –0.04398 0.04855 0.20941^a –0.07741 0.03271 –0.02107 0.07357

[0.00689] [0.00195] [0.00253] [0.00463] [0.00297] [0.00421] [0.00286] [0.00139] [0.00188] [0.00816] [0.00159]

m_i8 0.04289 0.07741 –0.05181 0.02719 0.02574 0.03305 0.05041 0.19083^a –0.07729 –0.05807 0.07949

[0.00138] [0.00875] [0.00492] [0.00151] [0.00720] [0.00872] [0.00184] [0.00938] [0.00409] [0.00487] [0.00338]

m_i9 0.02795 0.03538 0.05985 0.09068 0.08181 0.02885 –0.04191 0.06581 0.17595^a 0.05107 0.08241

[0.00308] [0.00919] [0.00273] [0.00801] [0.00795] [0.00964] [0.00299] [0.00731] [0.00591] [0.00125] [0.00136]

m_i10 –0.07039 0.09791 –0.05983 0.09189 0.07381 –0.05305 –0.08041 –0.06131 0.08708 0.17792^a 0.04109

[0.00092] [0.00903] [0.00354] [0.00167] [0.00684] [0.00115] [0.00581] [0.00791] [0.00828] [0.00943] [0.00151]

m_i11 0.01331 0.02195 0.01499 –0.02237 0.03714 –0.01725 –0.01541 –0.05631 0.07308 0.05691 0.14504^a

[0.00967] [0.00134] [0.00213] [0.00390] [0.00742] [0.00962] [0.00321] [0.00881] [0.00635] 0.00464] [0.00381]

Source: Reuters Datastream.

Note: ^a, ^b, ^c indicate statistical significance at 1%, 5% and 10% levels respectively. Standard errors are reported in brackets.

Table 6.
Estimations Outputs of the MTGARCH Conditional Variance-Covariance Equations

Country Morocco(i = 1) Tunisia(i = 2) Egypt(i = 3) Lebanon(i = 4) Jordan(i = 5) Kuwait(i = 6) Bahrain(i = 7) Qatar(i = 8) UAE(i = 9) Saudi(i = 10) Oman(i = 11)

c_i1 0.08212^a 0.02502 0.00399 0.00491 0.00527 0.00750 0.00539 0.00350 0.00706 0.00295 0.00345

[0.01645] [0.00905] [0.00718] [0.00720] [0.00509] [0.00957] [0.00812] [0.00439] [0.00820] [0.00731] [0.00397]

c_i2 0.02268^a 0.06273^a 0.00270 0.00581 0.00097 0.00391 0.00309 –0.00174 0.00548 –0.00059 0.00720

[0.00797] [0.00923] [0.00793 [0.00523] [0.00349] [0.00710] [0.00550] [0.00492] [0.00730] [0.00291] [0.00522]

c_i3 0.00575 0.00313 0.04695^a 0.00723 0.00905 0.00592 0.01239 0.01288^b 0.01821 0.00359 0.00597^c

[0.00526] [0.00834] [0.01067] [0.00522] [0.00297] [0.00419] [0.00829] [0.00499] [0.00392] [0.00295] [0.00532]

c_i4 0.00670 0.00759 0.00793 0.12922^a 0.00719 0.00517 0.00310 0.00175 0.00509 0.00088 0.00175^a

[0.00441] [0.00610] [0.00603] [0.01486] [0.00544] [0.00552] [0.00782] [0.00729] [0.00739] [0.00832] [0.00589]

c_i5 0.00398 0.00001 0.00349 0.00878 0.03508^a 0.00691 0.00905 0.00337 0.00399^b 0.00295 0.00851

[0.00354] [0.00391] [0.00300] [0.00622] [0.00693] [0.00341] [0.00571] [0.00209] [0.00581] [0.00360] [0.00371]

c_i6 0.00849 0.00247 0.00711 0.00468 0.00589 0.04095^a 0.01099^b 0.00412^c 0.01055 0.00352 0.00194

[0.01238] [0.00436] [0.00526] [0.00682] [0.00386] [0.00789] [0.00293] [0.00297] [0.00285] [0.00435] [0.00392]

c_i7 0.00657 0.00383 0.01015 0.00219 0.00869 0.01071^a 0.08299^a 0.01261^b 0.01034^c 0.00710 0.01207^a

[0.00902] [0.00464] [0.00688] [0.00570] [0.00564] [0.00389] [0.01107] [0.00824] [0.00719] [0.00950] [0.00499]

c_i8 0.00438 –0.00093 0.01361^a 0.00142 0.00526 0.00584^c 0.01095^b 0.02939^a 0.02178^a 0.00548^b 0.01163^b

[0.00556] [0.00450] [0.00511] [0.00457] [0.00389] [0.00316] [0.00456] [0.00486] [0.00490] [0.00419] [0.00399]

c_i9 0.00546 0.00660 0.01443^a 0.00469 0.00832^b 0.01219^a 0.01940^a 0.02510^a 0.06890 0.00772 0.01045

[0.00640] [0.00572] [0.00525] [0.00744] [0.00406] [0.00452] [0.00644] [0.00540] [0.01006] [0.00295] [0.00829]

c_i10 0.00428 –0.00012 0.00275 0.00006 0.00197 0.00212 0.00625 0.00706^b 0.00885^b 0.03901^a 0.00542^a

[0.00480] [0.00435] [0.00369] [0.00707] [0.00267] [0.00277] [0.00549] [0.00314] [0.00382] [0.00577] [0.00492]

c_i11 0.00519 0.00561 0.00787^c 0.00119 0.00920^b 0.00117 0.01155 0.01307^a 0.01812^a 0.00709^b 0.04892

[0.00663] [0.00379] [0.00460] [0.00552] [0.00381] [0.00227] [0.00413] [0.00362] [0.00490] [0.00355] [0.00631]

a_i1 0.07443^a 0.05984^a 0.00523 0.03091 0.02472 0.02283 0.03674 0.02092 0.02953^b 0.04530^a 0.02893

[0.01310] [0.02159] [0.01184] [0.01481] [0.01709] [0.02349] [0.01912] [0.01821] [0.01205] [0.01219] [0.01973]

a_i2 0.06826^a 0.02533^a 0.02498 0.02083 0.03817 0.02193 0.03995 0.02984 0.05284^b 0.03980^a 0.03840

[0.01857^] [0.01707] [0.01984] [0.01835] [0.02084] [0.01472] [0.02849] [0.01893] [0.01492] [0.01955] [0.01835]

a_i3 0.00618 0.02325 0.01819^a 0.03809^a 0.03379^b 0.03245^b 0.03502^a 0.02527^a 0.05820^a 0.03348^a 0.01179

[0.01314] [0.02220] [0.00587] [0.01786] [0.01492] [0.01053] [0.01097] [0.00983] [0.00952] [0.00691] [0.00982]

a_i4 0.04585^a 0.12086^a 0.03756^a 0.16142^a 0.02892 0.02971 0.05908^a 0.05319^a 0.03818^a 0.03447^b 0.03814

[0.01592] [0.01948] [0.01393] [0.01824] [0.02109] [0.01915] [0.02021] [0.01720] [0.01417] [0.01591] [0.01905]

a_i5 0.02159 0.02060 0.01321^c 0.06277^a 0.04784^a 0.04503^a 0.03029^a 0.03418^b 0.02798^a 0.04081^a 0.03219

[0.01419] [0.02002] [0.00689] [0.02041] [0.00841] [0.00879] [0.01932] [0.01172] [0.00930] [0.00834] [0.01487]

a_i6 0.01562 0.02653 0.02271^b 0.02249 0.02708^a 0.02730^a 0.04810^a 0.03801^b 0.03854^a 0.01959^b 0.02129^a

[0.02225] [0.01973] [0.00957] [0.01739] [0.00982] [0.00758] [0.01034] [0.00925] [0.00981] [0.00899] [0.01139]

a_i7 0.05970^b 0.04830^c 0.04190^a 0.05885^a 0.03686^b 0.03701^a 0.08621^a 0.07399^a 0.05921^a 0.07022^a 0.03329^a

[0.02526] [0.02678] [0.01125] [0.02085] [0.01612] [0.01189] [0.01382] [0.01145] [0.01173] [0.01183] [0.01481]

a_i8 0.03329^b 0.03733^c 0.01987^a 0.04038^a 0.02371^b 0.02692^a 0.04308^a 0.04794^a 0.07520^a 0.05507^a 0.02182^a

[0.01480] [0.01945] [0.00650] [0.01335] [0.01036] [0.00690] [0.01069] [0.00742] [0.01098] [0.00849] [0.01157]

a_i9 0.01715 0.04231^b 0.01926^a 0.02226 0.01750^b 0.02880^a 0.04890^a 0.03776^a 0.03348^a 0.07572^a 0.05022^a

[0.01225] [0.01707] [0.00646] [0.01562] [0.00756] [0.00713] [0.01010] [0.00729] [0.00709] [0.00810] [0.00975]

a_i10 0.02247 0.01993 0.02016^a 0.03847^b 0.02046^a 0.02922^a 0.06063^a 0.03554^a 0.02603^a 0.03378^a 0.02980^a

[0.01452] [0.01848] [0.00658] [0.01550] [0.00783] [0.00681] [0.01291] [0.00642] [0.00707] [0.00650] [0.00918]

a_i11 0.03760 0.05032^a 0.02116^b 0.08807^a 0.03861^a 0.02441^a 0.07233^a 0.04425^a 0.05141^a 0.04680^a 0.07017^a

[0.02110] [0.01798] [0.00942] [0.01771] [0.01012] [0.00874] [0.01242] [0.00947] [0.00909] [0.00861] [0.01040]

g_i1 0.89025^a 0.84304^a 0.25541^a 0.18275^a 0.26291^a 0.09901^a 0.08291^a 0.05729^a 0.11934^a 0.05521^a 0.14322^a

[0.01434] [0.03082] [0.02021] [0.02885] [0.03120] [0.14892] [0.17809] [0.02995] [0.03498] [0.03905] [0.05408]

g_i2 0.81290^a 0.84247^a 0.16241^a 0.18522^a 0.21405^a 0.10077^a 0.07372^a 0.05923^a 0.10575^a 0.04084^a 0.12905^a

[0.02172] [0.01319] [0.07511] [0.03952] [0.04388] [0.07205] [0.08221] [0.07390] [0.03954] [0.08952] [0.03987]

g_i3 0.65621^a 0.59217^a 0.95817^a 0.74033^a 0.45411^a 0.32763^a 0.28734^a 0.34217^a 0.54670^a 0.12256^a 0.22493^a

[0.01967] [0.08592] [0.00623] [0.03893] [0.01981] [0.02759] [0.03125] [0.02193] [0.01875] [0.01925] [0.01741]

g_i4 0.20287^a 0.23506^a 0.52088^a 0.80007^a 0.28099^a 0.29217^a 0.37154^a 0.30134^a 0.58381^a 0.36797^a 0.33672^a

[0.03281] [0.03674] [0.03026] [0.01361] [0.03984] [0.06092] [0.03207] [0.02810] [0.03157] [0.03589] [0.02981]

g_i5 0.16301^a 0.10491^a 0.25411^a 0.18107^a 0.92392^a 0.21898^a 0.45843^a 0.31875^a 0.23295^a 0.13954^a 0.19365^a

[0.03065] [0.04000] [0.00990] [0.03732] [0.00921] [0.01923] [0.03172] [0.01427] [0.01889] [0.02914] [0.01981]

g_i6 0.42957^a 0.32061^a 0.52961^a 0.39016^a 0.41827^a 0.93469^a 0.81970^a 0.83232^a 0.75672^a 0.90138^a 0.93215^a

[0.12563] [0.04286] [0.01741] [0.06628] [0.01711] [0.00822] [0.01925] [0.01481] [0.01799] [0.00987] [0.01254]

g_i7 0.38272^a 0.37730^a 0.28577^a 0.37192^a 0.55711^a 0.71926^a 0.84236^a 0.89095^a 0.93711^a 0.86193^a 0.80418^a

[0.10831] [0.07237] [0.03814] [0.03172] [0.03922] [0.01520] [0.01570] [0.01461] [0.01825] [0.02259] [0.02352]

g_i8 0.19738^a 0.18987^a 0.34226^a 0.50106^a 0.71846^a 0.73279^a 0.78690^a 0.93239^a 0.82175^a 0.95207^a 0.91107^a

[0.03195] [0.04367] [0.01113] [0.02187] [0.01312] [0.00980] [0.01562] [0.00612] [0.00918] [0.01493] [0.01893]

g_i9 0.21948^a 0.30593^a 0.54688^a 0.70371^a 0.73282^a 0.82676^a 0.87744^a 0.80162^a 0.93312^a 0.83043^a 0.89279^a

[0.03553] [0.03669] [0.00829] [0.03441] [0.01180] [0.01069] [0.01902] [0.00715] [0.00705] [0.01732] [0.01104]

g_i10 0.18561^a 0.21064^a 0.74256^a 0.56770^a 0.83984^a 0.74139^a 0.79891^a 0.83297^a 0.90793^a 0.92885^a 0.84817^a

[0.04006] [0.04941] [0.01029] [0.03874] [0.00907] [0.00785] [0.02970] [0.00843] [0.00700] [0.00590] [0.01925]

g_i11 0.04355^a 0.07932^a 0.12465^a 0.03624^a 0.09362^a 0.34710^a 0.54421^a 0.70128^a 0.89975^a 0.79806^a 0.87883^a

[0.07418] [0.02706] [0.01478] [0.02520] [0.01866] [0.01143] [0.01856] [0.00995] [0.01104] [0.01204] [0.01017]

d_i1 0.00184 0.00571 0.01935 0.00931 0.01108 –0.01599 –0.00529 0.01852 –0.01540 0.00982 –0.01854

[0.01677] [0.02150] [0.01120] [0.02095] [0.01241] [0.02509] [0.03198] [0.01935] [0.01790] [0.02017] [0.02168]

d_i2 –0.00044 0.02809 0.02901 –0.01832 –0.03270 0.01109 –0.03099 0.01981 0.00420 0.02774 0.01457

[0.02676] [0.02215] [0.01954] [0.01883] [0.02199] [0.03091] [0.02910] [0.02057] [0.01815] [0.02877] [0.02188]

d_i3 0.02075 0.03428 0.01419^a 0.00217 0.02955^b 0.01117 –0.01722 0.01821^c 0.01902^c 0.02954^a 0.02502^b

[0.01488] [0.03334] [0.00602] [0.01488] [0.00528] [0.01291] [0.01981] [0.00981] [0.00854] [0.00881] [0.01440]

d_i4 –0.00501 –0.03879 –0.00109 0.01753 –0.01815 0.01544 0.02019 0.02102^c 0.04911^b 0.03981^b 0.03801

[0.02213] [0.03599] [0.01510] [0.02273] [0.02011] [0.03419] [0.02355] [0.01410] [0.02593] [0.02159] [0.02184]

d_i5 –0.01438 0.04118 0.02289^b –0.01939 0.01314 0.02371^c 0.02910 0.03981^a 0.02055^a 0.02188^a 0.02091^c

[0.01391] [0.02800] [0.00977] [0.02462] [0.01054] [0.01105] [0.02028] [0.01135] [0.01201] [0.01271] [0.01520]

d_i6 0.03835 0.01246 0.01339 0.01797 0.02269^c 0.02729^a 0.00452^c 0.03059^a –0.01484^b 0.01198^c 0.02712^b

[0.03175] [0.02461] [0.01040] [0.03050] [0.01242] [0.00956] [0.01091] [0.00901] [0.00908] [0.00903] [0.00919]

d_i7 –0.00638 –0.03344 –0.01028 0.02819 0.02341 0.00757 0.01198 0.02582^a 0.01872 0.00143 –0.02095

[0.03315] [0.03485] [0.01437] [0.02988] [0.02112] [0.01226] [0.01659] [0.01472] [0.01288] [0.01410] [0.01173]

d_i8 0.02159^c 0.01470 0.01401^c 0.02884^c 0.03548^c 0.02376^a 0.02957^a 0.00784 0.02095^b 0.01481 0.02589^a

[0.01709] [0.02351] [0.00764] [0.01659] [0.01275] [0.00878] [0.01381] [0.00791] [0.00779] [0.00957] [0.01290]

d_i9 0.02635 –0.00038^c 0.01682^b 0.04669^b 0.02860^c 0.01911^b 0.01395 0.01973^b 0.01678^a 0.02084^a 0.01922

[0.01822] [0.01946] [0.00719] [0.02249] [0.01017] [0.00839] [0.01249] [0.00824] [0.00810] [0.00721] [0.01904]

d_i10 0.00306 0.03455 0.02714^a 0.05625^b 0.02682 0.01382^c 0.00101 0.01298^c 0.02555^a 0.03561^a 0.03812^b

[0.01996] [0.02557] [0.00952] [0.02837] [0.01014] [0.00754] [0.01664] [0.00789] [0.00825] [0.00874] [0.01039]

d_i11 0.02391 0.01189 0.02270^b 0.03397 0.02262^c 0.02175^b 0.01583 0.02841^a 0.01530 0.02832^a 0.02451^a

[0.02805] [0.02307] [0.01115] [0.02091] [0.01249] [0.00958] [0.01401] [0.01457] [0.00985] [0.00984] [0.01183]

Half-Life 19.27668 21.17718 28.97175 17.64844 24.19317 17.88710 9.35301 34.88033 20.40065 18.19796 13.24179

LB-Q² (10) 14.61299 12.82018 15.90275 8.53421 12.22174 11.20915 9.74932 5.91854 7.57328 16.88925 11.64834

MLB-Q² (10) 984.89025

Source:Reuters Datastream.

Notes: ^a, ^b, ^c indicate statistical significance at 1%, 5% and 10% levels respectively. Standard errors are reported in brackets. LB-Q²(Ljung and Box Q-statistics on the squared residuals). M LB-Q² (Multivariate Ljung and Box Q-statistics on the squared residuals).

The ARCH parameters that measure own lagged innovations are statistically significant in Lebanon (0.16141), followed by Tunisia (0.12533), Bahrain (0.08621), Morocco (0.07442), Oman (0.07016), Qatar (0.04793), Jordan (0.04783), Saudi Arabia (0.03377), UAE (0.03347), Kuwait (0.02729) and Egypt (0.01819).

In terms of the magnitude of cross-markets innovation parameters, the results are presented in the following orders. Past innovations in Tunisia (0.05984) and Saudi Arabia (0.04530) spillover into the volatility in Morocco. Volatility co-movement among Tunisia and lagged innovations in Morocco (0.06826) and Saudi Arabia (0.03980) markets are statistically significant. Egyptian volatility depends upon past innovations in Lebanon (0.03809), Bahrain (0.03502), Qatar (0.02527), UAE (0.05820) and Saudi Arabia (0.03348). Past innovations in Morocco (0.04585), Tunisia (0.02086), Egypt (0.03756), Bahrain (0.05908), Qatar (0.05319) and UAE (0.03818) influence the volatility in Lebanon. Significant volatility transmits to Jordan from past innovations in Lebanon (0.06277), Kuwait (0.04503), Bahrain (0.03029), UAE (0.02798) and Saudi Arabia (0.04081). Kuwait receives volatility spillovers from lagged innovations in Jordan (0.02708), Bahrain (0.04810), UAE (0.03854) and Oman (0.02129). Past innovations in Egypt (0.04190), Lebanon (0.05885), Kuwait (0.03701), Qatar (0.07399), UAE (0.05921), Saudi Arabia (0.07022) and Oman (0.03329) influence the volatility in Bahrain. Qatari market receives volatility spillover from lagged innovations in Egypt (0.01987), Lebanon (0.04038), Kuwait (0.02692), Bahrain (0.04308), UAE (0.07520), Saudi Arabia (0.05507) and Oman (0.02182). UAE volatility market depends upon past innovations in Egypt (0.01926), Kuwait (0.02880), Bahrain (0.04890), Qatar (0.03776), Saudi Arabia (0.07572) and Oman (0.05022). Volatility spills over from past innovations in Egypt (0.02016), Jordan (0.02046), Kuwait (0.02922), Bahrain (0.06063), Qatar (0.03554), UAE (0.02603) and Oman (0.02980) into the Saudi Arabia market. Finally, volatility co-movement among Oman and the lagged innovations in Tunisia (0.05032), Lebanon (0.08807), Jordan (0.03861), Kuwait (0.02421), Bahrain (0.07233), Qatar (0.04425), UAE (0.05141) and Saudi Arabia (0.04680) markets are statistically significant.

The markets of Morocco and Tunisia transmit and receive the lowest scale of cross-innovations from the remaining MENA markets.

Conversely, Saudi Arabia and UAE exercise substantial influence on cross innovations in other markets probably due to their large equity market. Accordingly, the small markets of Oman and Bahrain receive substantial magnitude of cross-innovations from other markets.

Despite their large size, the Saudi and Kuwaiti markets did not transmit cross-innovations into Lebanon. The latter, one of the smallest MENA market, spills over significant cross-innovations into the relatively large market of Qatar. Equally, the small size of the Bahraini stock market did not prevent it from transmitting significant influence on the volatility of Kuwait, Qatar, UAE and Saudi Arabia.

However, the low degree of openness in the Saudi market could not explain the average measure of cross-innovations effects that spill over into it.

Analysis of Volatility Persistence

The diagonal parameters g_ij defined in matrix G measure the own lagged volatility persistence. On the other hand, the off-diagonal parameters g_ij in the same matrix measure the cross volatility persistence. The volatility persistent in any given market is considered to be high if its value is close to one. In measuring own volatility persistence in terms of conditional variance, the results reveal high own and cross volatility persistence across all markets.

The highest own lagged persistence is for Egypt (0.95816), followed by Kuwait (0.93469), UAE (0.93311), Qatar (0.93238), Saudi Arabia (0.92885), Jordan (0.92391), Morocco (0.89025), Oman (0.87883), Tunisia (0.84246), Bahrain (0.84235) and Lebanon (0.80006). This evidence of large and significant own volatility persistence indicates that all MENA stock markets sustain volatility for some time into the future.

Regarding the coefficients of cross-market volatility persistence, they are all large and significant. To compute the persistence of information shocks in days, we use the following formula that measure the period of time it takes a shock to diminish to one Half – life = ln(3)0.5/lnΩ

where ln symbolises the natural logarithm, and Ω denotes the sum of estimated ARCH and GARCH coefficients for each index.

The half-life value for each market is reported in Table 6. The highest duration of shock impact is for the Qatari market (34.88033), and the lowest is for the Bahraini market (9.35301). This may suggest that the Bahraini market exhibits more efficiency compared to other MENA markets as the effects of the shocks take a shorter time to decay.

Analysis of the Leverage Effect

The parameters in matrix D measure the leverage effect from market i to market j. The coefficients of the asymmetric response to bad news of own markets are statistically significant in Egypt, Kuwait, UAE, Saudi Arabia and Oman. Concerning cross-markets responses, the effects of negative shocks are asymmetric and bidirectional between Egypt and Saudi Arabia, UAE and Saudi Arabia, Kuwait and Qatar, Bahrain and Qatar, Oman and Qatar; similarly, the Jordanian market exhibits an asymmetric response to negative news from the markets of Qatar, UAE and Saudi Arabia. With all the statistically significant coefficients being positive, higher amount of good news is needed to compensate the negative effects of bad news on volatility spillovers across markets. The highest asymmetric effect is displayed between Qatar and Jordan (0.03548), while the lowest effect is revealed between Saudi Arabia and UAE (0.02555).

For all series, the Ljung–Box statistics indicate that the serial autocorrelation in squared residuals is insignificant up to 10 lags and point toward a random behaviour of the multivariate squared residuals.

Concluding Remarks

The main focus of this research is to examine the source and magnitude of mean and volatility spillovers across the stock markets of Morocco, Tunisia, Egypt, Lebanon, Jordan, Kuwait, Bahrain, Qatar, UAE, Saudi Arabia and Oman. As a preliminary step, the univariate statistics of our data imply that the returns of all indices are non-normally distributed and serially correlated with excessive presence of conditional heteroskedasticity. To benefit from this surplus of information in the residuals of the data, we rely on the existing literature to well specify a robust econometric model that can seize the time-varying conditional co-variances of returns across the MENA markets. To this end, we employ the MTGARCH methodology based on the work of BEKK defined in Engle and Kroner (1995).

We summarise the findings as follows. First, every MENA market return can be explained by its own lag with positive drift patterns. On the contrary, and despite the geographical proximity between several markets, we record independency in term of cross-mean returns which makes risk reduction possible. This weak form of size dominance and efficiency regarding information transmissions across these markets is broadly in line with previous results obtained by Lagoarde–Segot and Lucey (2008). Second, for all countries, the own lagged innovations are statistically significant. Lebanon and Tunisia exercise substantial influence on their own innovations. Across most of the series, we find significant impact in cross-innovations spillovers that differ probably according to the level of capital market liberalisation. In particular, the markets of Kuwait, Bahrain, Qatar, UAE, Saudi Arabia and Oman are found regionally integrated. This is not surprising given the geographical proximity and the high level of economic ties across these markets that eliminate the possibility of diversification at the regional level. Such observations are consistent with earlier empirical results (Assaf 2003; Neaime and Colton 2005). Conversely, low levels of cross-innovations exist particularly between Morocco and Tunisia, and the remaining MENA countries. This finding is plausible given the weak trade relations and geographical detachment of the two markets from the remaining studied MENA countries. However, the high level of cross-innovations transmission from Bahrain and Lebanon to larger markets is somewhat surprising. This important and unusual form of domination from small to relatively larger markets contradicts previous researches (Janakiramanan and Lamba 1998; Wang and Firth 2004). Third, the volatility persistence affects the conditional variance of all indices. However, own volatility persistence is higher than cross volatility persistence; this outcome indicates that volatility in each market will be more influenced by its own past conditional variance than by the affect of cross shocks spillovers from other markets. Fourth, new evidence reveals that seven of the studied MENA markets display positive asymmetric information pattern that implies a stronger response to bad news.

From an asset allocation and risk prediction perspective, our results bring the dynamics and the degree of volatility transmissions across the MENA markets to light. The inclusion of significant parameters of cross spillovers will improve the accuracy of forecasting the next period change in second moment of return. Policy makers, regulators and risk managers can profit from our results in their quest to capture the transmission of volatility shocks across the MENA equity markets.

We suggest for future researches the examination of volatility co-movement by including regime switching between periods of high and low volatility.

	Morocco	Tunisia	Egypt	Lebanon	Jordan	Kuwait	Bahrain	Qatar	UAE	Saudi	Oman
ADF	−33.32210^a	−24.55917^a	−26.97941^a	−12.64030^a	−48.20582^a	−8.52152^a	−6.64045^a	−7.88110^a	−25.91963^a	−23.74027^a	−7.40954^a
PP	−41.86495^a	−39.46318^a	−46.34183^a	−45.01022^a	−48.26880^a	−40.37385^a	−39.29020^a	−38.74051^a	−36.53521^a	−48.53145^a	−37.77518^a
Lag	1	2	2	12	0	14	19	24	1	3	19

Country	Morocco(i = 1)	Tunisia(i = 2)	Egypt(i = 3)	Lebanon(i = 4)	Jordan(i = 5)	Kuwait(i = 6)	Bahrain(i = 7)	Qatar(i = 8)	UAE(i = 9)	Saudi(i = 10)	Oman(i = 11)
φ	0.04364^b	0.01824	0.07371^b	–0.00244	–0.02386	0.05394^c	–0.02782	0.06119^b	0.01774	0.01577	0.01632
	[0.01037]	[0.01560]	[0.02730]	[0.02164]	[0.02464]	[0.02850]	[0.02418]	[0.02767]	[0.03386]	[0.00195]	[0.02097]
m_i1	0.08129^a	0.09695	–0.03019	0.00369	0.00819	0.02195	0.01543	0.00809	0.00721	0.00993	0.00438
	[0.00195]	[0.00378]	[0.00305]	[0.00495]	[0.00502]	[0.00107]	[0.00633]	[0.00394]	[0.00233]	[0.00789]	[0.00872]
m_i2	–0.05593	0.11327^a	–0.05004	–0.02593	0.02395	0.02775	0.00393	0.02259	0.01621	0.01693	0.00728
	[0.00429]	[0.09542]	[0.00721]	[0.00208]	[0.00514]	[0.00270]	[00.00175]	[0.00102]	[0.00599]	[0.00775]	[0.00625]
m_i3	–0.04491	0.05769	0.16834^a	0.05919	0.03174	0.01355	0.02243	0.01709	0.03721	0.02993	0.01018
	[0.00301]	[0.00713]	[0.00398]	[0.00595]	[0.00793]	[0.00598]	[0.00112]	[0.00188]	[0.00574]	[0.00891]	[0.00687]
m_i4	0.00382	0.00806	0.00721	0.11779^a	0.00989	–0.00775	–0.01393	0.02159	–0.03179	0.02293	0.01308
	[0.00184]	[0.00407]	[0.00603]	[0.00280]	[0.00198]	[0.00380]	[0.00651]	[0.00775]	[0.00834]	[0.00534]	[0.00205]
m_i5	0.00595	–0.00781	0.00584	–0.05068	0.08057^a	0.02195	0.04543	0.02609	–0.01079	–0.02407	0.03598
	[0.00285]	[0.00418]	[0.00769]	[0.00579]	[0.00093]	[0.00056]	[0.00854]	[0.00579]	[0.00402]	[0.00613]	[0.00789]
m_i6	0.05938	0.08693	0.09128	0.05758	0.05149	0.16877^a	–0.08037	0.01759	–0.02629	–0.01307	0.03898
	[0.00512]	[0.00292]	[0.00137]	[0.00921]	[0.00869]	[0.00291]	[0.00318]	[0.00790]	[0.00261]	[0.00432]	[0.00491]
m_i7	–0.02718	0.01850	0.08297	–0.03351	–0.04398	0.04855	0.20941^a	–0.07741	0.03271	–0.02107	0.07357
	[0.00689]	[0.00195]	[0.00253]	[0.00463]	[0.00297]	[0.00421]	[0.00286]	[0.00139]	[0.00188]	[0.00816]	[0.00159]
m_i8	0.04289	0.07741	–0.05181	0.02719	0.02574	0.03305	0.05041	0.19083^a	–0.07729	–0.05807	0.07949
	[0.00138]	[0.00875]	[0.00492]	[0.00151]	[0.00720]	[0.00872]	[0.00184]	[0.00938]	[0.00409]	[0.00487]	[0.00338]
m_i9	0.02795	0.03538	0.05985	0.09068	0.08181	0.02885	–0.04191	0.06581	0.17595^a	0.05107	0.08241
	[0.00308]	[0.00919]	[0.00273]	[0.00801]	[0.00795]	[0.00964]	[0.00299]	[0.00731]	[0.00591]	[0.00125]	[0.00136]
m_i10	–0.07039	0.09791	–0.05983	0.09189	0.07381	–0.05305	–0.08041	–0.06131	0.08708	0.17792^a	0.04109
	[0.00092]	[0.00903]	[0.00354]	[0.00167]	[0.00684]	[0.00115]	[0.00581]	[0.00791]	[0.00828]	[0.00943]	[0.00151]
m_i11	0.01331	0.02195	0.01499	–0.02237	0.03714	–0.01725	–0.01541	–0.05631	0.07308	0.05691	0.14504^a
	[0.00967]	[0.00134]	[0.00213]	[0.00390]	[0.00742]	[0.00962]	[0.00321]	[0.00881]	[0.00635]	0.00464]	[0.00381]

Country	Morocco(i = 1)	Tunisia(i = 2)	Egypt(i = 3)	Lebanon(i = 4)	Jordan(i = 5)	Kuwait(i = 6)	Bahrain(i = 7)	Qatar(i = 8)	UAE(i = 9)	Saudi(i = 10)	Oman(i = 11)
c_i1	0.08212^a	0.02502	0.00399	0.00491	0.00527	0.00750	0.00539	0.00350	0.00706	0.00295	0.00345
	[0.01645]	[0.00905]	[0.00718]	[0.00720]	[0.00509]	[0.00957]	[0.00812]	[0.00439]	[0.00820]	[0.00731]	[0.00397]
c_i2	0.02268^a	0.06273^a	0.00270	0.00581	0.00097	0.00391	0.00309	–0.00174	0.00548	–0.00059	0.00720
	[0.00797]	[0.00923]	[0.00793	[0.00523]	[0.00349]	[0.00710]	[0.00550]	[0.00492]	[0.00730]	[0.00291]	[0.00522]
c_i3	0.00575	0.00313	0.04695^a	0.00723	0.00905	0.00592	0.01239	0.01288^b	0.01821	0.00359	0.00597^c
	[0.00526]	[0.00834]	[0.01067]	[0.00522]	[0.00297]	[0.00419]	[0.00829]	[0.00499]	[0.00392]	[0.00295]	[0.00532]
c_i4	0.00670	0.00759	0.00793	0.12922^a	0.00719	0.00517	0.00310	0.00175	0.00509	0.00088	0.00175^a
	[0.00441]	[0.00610]	[0.00603]	[0.01486]	[0.00544]	[0.00552]	[0.00782]	[0.00729]	[0.00739]	[0.00832]	[0.00589]
c_i5	0.00398	0.00001	0.00349	0.00878	0.03508^a	0.00691	0.00905	0.00337	0.00399^b	0.00295	0.00851
	[0.00354]	[0.00391]	[0.00300]	[0.00622]	[0.00693]	[0.00341]	[0.00571]	[0.00209]	[0.00581]	[0.00360]	[0.00371]
c_i6	0.00849	0.00247	0.00711	0.00468	0.00589	0.04095^a	0.01099^b	0.00412^c	0.01055	0.00352	0.00194
	[0.01238]	[0.00436]	[0.00526]	[0.00682]	[0.00386]	[0.00789]	[0.00293]	[0.00297]	[0.00285]	[0.00435]	[0.00392]
c_i7	0.00657	0.00383	0.01015	0.00219	0.00869	0.01071^a	0.08299^a	0.01261^b	0.01034^c	0.00710	0.01207^a
	[0.00902]	[0.00464]	[0.00688]	[0.00570]	[0.00564]	[0.00389]	[0.01107]	[0.00824]	[0.00719]	[0.00950]	[0.00499]
c_i8	0.00438	–0.00093	0.01361^a	0.00142	0.00526	0.00584^c	0.01095^b	0.02939^a	0.02178^a	0.00548^b	0.01163^b
	[0.00556]	[0.00450]	[0.00511]	[0.00457]	[0.00389]	[0.00316]	[0.00456]	[0.00486]	[0.00490]	[0.00419]	[0.00399]
c_i9	0.00546	0.00660	0.01443^a	0.00469	0.00832^b	0.01219^a	0.01940^a	0.02510^a	0.06890	0.00772	0.01045
	[0.00640]	[0.00572]	[0.00525]	[0.00744]	[0.00406]	[0.00452]	[0.00644]	[0.00540]	[0.01006]	[0.00295]	[0.00829]
c_i10	0.00428	–0.00012	0.00275	0.00006	0.00197	0.00212	0.00625	0.00706^b	0.00885^b	0.03901^a	0.00542^a
	[0.00480]	[0.00435]	[0.00369]	[0.00707]	[0.00267]	[0.00277]	[0.00549]	[0.00314]	[0.00382]	[0.00577]	[0.00492]
c_i11	0.00519	0.00561	0.00787^c	0.00119	0.00920^b	0.00117	0.01155	0.01307^a	0.01812^a	0.00709^b	0.04892
	[0.00663]	[0.00379]	[0.00460]	[0.00552]	[0.00381]	[0.00227]	[0.00413]	[0.00362]	[0.00490]	[0.00355]	[0.00631]
a_i1	0.07443^a	0.05984^a	0.00523	0.03091	0.02472	0.02283	0.03674	0.02092	0.02953^b	0.04530^a	0.02893
	[0.01310]	[0.02159]	[0.01184]	[0.01481]	[0.01709]	[0.02349]	[0.01912]	[0.01821]	[0.01205]	[0.01219]	[0.01973]
a_i2	0.06826^a	0.02533^a	0.02498	0.02083	0.03817	0.02193	0.03995	0.02984	0.05284^b	0.03980^a	0.03840
	[0.01857^]	[0.01707]	[0.01984]	[0.01835]	[0.02084]	[0.01472]	[0.02849]	[0.01893]	[0.01492]	[0.01955]	[0.01835]
a_i3	0.00618	0.02325	0.01819^a	0.03809^a	0.03379^b	0.03245^b	0.03502^a	0.02527^a	0.05820^a	0.03348^a	0.01179
	[0.01314]	[0.02220]	[0.00587]	[0.01786]	[0.01492]	[0.01053]	[0.01097]	[0.00983]	[0.00952]	[0.00691]	[0.00982]
a_i4	0.04585^a	0.12086^a	0.03756^a	0.16142^a	0.02892	0.02971	0.05908^a	0.05319^a	0.03818^a	0.03447^b	0.03814
	[0.01592]	[0.01948]	[0.01393]	[0.01824]	[0.02109]	[0.01915]	[0.02021]	[0.01720]	[0.01417]	[0.01591]	[0.01905]
a_i5	0.02159	0.02060	0.01321^c	0.06277^a	0.04784^a	0.04503^a	0.03029^a	0.03418^b	0.02798^a	0.04081^a	0.03219
	[0.01419]	[0.02002]	[0.00689]	[0.02041]	[0.00841]	[0.00879]	[0.01932]	[0.01172]	[0.00930]	[0.00834]	[0.01487]
a_i6	0.01562	0.02653	0.02271^b	0.02249	0.02708^a	0.02730^a	0.04810^a	0.03801^b	0.03854^a	0.01959^b	0.02129^a
	[0.02225]	[0.01973]	[0.00957]	[0.01739]	[0.00982]	[0.00758]	[0.01034]	[0.00925]	[0.00981]	[0.00899]	[0.01139]
a_i7	0.05970^b	0.04830^c	0.04190^a	0.05885^a	0.03686^b	0.03701^a	0.08621^a	0.07399^a	0.05921^a	0.07022^a	0.03329^a
	[0.02526]	[0.02678]	[0.01125]	[0.02085]	[0.01612]	[0.01189]	[0.01382]	[0.01145]	[0.01173]	[0.01183]	[0.01481]
a_i8	0.03329^b	0.03733^c	0.01987^a	0.04038^a	0.02371^b	0.02692^a	0.04308^a	0.04794^a	0.07520^a	0.05507^a	0.02182^a
	[0.01480]	[0.01945]	[0.00650]	[0.01335]	[0.01036]	[0.00690]	[0.01069]	[0.00742]	[0.01098]	[0.00849]	[0.01157]
a_i9	0.01715	0.04231^b	0.01926^a	0.02226	0.01750^b	0.02880^a	0.04890^a	0.03776^a	0.03348^a	0.07572^a	0.05022^a
	[0.01225]	[0.01707]	[0.00646]	[0.01562]	[0.00756]	[0.00713]	[0.01010]	[0.00729]	[0.00709]	[0.00810]	[0.00975]
a_i10	0.02247	0.01993	0.02016^a	0.03847^b	0.02046^a	0.02922^a	0.06063^a	0.03554^a	0.02603^a	0.03378^a	0.02980^a
	[0.01452]	[0.01848]	[0.00658]	[0.01550]	[0.00783]	[0.00681]	[0.01291]	[0.00642]	[0.00707]	[0.00650]	[0.00918]
a_i11	0.03760	0.05032^a	0.02116^b	0.08807^a	0.03861^a	0.02441^a	0.07233^a	0.04425^a	0.05141^a	0.04680^a	0.07017^a
	[0.02110]	[0.01798]	[0.00942]	[0.01771]	[0.01012]	[0.00874]	[0.01242]	[0.00947]	[0.00909]	[0.00861]	[0.01040]
g_i1	0.89025^a	0.84304^a	0.25541^a	0.18275^a	0.26291^a	0.09901^a	0.08291^a	0.05729^a	0.11934^a	0.05521^a	0.14322^a
	[0.01434]	[0.03082]	[0.02021]	[0.02885]	[0.03120]	[0.14892]	[0.17809]	[0.02995]	[0.03498]	[0.03905]	[0.05408]
g_i2	0.81290^a	0.84247^a	0.16241^a	0.18522^a	0.21405^a	0.10077^a	0.07372^a	0.05923^a	0.10575^a	0.04084^a	0.12905^a
	[0.02172]	[0.01319]	[0.07511]	[0.03952]	[0.04388]	[0.07205]	[0.08221]	[0.07390]	[0.03954]	[0.08952]	[0.03987]
g_i3	0.65621^a	0.59217^a	0.95817^a	0.74033^a	0.45411^a	0.32763^a	0.28734^a	0.34217^a	0.54670^a	0.12256^a	0.22493^a
	[0.01967]	[0.08592]	[0.00623]	[0.03893]	[0.01981]	[0.02759]	[0.03125]	[0.02193]	[0.01875]	[0.01925]	[0.01741]
g_i4	0.20287^a	0.23506^a	0.52088^a	0.80007^a	0.28099^a	0.29217^a	0.37154^a	0.30134^a	0.58381^a	0.36797^a	0.33672^a
	[0.03281]	[0.03674]	[0.03026]	[0.01361]	[0.03984]	[0.06092]	[0.03207]	[0.02810]	[0.03157]	[0.03589]	[0.02981]
g_i5	0.16301^a	0.10491^a	0.25411^a	0.18107^a	0.92392^a	0.21898^a	0.45843^a	0.31875^a	0.23295^a	0.13954^a	0.19365^a
	[0.03065]	[0.04000]	[0.00990]	[0.03732]	[0.00921]	[0.01923]	[0.03172]	[0.01427]	[0.01889]	[0.02914]	[0.01981]
g_i6	0.42957^a	0.32061^a	0.52961^a	0.39016^a	0.41827^a	0.93469^a	0.81970^a	0.83232^a	0.75672^a	0.90138^a	0.93215^a
	[0.12563]	[0.04286]	[0.01741]	[0.06628]	[0.01711]	[0.00822]	[0.01925]	[0.01481]	[0.01799]	[0.00987]	[0.01254]
g_i7	0.38272^a	0.37730^a	0.28577^a	0.37192^a	0.55711^a	0.71926^a	0.84236^a	0.89095^a	0.93711^a	0.86193^a	0.80418^a
	[0.10831]	[0.07237]	[0.03814]	[0.03172]	[0.03922]	[0.01520]	[0.01570]	[0.01461]	[0.01825]	[0.02259]	[0.02352]
g_i8	0.19738^a	0.18987^a	0.34226^a	0.50106^a	0.71846^a	0.73279^a	0.78690^a	0.93239^a	0.82175^a	0.95207^a	0.91107^a
	[0.03195]	[0.04367]	[0.01113]	[0.02187]	[0.01312]	[0.00980]	[0.01562]	[0.00612]	[0.00918]	[0.01493]	[0.01893]
g_i9	0.21948^a	0.30593^a	0.54688^a	0.70371^a	0.73282^a	0.82676^a	0.87744^a	0.80162^a	0.93312^a	0.83043^a	0.89279^a
	[0.03553]	[0.03669]	[0.00829]	[0.03441]	[0.01180]	[0.01069]	[0.01902]	[0.00715]	[0.00705]	[0.01732]	[0.01104]
g_i10	0.18561^a	0.21064^a	0.74256^a	0.56770^a	0.83984^a	0.74139^a	0.79891^a	0.83297^a	0.90793^a	0.92885^a	0.84817^a
	[0.04006]	[0.04941]	[0.01029]	[0.03874]	[0.00907]	[0.00785]	[0.02970]	[0.00843]	[0.00700]	[0.00590]	[0.01925]
g_i11	0.04355^a	0.07932^a	0.12465^a	0.03624^a	0.09362^a	0.34710^a	0.54421^a	0.70128^a	0.89975^a	0.79806^a	0.87883^a
	[0.07418]	[0.02706]	[0.01478]	[0.02520]	[0.01866]	[0.01143]	[0.01856]	[0.00995]	[0.01104]	[0.01204]	[0.01017]
d_i1	0.00184	0.00571	0.01935	0.00931	0.01108	–0.01599	–0.00529	0.01852	–0.01540	0.00982	–0.01854
	[0.01677]	[0.02150]	[0.01120]	[0.02095]	[0.01241]	[0.02509]	[0.03198]	[0.01935]	[0.01790]	[0.02017]	[0.02168]
d_i2	–0.00044	0.02809	0.02901	–0.01832	–0.03270	0.01109	–0.03099	0.01981	0.00420	0.02774	0.01457
	[0.02676]	[0.02215]	[0.01954]	[0.01883]	[0.02199]	[0.03091]	[0.02910]	[0.02057]	[0.01815]	[0.02877]	[0.02188]
d_i3	0.02075	0.03428	0.01419^a	0.00217	0.02955^b	0.01117	–0.01722	0.01821^c	0.01902^c	0.02954^a	0.02502^b
	[0.01488]	[0.03334]	[0.00602]	[0.01488]	[0.00528]	[0.01291]	[0.01981]	[0.00981]	[0.00854]	[0.00881]	[0.01440]
d_i4	–0.00501	–0.03879	–0.00109	0.01753	–0.01815	0.01544	0.02019	0.02102^c	0.04911^b	0.03981^b	0.03801
	[0.02213]	[0.03599]	[0.01510]	[0.02273]	[0.02011]	[0.03419]	[0.02355]	[0.01410]	[0.02593]	[0.02159]	[0.02184]
d_i5	–0.01438	0.04118	0.02289^b	–0.01939	0.01314	0.02371^c	0.02910	0.03981^a	0.02055^a	0.02188^a	0.02091^c
	[0.01391]	[0.02800]	[0.00977]	[0.02462]	[0.01054]	[0.01105]	[0.02028]	[0.01135]	[0.01201]	[0.01271]	[0.01520]
d_i6	0.03835	0.01246	0.01339	0.01797	0.02269^c	0.02729^a	0.00452^c	0.03059^a	–0.01484^b	0.01198^c	0.02712^b
	[0.03175]	[0.02461]	[0.01040]	[0.03050]	[0.01242]	[0.00956]	[0.01091]	[0.00901]	[0.00908]	[0.00903]	[0.00919]
d_i7	–0.00638	–0.03344	–0.01028	0.02819	0.02341	0.00757	0.01198	0.02582^a	0.01872	0.00143	–0.02095
	[0.03315]	[0.03485]	[0.01437]	[0.02988]	[0.02112]	[0.01226]	[0.01659]	[0.01472]	[0.01288]	[0.01410]	[0.01173]
d_i8	0.02159^c	0.01470	0.01401^c	0.02884^c	0.03548^c	0.02376^a	0.02957^a	0.00784	0.02095^b	0.01481	0.02589^a
	[0.01709]	[0.02351]	[0.00764]	[0.01659]	[0.01275]	[0.00878]	[0.01381]	[0.00791]	[0.00779]	[0.00957]	[0.01290]
d_i9	0.02635	–0.00038^c	0.01682^b	0.04669^b	0.02860^c	0.01911^b	0.01395	0.01973^b	0.01678^a	0.02084^a	0.01922
	[0.01822]	[0.01946]	[0.00719]	[0.02249]	[0.01017]	[0.00839]	[0.01249]	[0.00824]	[0.00810]	[0.00721]	[0.01904]
d_i10	0.00306	0.03455	0.02714^a	0.05625^b	0.02682	0.01382^c	0.00101	0.01298^c	0.02555^a	0.03561^a	0.03812^b
	[0.01996]	[0.02557]	[0.00952]	[0.02837]	[0.01014]	[0.00754]	[0.01664]	[0.00789]	[0.00825]	[0.00874]	[0.01039]
d_i11	0.02391	0.01189	0.02270^b	0.03397	0.02262^c	0.02175^b	0.01583	0.02841^a	0.01530	0.02832^a	0.02451^a
	[0.02805]	[0.02307]	[0.01115]	[0.02091]	[0.01249]	[0.00958]	[0.01401]	[0.01457]	[0.00985]	[0.00984]	[0.01183]
Half-Life	19.27668	21.17718	28.97175	17.64844	24.19317	17.88710	9.35301	34.88033	20.40065	18.19796	13.24179
LB-Q² (10)	14.61299	12.82018	15.90275	8.53421	12.22174	11.20915	9.74932	5.91854	7.57328	16.88925	11.64834
MLB-Q² (10)	984.89025

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