Emerging Markets Review

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1 Emerging Markets Review 13 (2012) Contents lists available at SciVerse ScienceDirect Emerging Markets Review journal homepage: The global financial crisis, financial linkages and correlations in returns and volatilities in emerging MENA stock markets Simon Neaime Department of Economics, American University of Beirut, Beirut, Lebanon article info abstract Available online 8 February 2012 JEL classification: C320 G110 G150 Keywords: Financial crisis Financial integration and contagion MENA region The 2007 United States financial crisis has developed into the most severe worldwide economic crisis since the 1927 Great Depression. In addition to its severe repercussion in North America and the European Union, the crisis has put pressure on emerging markets in general, and the Middle East and North Africa region in particular. For a better understanding of how the crisis affected the MENA region, we focus in this paper on the global and regional financial linkages between MENA stock markets and the more developed financial markets, and on the intra-regional financial linkages between MENA countries' financial markets Elsevier B.V. All rights reserved. 1. Introduction and overview The 2007 United States (US) financial crisis, which started in the subprime market, has developed into the most severe worldwide economic crisis since the 1927 Great Depression. What has prevented an even more important fallout was the introduction of a spectacular fiscal and monetary stimulus package in the more advanced economies. In addition to its severe repercussion in North America and the European Union (EU), the crisis has put pressure on emerging markets in general, and the Middle East and North Africa (MENA) region in particular, contributing to fast declines in their stock markets, and GDP growth rates. For a better understanding of how the recent financial crisis affected the MENA region, we focus An earlier version of this manuscript was presented at a conference on Institutional, Corporate, and Individual Behaviors in Emerging and Subsistence Marketplaces Aix-en-Provence, France, 1 2 September, The author is grateful to the editor of the journal, two anonymous referees and several conference participants for very constructive comments and suggestions. The author is also grateful to Mario Rached for superb research assistance. Financial support from the Institute of Financial Economics of the American University of Beirut is gratefully acknowledged. Tel.: ; fax: address: sn01@aub.edu.lb /$ see front matter 2012 Elsevier B.V. All rights reserved. doi: /j.ememar

2 S. Neaime / Emerging Markets Review 13 (2012) in this paper on the global and regional financial linkages between MENA stock markets 1 and the more mature markets of the US and EU, and on the intra-regional financial linkages between the oil and nonoil producing MENA countries' financial markets. To this end we focus on the dynamic relationships in the volatilities of the returns in MENA stock markets. In addition the causality in variances GARCH model, the Threshold ARCH and ARCH-M models, and VAR analysis are also used to model conditional volatilities in stock market returns. The spillover effects of the recent global financial crisis on MENA countries and its effects on their stock markets varied according to their degree of financial integration with the more mature financial markets. Given their strong linkages with global stock markets, the stock markets of Egypt, Jordan, Kuwait, Morocco, and the United Arab Emirates (UAE) were the most affected by the global financial crisis, with insignificant impacts on Saudi Arabia, which was not that affected by the crisis due to the fact that its financial market has remained relatively closed to foreign investors and its degree of financial integration has remained low. The Saudi stock market's financial linkages with the global equity markets are still insignificant, despite recent efforts to further enhance its intra-regional integration with the remaining MENA countries' stock markets. Egypt, Jordan, Morocco, and the UAE with large exposure to EU/US banks and equity markets, were the first to suffer. These countries have faced a four-edged sword, i.e. plunging asset prices, higher cost of capital, a slowdown in capital inflows and a decrease in exports. Other MENA countries, namely Tunisia and Saudi Arabia have become more resilient with respect to past crises, owing to the built up of adequate foreign exchange reserves, and robust fiscal stance. Private capital and portfolio flows to the MENA region have remained relatively limited. While intraregional capital flows between the GCC financial markets increased significantly in recent years, they remained negligible with the non-oil producing MENA countries. Cross border investments have been made mainly in those MENA countries that have implemented policies conducive to strengthening the operational framework of the domestic financial market, namely Egypt, Jordan, and the UAE. It should also be noted that MENA's capital markets have traditionally been less important in channeling capital flows. A fairly developed commercial banking system has taken the lead in attracting and distributing capital, and in stimulating portfolio investments in the MENA region. Relative to $78.5 billion in 2008, FDI inflows to the MENA region declined to $68.4 billion in The MENA region's share in global FDI inflows has remained low relative to other emerging regions worldwide. MENA's share in total FDI inflows has remained at 5% in While South, East, and South East Asia ranked first in terms of FDI inflows with a 21% share, followed by Latin America and the Caribbean region with a 10% share, Southeast Europe and the Commonwealth of Independent States (CIS) with a 6% share (UNCTAD, 2010). Compared to other emerging market economies, MENA's attractiveness to international investors has been quite modest before the crisis, even in the better performing MENA countries of Saudi Arabia, the UAE, Jordan, Tunisia and Egypt. Some MENA countries, namely Egypt, Tunisia and Jordan have recently liberalized more than others investment regulation, removed ownership restrictions as well as trade and capital flow barriers. The availability of adequate and well organized institutions can reduce investment transaction costs, turning projects more profitable. Since FDI flows to MENA countries may involve large sunk costs, they become very sensitive to current instability and the lack of security in several MENA countries, as was more recently the case during the social unrest episodes experienced in each of Tunisia and Egypt. Based on the stylized facts, MENA countries' decrease in FDI inflows is related to either internal institutional factors and social and political turmoil, or to the global financial flows conditions emanating from the financial crisis. Some MENA countries are now faced with tighter international capital markets and a drying up of external financing, as the European sovereign debt crisis is still unfolding coupled with social internal unrests further curtailed international investors' interest in these economies. If these declining trends continue in the foreseeable future, some MENA countries mainly Egypt, Tunisia, and Lebanon due the neighborhood effects spilling over as a result of the Syrian crisis may be deprived from their main growth engine, which may also translate into more pressure on the respective banking systems and 1 In this paper the oil producing MENA countries or Gulf Cooperation Council (GCC) Countries are Kuwait, Saudi Arabia and the United Arab Emirates, while the non-oil producing MENA countries are: Egypt, Jordan, Morocco, and Tunisia.

3 270 S. Neaime / Emerging Markets Review 13 (2012) Table 1 Stock market capitalization in MENA Countries, Source: Arab Monetary Fund, Joint Arab Economic Report, various issues, and Federation of Euro/Asian Stock Exchange ( feas.org). Market capitalization (USD million) Country/year Egypt 79,672 93, ,289 85,885 89,953 82,495 Jordan 37,639 29,729 41,216 35,847 31,865 30,864 Kuwait 130,80 128, , ,168 95, ,621 Morocco 27,220 49,360 75,495 65,748 62,910 69,153 Saudi Arabia 646, , , , , ,414 Tunisia ,682 UAE 225, , ,675 97, , ,669 Total MENA region 1,019, ,352 1,189, , , ,898 financial markets. These declining trends may jeopardize the recent integration efforts of MENA countries through a widening of the income gap between them and the more developed economies. With the possible exception of Egypt, MENA countries' equity markets have only come to the fore in the 1990s. Despite their small market capitalization, during the past 10 years, MENA countries' equity markets have exhibited performance characteristics parallel to other emerging markets in similar stages of financial development. Record market capitalization growth rates can be noted in Egypt and Jordan, and to a lesser extent in Kuwait, over the period (Table 1) prior to the financial crisis. This is due to massive privatization schemes introduced in those countries, and to the extensive sale of government assets to private firms, and to the considerable efforts devoted recently in enhancing the efficiency, depth, integration, and liquidity of the three stock markets. Moreover, the recent open access to foreign investors to almost all non-oil producing MENA stock markets has contributed significantly to the growth performances of those countries stock market capitalization. However, stock market capitalizations in Egypt and Jordan declined significantly in between 2007 and 2009 as a result of the global financial crisis, from $139.3 and $41.3 billion in 2007, to $89.95 and $31.86 billion respectively in 2009 (Table 1). Among the non-oil producing MENA countries, Table 1 indicates that Morocco experienced a milder stock market capitalization decline from $75.5 billion in 2007 to $62.9, while Tunisia stands alone in experiencing an improvement in its market capitalization from $5.3 billion to $9.1 billion in between 2007 and Stock market capitalization in the oil producing MENA countries has also declined significantly in between 2007 and 2009 as a result of the global financial crisis. In the UAE, it went down from $224.6 billion in 2007, to $109.6 billion in 2009 a decline of about 100% similarly in Kuwait stock market capitalization went down from $188 billion in 2007 to $95.9 billion in However, the decline in Saudi Arabia's stock market capitalization was milder during and after the global financial crisis, decreasing from $515 billion in 2007, to $318 billion in 2009; a decrease of about 60% (see Table 1). In this paper, the transmission channels of the global financial crisis on the MENA region will be carefully explored and identified. We will further elaborate on how economic integration within the MENA region could have helped in dampening the negative transmission effects of those shocks into the region. This includes fundamental contagion effects arising through the financial and goods markets. Although MENA's financial integration is still relatively weak among countries in the region, it has significantly improved over the past few years. This explains how financial shocks in one GCC stock market were swiftly propagated into the remaining MENA capital markets. Also shocks disseminate through intra and inter-regional trade agreements. 2 Inter-regional trade agreements, namely the EU-Mediterranean Trade Agreements, as well as intra-regional trade agreements between MENA countries such as the Greater Arab Free Trade Area (GAFTA), and the GCC trade agreements have potentially played a crucial role in determining the size of the spillovers. 2 Glick and Rose (1998), find that currency crises tend to spread along the lines of trade linkages making them more likely to become regional, since most countries tend to export and import with countries in geographic proximity.

4 S. Neaime / Emerging Markets Review 13 (2012) But they did not act as a catalyst for higher spillovers per se, since some MENA countries still have low degrees of trade and financial integration, and the magnitude of intra MENA trade has remained significantly low despite the recent efforts devoted toward enhancing regional trade. Insights on the quantitative dimension of the transmission channels are very scarce and may contribute to a better design of macroeconomic policy management. For example, they might be helpful in determining countercyclical and coordinated policy measures such as the extent of the fiscal stimulus, the size of interest rate cuts and real exchange rate changes required to maintain stability in the instance of a new financial crisis erupting, since a double dip recession still looms at the horizon. These issues will be dealt with in detail in the following sections. The empirical evidence we document on the causal and dynamic temporal relations in returns and volatilities among the MENA countries will also have important implications for policy makers. For instance, it will give policy makers the opportunity to preempt an impending financial crisis arising from an outside source, hence making it less likely for policy makers to have to resort to painful economic measures similar to those adopted during the recent global financial crisis in the UAE after the fact to resolve the crisis. The rest of the paper is divided as follows. The next section reviews related literature. Section 3 highlights the data and sample and provides some preliminary statistical analysis. Section 4 lays down the empirical methodology and the empirical findings of the TARCH, GARCH and ARCH-M models. Section 4 then uses the conditional volatilities obtained from the best fit to estimate a VAR model and causality patters across the sample of the 10 stock markets under investigation. Section 5 concludes the paper with some policy implications. 2. Related literature The empirical finance literature was mostly concerned with the financial integration of the world major stock markets (see for example, Arshanapalli and Doukas, 1993; Eun and Shim, 1989; Joen and Von Furstenberg, 1990; Kasa, 1992; Kim and Wadhwani, 1990). However, recently, there has been a shift in attention to the emerging markets of developing countries (Bekaert and Harvey, 1997; Chowdhury, 1994; Darat and Hakim, 1997; De Santis and Imrohoroglu, 1997; Hakim and Neaime, 2000, 2009; Neaime, 2002, 2005; Neaime and Hakim, 2002). The new focus stems from the fact that these markets present portfolio and fund managers a new possibility to enhance and optimize their portfolios, and may constitute safe heavens for international investors as was the case during the latest global financial crisis. For instance, Lagoarde-Segot and Lucey (2006) and using a fixed effect panel data model tested whether the MENA markets of Turkey, Israel, Jordan, Lebanon, Tunisia, Morocco and Egypt are subject to joint vulnerability to common exogenous shocks. Bekaert (1993), and Bekaert and Harvey (1997) found that stock market returns in emerging markets were high and predictable but lacked strong correlation with major markets. Meaning that in the instance of a financial crisis erupting those emerging stock markets may not experience significant losses comparable to those in the more mature markets as was indeed the case during the latest financial crisis. As emerging markets mature, they are likely to become increasingly sensitive to the volatility of stock markets elsewhere. Their increasing degree of integration with world markets will diminish their ability to enhance and diversify international portfolios, and will make those stock markets more vulnerable to external financial shocks through the potential of greater financial contagion. One important consequence of stock market integration levels is that it determines access to finance for a firm listed in an emerging market's stock exchange. In the long run, market integration expands diversification opportunities for domestic investors and international investors, and hence negatively affects expected returns (Chari and Henry, 2004; Stulz, 1999). The linkage between market integration and shock vulnerability has been formally described by Bekaert et al. (2005). Their approach was to extend the traditional Capital Asset Pricing Model (CAPM) from a one-factor to a two-factor setting. Moreover, the finance literature distinguishes between fundamental contagion and shift contagion. Fundamental contagion occurs as a result of greater economic and financial integration and disseminate through bilateral and multilateral trade agreements and stock market integration independently of the occurrence of a financial crisis leading to simultaneous negative co-movements in economic fundamentals. These shocks can be real or financial, and include among others, a fall in major stock markets indices, an increase in world interest rates, a decrease in international demand and capital flows, or sudden variations in the exchange rates of major currencies. By contrast, shift-contagion refers to the change in

5 272 S. Neaime / Emerging Markets Review 13 (2012) international and domestic investors' behavior and sentiments resulting from a shift in market expectations after controlling for the effects of fundamentals. The resulting shift in market expectations resulting from herding behavior leads to an observable structural break in the market linkages. Underlying mechanisms include financial cognitive dissonance, endogenous liquidity shocks, perception of political risk (Forbes and Rigobon, 2000), portfolio rebalancing (Kodres and Pritsker, 2002), and informational cascades (Calvo and Mendoza, 2000). Those complex factors which may occur simultaneously may affect categories of investors differently and are ultimately contingent on the crisis scenario. This study contributes to the existing literature by identifying how these two types of shocks were transmitted into the MENA region's real sector during and after the global financial crisis. Distinguishing between fundamental and shift-contagion vulnerability will be particularly useful when it comes to formulating policy recommendations on how to prevent future financial crisis from affecting the MENA region. Fundamental contagion can indeed be avoided through policies seeking a trend reversal in the MENA region's economic integration with the rest of the world. By contrast, restrictive policies are unlikely to be successful in the presence of shift-contagion since shock transmission operates through a change in the investor's set of beliefs. In that case, the relevant policy question is whether shock transmission stems from the irrational behavior of investors or from a set of domestic risk factors. On the other hand, the relationship between a financial asset's risk as proxied by its variance and its return is not only important for pricing financial assets, but also for quantifying the risk of contagion between financial markets. Therefore, the theoretical asset pricing models (e.g., Merton, 1973, 1980; Sharpe, 1964) link the return of an asset to its variance. However, the finance literature is still debating whether such a relationship should be positive or negative (Bollerslev and Zhou, 2006; Bouchaud et al., 2001; Campbell and Hentschel, 1992; French et al., 1987; Pindyck, 1984). Although most asset pricing models imply a positive relationship between stock portfolio's expected returns and risk (Baillie and DeGennarro, 1990) under the assumption of investor risk aversion, a long tradition in empirical finance states that stock return volatility is negatively correlated with stock returns (Cox and Ross, 1976; Whitelaw, 2000). The empirical results in previous studies may not be reliable because the variance modeling in this strand of the literature does not make efficient use of the data (Bollerslev, 1990). Several empirical papers found an insignificant and unstable relationship between returns and conditional variances in international stock markets (Glosten et al., 1993; Turner et al., 1989). In addition, while some empirical papers report a negative relationship (Nelson, 1991), other studies report a positive relationship between stock market returns and conditional variance of these returns (Bae et al., 2007; Campbell and Hentschel, 1992; French et al., 1987; Ghysels et al., 2005; Scruggs, 1998). Although the finance literature is rich in studies focusing on the interdependencies of major stock markets, 3 this study distinguishes itself from previous studies in two major respects. First, the paper constitutes the first attempt at studying how the recent global financial crisis affected the MENA region by looking at stock return volatilities in a broad number of financial markets in the MENA region: Kuwait Saudi Arabia, Egypt, Jordan, Morocco, Tunisia, and the UAE. Second, we include recent financial data and a larger data sample using daily returns correcting for nonsynchronous trading quotes which were cited as one of the reasons for the inconsistent results in previous studies (Lin et al., 1994). 3. Data and sample In order to identify the relationship between expected returns and time-varying volatility, this study applies a GARCH-in Mean model (GARCH-M) the Threshold ARCH and ARCH-M models, as well as VAR analysis. Our sample consists of daily observations of the national indices of the US (S&P 500), UK (FTSE 100), and France (CAC 40) and the 7 MENA major stock market indices of Egypt (EGX 30), Jordan (Amman Stock Exchange), Morocco (MADEX), Tunisia (Tunindex), Kuwait (Kuwait Stock Market Index), Saudi Arabia (Tadawul All Stock Index) and the UAE's Dubai Financial Market General Index (DFMGI: IND) for the period January 1, 2007 December 31, 2010, acquired from Morgan Stanley's financial data 3 The primary motivation for these studies has been to investigate the benefits of international diversification and to better gauge the transmission mechanisms across international boundaries. Some of the recent studies include Chan et al. (1992), Chung and Liu (1994), Engle and Susmel (1993), Hamao et al. (1990), Karolyi (1995), Lin et al. (1994).

6 S. Neaime / Emerging Markets Review 13 (2012) Table 2 Descriptive statistics for the market returns: Source: Author's calculations. Sample Country FR US UK Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE Mean (%) Median (%) Max (%) Min (%) Standard deviation (%) Skewness Kurtosis Jarqe Bera Prob # of days base. The missing data arising from holidays and special events are assumed to be the average of the recorded previous price and the next price. All of the national indices are based on local currencies. Thus, possible correlations due to a common factor such as common currency appreciation or depreciation are eliminated. Table 2 presents some descriptive statistics for the US, United Kingdom (UK), France (FR) and the MENA stock returns. The largest average daily return is observed for Egypt at 80%, while the smallest is observed for the UAE at 40.3%. The UAE has the largest standard deviation of the returns, while Kuwait has the smallest. The highest negative skewness is seen for Egypt, while the highest positive skewness is for the more mature markets of the US, UK and FR. The Jarque Bera statistic, which is a function of the skewness and kurtosis, indicates that for most MENA series, stock returns are not normal. The same is true for the US, FR and UK markets. Table 3 reports the correlations in returns for all 10-market indices. Stock returns show positive correlations in general but different magnitudes. The Saudi market stands alone with the lowest correlation even with the more mature markets of the US, UK and FR's stock markets. The markets of Tunisia and Morocco appear to be highly correlated with the more mature markets of the EU, namely FR and the UK, whereas the GCC markets, namely Kuwait and the UAE are more correlated with that of the US. The extensive trade within the context of the EU-Mediterranean Trade Agreements and financial links between EU countries and the two North African countries is one factor explaining the high correlation in stock market indices. The UAE market shows the highest correlation with that of the US, 45%, followed by Kuwait 16% and Morocco 11%. The inter-mena stock market correlations are the highest between the UAE and Kuwait, 55%, and Morocco and Tunisia at 30%. With the exception of Kuwait and the UAE, it is not surprising to find that the GCC countries are weakly correlated with the rest of the MENA countries. Unlike Table 3 Correlations of the stock returns (%). Source: Author's calculations. Sample FR US UK Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE FR US UK Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE 100

7 274 S. Neaime / Emerging Markets Review 13 (2012) Table 4 Correlations in the variances of the stock returns (%). Source: Author's calculations. Sample FR US UK Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE FR US UK Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE 100 the markets of Egypt, Tunisia, Morocco, and Jordan which are relatively open and liberalized, the Saudi stock market has remained relatively closed and over regulated. Saudi Arabia still needs to improve and liberalize its capital account, and promote greater integration with the world financial markets. Its stock market still lacks transparency and the government ownership in most companies listed, dominates by far the shares of the remaining MENA governments in their stock markets. In Table 4, we report the sample correlations in variances of the returns. Squared returns are used to represent the time-varying variances of the returns. One striking observation is that the correlations in the variances of the returns appear to be very similar to the correlations in the market returns of Table 3. The magnitude of the correlations is, however, much higher here than previously observed in Table 3, especially for the correlations between the MENA stock markets of Jordan, Morocco, Saudi Arabia, Kuwait, Tunisia, and the UAE, on one hand, and the more developed markets on the other. The highest correlation with the US market is that of Kuwait, 44%, followed by the UAE, 36% and Jordan 29%. Unlike Morocco and Tunisia, surprisingly Egypt appears not to be highly correlated with the EU markets. 4. Empirical methodology The causality-in-variance test of Cheung and Ng (1996) will be used to detect causal relations and identify causal patterns in variances between the emerging MENA stock markets on one hand, and between MENA and the more mature markets of the US, UK, and FR on the other. This test procedure involves two stages. We first determine the best-fit GARCH specification for the market return data; in particular, we will consider the following specifications: (1) GARCH (1,1); (2) TARCH or Threshold ARCH (1,1); and (3) ARCH-in-Mean. We then assess cross-correlations of the conditional variances from the retained GARCH estimation. Note that Cheung and Ng (1996) present a modified version of step (1) where, in addition to univariate modeling, a multivariate GARCH is also estimated. In this paper, we focus on the single equation (univariate) approach. 4 The reason is to guard against identification problems given the dimension of the system GARCH model There exists ample empirical evidence in the finance literature to suggest that return volatilities vary over time. To account for this empirical regularity we consider GARCH modeling. The general model under consideration is r t ¼ λ 0 þ λ 1 θ 2 t þ λ 2 r t 1 þ ζ t ; ð1þ 4 Engle and Susmel (1993) adopted a univariate ARCH model and assumed that the idiosyncratic component of the variances is constant (see also Chowdhury, 1994). The motivation for using this specification instead of a multivariate GARCH model is that it might be difficult to estimate the parameters, since it is very easy for the number of parameters in the M-GARCH model to increase to a larger number as the number of variables increase.

8 S. Neaime / Emerging Markets Review 13 (2012) where r t, represents the returns in period t and the specification of the variance is given by θ 2 t ¼ ρ þ νζ 2 t 1 þ δζ 2 t 1d t 1 þ βθ 2 t 1; ð2þ where d t =1 if ζ 0, and d t =0 otherwise. Restrictions on the parameters: ζ 0, in model (2) lead to popular special cases like for example θ 2 t ¼ ρ þ νζ 2 t 1 þ βθ 2 t 1: ð3þ Model (3) is the standard GARCH(1,1) model (Bollerslev, 1986), which ignores asymmetries, and the standard ARCH(1,1) special case (Engle, 1982) is θ 2 t ¼ ρ þ νζ 2 t 1 : ð4þ The simplest specification (4) is consistent with the volatility clustering often seen in financial returns data, where large changes in returns are likely to be followed by further large changes. The GARCH specification (3) is often interpreted in a financial context, where an agent or trader predicts this period's variance by forming a weighted average of a long term average (the constant), the forecasted variance from last period (the GARCH term), and information about volatility observed in the previous period (the ARCH term). In other words, if the asset return was unexpectedly large in either the upward or the downward direction, then the trader will increase the estimate of the variance for the next period. Table 5 reports the correlations in conditional variances from the GARCH model estimations. The contemporaneous correlation relationships are more or less similar to those obtained in Table 4. Again the correlations in variances from the GARCH model show that the GCC markets of Kuwait and Saudi Arabia are weekly correlated in variances with the other markets. The UAE stands alone in being highly correlated with the US market at 45.2% and the UK market at 29.6%. The remaining MENA markets are strongly related with each other and with the more mature market of the US, and to a lesser extent with the UK and FR's markets. Specifically, the stock markets of Morocco and Tunisia are strongly correlated in variances with the FR market at 42.1 and 43.1% respectively, while the correlation with the UK market is at 19.8 and Table 5 Correlations in conditional variances from the GARCH model estimation (%). Source: Author's calculations. Sample US UK FR Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE US ** 69** 25** 33.2** ** * 45.23* (0.002) (0.001) (0.02) (0.01) (0.027) (0.007) (0.066) (0.025) (0.006) UK ** 13.25* 12.44* * * 29.65* (0.001) (0.03) (0.02) (0.059) (0.02) (0.079) (0.059) (0.045) FR ** ** ** (0.03) (0.07) (0.079) (0.006) (0.081) (0.006) (0.045) Egypt ** ** ** 24.56** (0.03) (0.077) (0.066) (0.47) (0.059) (0.047) Jordan * ** 34.61** (0.088) (0.049) (0.063) (0.019) (0.033) Kuwait (0.02) (0.058) (0.05) (0.09) Morocco ** 54.66** (0.038) (0.065) (0.053) Saudi Arabia (0.02) (0.03) Tunisia (0.071) UAE 100 Notes: The conditional variances are measured by the squared of the standardized residuals from the estimated GARCH model. A *, ** indicate significance at the 5% and 1% levels respectively. The numbers in parentheses are the standard errors of the estimates.

9 276 S. Neaime / Emerging Markets Review 13 (2012) % respectively. Most of the correlation coefficients are significant at either the 5 or 1% level of significance TARCH model In the general model (2) where δ 0, the news impact is asymmetric, the good news (ζ 0) and bad news (ζ 0) have differential effects on the conditional variance. Good news will have an impact on ν, while bad news will have an impact on ν+δ. This specification corresponds to the TARCH model or Threshold ARCH model introduced independently by Glosten et al. (1993), Rabemananjara and Zakoian (1993), and Zakoian (1990), which takes into consideration the observation that stock returns are often found to be more volatile when the market experiences downward movements than when it experiences upward movements of the same magnitude. Table 6 reports the contemporaneous correlations of the squares of standardized residuals resulting from the estimated TARCH model. It is instructive to compare the results obtained from the TARCH model with those obtained earlier from the GARCH model and those in Table 5. One striking difference is that the correlations in variances have increased between the non-oil producing MENA countries on one hand, and between those countries and the more mature markets of the US, FR and the UK. The level of significance has also increased for Tunisia and Morocco where it is now at the 1% level. The highest correlation in variances is observed between Morocco and the US, 50.6% followed by Jordan, Egypt and Tunisia at 49.5, 49, and 26% respectively. The correlation in variances for Saudi Arabia remains low and not significant whether with the remaining MENA countries or with the more mature markets. However, those of Kuwait and the UAE have increased to 35 and 25.6% respectively. Again the correlation in variances has remained high between Morocco and Tunisia on one hand and FR on the other, at 44 and 45% respectively Causality patterns The most important causality patterns between the market returns and return variances between MENA and the more mature markets are presented in Table 7. The most striking result is the existence of a uni-directional relationship in mean and variances between the markets of the US and to lesser extent Table 6 Correlations in conditional variances from the TARCH model estimation (%). Source: Author's calculations. Sample US UK FR Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE US ** 55.4** 49** 49.5** 35** 50.6** ** 25.63* (0.003) (0.004) (0.01) (0.009) (0.025) (0.005) (0.023) (0.02) (0.068) UK ** 29.23* 32.1** ** ** 31.2* (0.002) (0.03) (0.03) (0.055) (0.03) (0.035) (0.036) (0.049) FR ** ** ** (0.016) (0.05) (0.025) 0.01 (0.065) (0.002) (0.032) Egypt ** ** ** 29.2** (0.003) (0.004) (0.003) (0.06) (0.021) (0.033) Jordan ** ** 35.6** (0.066) (0.005) (0.08) (0.009) (0.015) Kuwait (0.05) (0.042) (0.05) (0.07) Morocco ** 12.58** (0.02) (0.004) (0.08) Saudi Arabia (0.03) (0.02) Tunisia (0.023) UAE 100 The conditional variances are measured by the squared of the standardized residuals from the estimated TARCH model. A *, ** indicate significance at the 5% and 1% levels respectively. The numbers in parentheses are the standard errors of the estimates.

10 S. Neaime / Emerging Markets Review 13 (2012) Table 7 Summary of causality patterns. Source: Author's calculations. Sample Panel A: causality in mean patterns: sample Uni-directional causality Bi-directional causality No causality US(9**, 11**) E, US(8*, 11**, 12**) J, US(10**, 11**, 13**) M, US(4**, 6**, 10**) T, US(8**, 10**, 11*) U, UK(7*, 9**) J, UK(8*, 10**, 14*) T, FR(10**, 12**, 13**) M, FR(6*, 8*, 10*) T, S(0**, 1**, 3*) K, E(0**, 3**, 5**) J, E(0**, 1**, 2*) M, E(3**,4*, 6*) T. US(0**, 1**, 3**) UK(2*, 3**), US(0**, 1**, 3**) FR(0**, 2*, 3**), FR(0**, 1**, 3**) UK(2*, 3**), M(3**, 7**, 8**) T(1**, 3**, 6*), E(1**, 3**, 4*) T(0*, 2**). US and S, US and K, US and S, UK and M, UK and K, UK and S, FR and S, FR and K, E and K, E and S, J and M, J and K, J and S, T and U, T and K, T and S, M and U, M and K, M and S. Panel B: causality in variance patterns: sample Uni-directional US(10**, 12**, 16*) E, US(9**, 11*, 14*) J, S(5*, 7**, 9*) M, US(7*, 11*, 13**) T, UK(6**, 8**, 10*) causality J, UK(4*, 6**, 10*) T, US(3*, 6*, 8**) U, FR(5*, 8**, 10*) T, FR(4*, 8**, 10*) M, S(0**, 3**) K, S(1*, 3**, 6*) K, E(1**, 3**, 5**) J. Bi-directional US(1**, 2*, 5**) UK(0**, 5*), UK(0**, 1*, 4**) FR(0**, 5*), E(5*, 6**, 12**) T(4**, 8**), J(0**, 2*, 6**) causality M(1**, 3**), E(2**, 3**, 5**) M(0*, 2**). No causality US and T, US and K, US and S, UK and M, UK and K, UK and S, FR and S, FR and U, FR and K, UK and T, E and U, E and K, E and S, J and U, J and K, J and S, T and U, T and K, T and S, M and J, M and K, M and S. Notes: The symbols US, UK, FR, E, J, M, T, K, S and U denote the countries, United States, United Kingdom, France, Egypt, Jordan, Morocco, Tunisia, Kuwait, Saudi Arabia, and the UAE respectively. The numbers in parentheses denote the number of lags in days. The arrow symbols represent uni-directional causality from the left hand side to the right hand side, while represents bidirectional causality. For instance US(0**, 1**, 3**) UK(2*, 3**), means that the US returns at lags 0, 1, and 3 causes the UK returns (returns variance), and the UK returns at lags 2 and 3 causes the US returns. A *, ** denote significance at the 5 and 1% level respectively. the UK and FR and the MENA markets of Egypt, Jordan, Morocco, Tunisia, and the UAE, although with differences in lags. In addition, the statistically significant mean causal effects are arising more within 5 to 14 days. In other words, the US returns and variances seem to cause MENA's returns and variances but with a lag. This is, however, not the case for the remaining MENA stock market of Kuwait and Saudi Arabia, where the results show no causality between those markets and the more mature markets. The UAE is the only GCC market with stronger ties with the US stock market. The causality patterns in between the oil producing MENA markets are significant where the Saudi market seems to be the dominant market in uni-directionally causing both the Kuwait and the UAE markets in both the mean and variance. In the non-oil producing MENA markets, Egypt's returns seem to cause the markets of Jordan, Tunisia, and Morocco, while Egypt's variance is only causing Jordan's. We can summarize our findings as follows. The group of countries that has the stronger causal relationships in variances includes the US, UK, and FR and the non-oil producing MENA markets of Egypt, Morocco, and Tunisia. Among the non-oil producing MENA markets, Egypt appears to be the dominant market, while Saudi Arabia appears to be the dominant market for the oil producing MENA markets. The statistically significant effects between the non-oil producing MENA markets and the more mature markets originate from observations at relatively long-lags. In contrast, causality in variances within MENA happens at relatively short lags. Based on the causality patterns and the earlier empirical findings from the estimation of the two model specifications (GARCH, and TARCH), our empirical results confirm that while the stock markets of Egypt, Tunisia, Jordan, and Morocco have matured and are now integrated with the world financial markets (US, UK and FR), evidence of regional financial integration is still weak except among the GCC stock markets. Although, the stock market of Saudi Arabia appears to be segregated from the rest of the world, it can still offer portfolio diversification potentials to international and regional non-gcc MENA investors through mainly mutual funds, as well as a safe haven that would shield international investors form financial contagion in the instance of a new financial crisis erupting in the more mature financial markets. Moreover, the two dominant regional stock markets of Egypt and Saudi Arabia were not that affected by the global financial crisis. There were very minimal effects on Saudi Arabia's stock market, while the contagion effects of the global financial crisis were relatively limited on Egypt's stock market. This explains why the regional fundamental contagion effects of the global financial on the MENA region have been

11 278 S. Neaime / Emerging Markets Review 13 (2012) Table 8 Correlations in conditional variances from the ARCH-M model estimation (%). Source: Author's estimates, sample US UK FR Egypt Jordan Kuwait Morocco Saudi Arabia Tunisia UAE US * 27* * * 34.25* (0.03) (0.031) (0.057) (0.031) (0.016) (0.01) (0.013) (0.06) (0.07) UK * * * * 21.15* (0.05) (0.036) (0.03) (0.042) (0.03) (0.031) (0.02) (0.03) FR (0.031) (0.09) (0.026) (0.043) (0.07) (0.067) (0.067) Egypt * 20.32* 33.73** ** 25.66** (0.02) (0.017) (0.036) (0.007) (0.034) (0.037) Jordan * * 19.88* (0.051) (0.09) (0.05) (0.035) (0.036) Kuwait (0.05) (0.041) (0.06) (0.06) Morocco * 29.65* (0.03) (0.019) (0.019) Saudi Arabia (0.004) (0.006) Tunisia (0.019) UAE 100 The conditional variances are measured by the squared of the standardized residuals from the estimated ARCH-M model. A *, ** indicate significance at the 5% and 1% levels respectively. The numbers in parentheses are the standard errors of the estimates. relatively limited, and did not develop into a complete fall out of those markets in the aftermath of the global financial crisis. The sources of fundamental contagion have been mainly through the direct financial linkages of some MENA stock markets with the more mature stock markets of the US, FR and the UK, and through a real estate bubble which was developing in the Dubai real estate market prior to the crisis ARCH-M model Finally, the ARCH-in-Mean (ARCH-M) model (Engle et al., 1987) obtains in model (1) where λ 0. Table 8 reports the correlations in conditional variances from the ARCH-M model estimations. It is clear from the results obtained that the level of significance is lower for the correlations in variances between the MENA countries and the markets of the US UK, and FR. In addition, the correlation coefficients are also lower and have become sometime insignificant. Overall, the ARCH-M model seems to present the lowest significance for its estimated parameters. 5. Conclusion and policy implications In the aftermath of the global financial crisis, several MENA countries experienced significant financial/ economic slowdowns. MENA stock markets tumbled, real estate asset prices crashed, loans to the private sector and private capital flows dried up, GDP growth rates turned negative, spreads on sovereign bonds soared, and risk aversion increased dramatically. In South and East Asia, the economies in transition and those of Latin America and the Caribbean, the fallout of the global crisis was devastating, measuring in some instances three times the impact on MENA countries, primarily because of greater financial and economic integration. Yet, despite these negative fundamental contagion effects, the regional integration of financial markets should remain the policy objective of all MENA countries in general and the GCC countries in particular where there is a push for greater financial and economic integration and accelerated liberalization. However, as the crisis began to unfold and the fundamental contagion effects were spreading fast to their markets, some MENA countries began to question whether the benefit offinancial integration has been overestimated and its potential harmful consequences neglected and in some instances totally ignored.

12 S. Neaime / Emerging Markets Review 13 (2012) Moreover, the recent global financial crisis has shown that in the case of Egypt, Jordan, Morocco, Kuwait and the UAE's capital markets, greater global financial integration combined with the formation of a real estate bubble can increase vulnerabilities and create systemic risks. Prior to the global financial crisis, those emerging markets were growing at a fast pace with a strong endeavor to financially integrate regionally and globally. Specifically, intricate and complicated financial instruments were designed and traded, innovative securitization techniques were adopted, inappropriate incentive structures were introduced, all in an environment of excessive risk taking coupled with high liquidity and credit growth resulting from high oil prices and revenues fuelling a real estate bubble in some cases like the UAE. It is important to point that those MENA countries were not alone in this endeavor because globally and in other emerging markets, a sense of excessive risk-creation and risk-taking behavior was taking over, which, as we now have come to realize, has increased the scope for fundamental contagion across institutions, financial markets and borders. However, as these dynamics were unfolding, corporate governance, risk management, and prudential supervision in the oil producing MENA countries were failing to keep up with the rapid transformation of the financial systems, often deliberately in the spirit of greater financial integration and less government intervention. In this context, Dubai is a case in point. These financial developments have demonstrated the negative consequences of fast and deregulated global and regional financial integration. This paper highlighted some important aspects of financial contagion in the emerging MENA stock markets. After exploring the correlation in returns and variances within the MENA region on one hand, and between MENA and the more developed financial markets of the US, UK, and FR on the other, the paper used a dynamic model to empirically assess the implications of financial integration both at the regional and international levels. The paper has identified the common factors that might be partially driving the volatilities of the MENA stock markets. The estimation of the short-run dynamic relationships of the conditional volatilities in different markets has revealed how conditional volatilities are related and how important each MENA market is to the rest. The causality patterns in between the oil producing MENA markets are significant, where Saudi Arabia seems to be the dominant market in uni-directionally causing both the UAE market and the Kuwait market in both the mean and variance. In the non-gcc MENA markets, Egypt's returns seem to cause the markets of Jordan, Tunisia, and Morocco. Therefore, the group of countries that has the stronger causal relationships in variances includes the US, UK, and FR, and the non-oil producing MENA markets of Egypt, Jordan, Morocco, and Tunisia. The statistically significant effects between the non-gcc MENA markets and the more mature markets originate from observations at relatively long-lags. In contrast, causality in variances within MENA happens at relatively short lags. The paper has also identified the regional financial centers in the MENA region, as well as the global financial centers, and classified the MENA and world markets into several groups accordingly. Based on the above classification, the stock market of Saudi Arabia can diversify regional and international portfolios, and at the same time is the least to be affected by future financial disturbances whether regional or international. However, while the remaining non-oil producing MENA markets appear to offer little diversification potentials to international portfolios, they offer GCC investors significant portfolio diversification potentials, and safe havens in the instance of a new financial crisis erupting. While integration is generally a goal of any emerging market, it offers little reward to international investors seeking diversification. If all stock markets were fully integrated, investors will not find the diversification benefits they desire by tapping into emerging markets. Our results suggest that the stock markets of, Egypt, Jordan, Morocco, Tunisia and the UAE appear already integrated with the rest of the world's markets. It was argued above that the trade channel has constituted a major pass through of the global financial crisis onto the non-oil producing MENA countries of Egypt, Morocco and Tunisia. In addition, the still weak regional trade and financial integration linkages and the heavy dependence on external resources to finance development have also constituted another important transmission channel of the global financial crisis on the economies of the MENA region. While the relatively limited global financial integration of some MENA markets has prevented a complete fallout of those markets as a result of the recent financial crisis, greater MENA trade/financial integration could have helped the region avert the negative effects of the crisis on its economies, and could have dampened the effects of the trade channel pass through. Closer MENA trade links emanating from GAFTA since 1997, and from the GCC since 1981, have implied greater economic links among Arab countries in general and those of the MENA region in particular, and greater

13 280 S. Neaime / Emerging Markets Review 13 (2012) prospects for enhanced trade integration in the future among those countries. However, those trade integration efforts/initiatives appear to have been limited in stimulating and enhancing inter-mena trade. Enhancing south south trade and financial integration is the optimal macroeconomic policy option to avert future financial/economic crises from affecting the MENA region. Had the amount of intra regional trade and financial flows been significant, the trade and financial transmission channel of the global financial crisis could have been averted. Before enhancing trade integration with the rest of the world through the World Trade Organization, and other bilateral trade agreements, namely the Euro Mediterranean Free Trade Agreements, and other bilateral free trade agreements, such as the Jordan US free trade agreements, MENA countries need to enhance and strengthen trade integration at the regional level, and enhance intra-mena trade flows to strengthen the local markets, and subsequently enhance trade with rest of the world. One exit strategy from the global financial crisis would be an integrated MENA capital market which would lower the region's interest rates, thereby reducing the vulnerability of those economies to interest rate shocks, and benefiting those countries burdened with high levels of debt. Specifically, a larger, integrated regional financial market would reduce the huge costs associated with servicing the accumulated public debt, and would lower the cost of raising capital, allowing companies in the region to rely increasingly on the local market rather than the world market for economic development resources; lower capital-raising costs translate into higher investment and GDP growth rates. Enhanced financial integration would also substantially improve the MENA region's attractiveness to all types of Foreign Direct Investment (FDI). However, the global financial crisis has shown that private capital whether short term or long term capital flows has so far not been a reliable source of financing for development and growth in MENA countries, namely because short term capital flows such as portfolio investments are very volatile and speculative and because financial integration may lead to an increase in short-term speculative flows. On the other hand, long-term private capital flows, namely FDI, are concentrated in a number of emerging-market economies other than MENA countries. While most MENA countries have a significant demand for external financing for development needs and have relied in the past on such flows, they have so far received relatively small amounts of such overall FDI and other types of financial flows. Overall, the ease in transmission of the recent financial external shocks into MENA countries can be explained by their higher overall trade openness and by mismanagement in domestic financial and macroeconomic policies. Therefore, the size and impact of future external shocks and their persistence in the oil producing MENA countries will depend on the future domestic fiscal/financial policy responses, and on the success of the efforts to prevent the transmission of these shocks into domestic economies. This may be achieved through for instance implementation of appropriate fiscal reform measures, and through active reforms of the financial sector. Oil producing MENA countries should continue with their diversification efforts away from the oil sector into the services and industrial sectors. This will reduce their vulnerability to external shocks, their excessive trade openness and their heavy reliance on the contribution of oil and oil related products to their current and fiscal accounts. The recent GCC efforts to integrate into the global economy, which were recently enhanced by the accession of its largest economy, Saudi Arabia to the World Trade Organization in December of 2004, and the expected ratification of the EU/GCC Free Trade Area agreements, are all contributing factors to further enhance trade and investment climates in the region. Other initiatives to enhance regional and international integration efforts such as harmonizing investment laws and stock market regulation are also contributing factors toward the realization of the monetary union in the foreseeable future. The enhancement of local capital markets, especially stock markets is also another way to dampen the effects of the crisis, and will help reduce the exposure of private corporations to currency mismatches due to foreign borrowings. Those corporations will be able to raise funds locally and reduce their exposure to external financial shocks. They will also reduce any currency mismatch (exchange rate risk) in their balance sheets, and dampen the implications of any sudden outflows of capital emanating from the current crisis. The development of MENA's financial sector should be a top priority on the reform agenda. The role of the banking sector has been crucial to economic growth, because it has constituted the main source of financing the private sector's activity. Stock and bond markets are sometime virtually absent and firms

14 S. Neaime / Emerging Markets Review 13 (2012) cannot raise capital domestically. Increased financial integration within the MENA region is expected to bring considerable benefits to MENA's investors by rendering capital more mobile across borders. As a result, a more liquid capital market would offer lower borrowing costs for MENA's corporate sector wishing to raise funds locally and would lower its exposure to the short term speculative capital inflows. On the other hand, an important part of financial/trade integration is the increase in cross border trade through the lifting of trade and capital barriers as provided for in the various regional trade agreements, the GCC and the GAFTA agreements, and other MENA specific trade agreements. Regional trade agreements should have emerged from the global financial crisis as a way for middle and low-income MENA countries to increase trade, spur growth, lower unemployment rates, and dampen the negative transmission channels of the crisis. However, these regional trade agreements have so far failed in assuming this function, and their contribution to growth and development in MENA countries has been rather disappointing. Moreover, given the unstable economic environment in certain parts of the MENA region, the pursued macroeconomic/financial policies may not provide the required stability for increasing financial integration on a regional scale. It is well known that financial/macroeconomic instability have so far been a detriment to further integration in the MENA region. As regional and global liberalization proceeds, policies formed under former macroeconomic conditions will become increasingly under pressure for not providing the stability needed for sound economic development in the new context of regional integration. Macroeconomic policy coordination, as an integral part of multilateral free trade agreements in the MENA region, may therefore prove indispensable for successful economic integration in the region. 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