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Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
Cycles, Contagion and Volatility in the Stock Markets in ...
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  • 1. Cycles, Contagion and Volatility in the Stock Markets in Middle East and North Africa NFA Ver Version Abstract. Using weekly data for ten stock markets in the Middle East and North Africa (MENA) area grouped into three regions, we decompose the market indices in each region into short term cycles and long term trends. We then construct GARCH models for the volatile index cycles in each region to examine their behaviors in response to cycle contagion from other markets of the common region, changes in trends, political events, and changes in oil prices and global financial markets. We make recommendations on spillover versus diversification strategies based on the average cycles’ behaviors. We also measure the persistence of shocks to cycle volatility in each market and discuss the implications to traders, investors, and policy makers. JEL: F 3 Keyword: Trend; cycle; GARCH model; diversification: spillovers Acknowledgement: The authors wish to thank Professor Nadir Habibi of Brandies University and Aksel Kibar of the National Bank of Abu Dhabi for helpful comments, and Dr. Sulayman Al-Qudsi of the Arab Bank for helpful comments, encouragement and for providing us with data. The usual disclaimer applies. 1
  • 2. Cycles, Contagion and Volatility in the Stock Markets in Middle East and North Africa 1. Introduction Speculative attacks such as the 1982 Kuwait market crash, the 1987 US market crash, the 1994 Mexican crisis are short-lived phenomena that inflect damage the world’s stock markets. Several stock markets in the Middle East and North Africa (MENA) area, particularly those in the Gulf region, experienced a devastating collapse in 2006 and damage in January 2008 despite their strong fundamentals. In these crashes or crises the markets experience a large drop in stock prices and a dramatic jump in volatility in the short run. The speculative attacks can be caused by noisy traders who under price misperceptions bid stock or oil prices away from the fundamentals, leading to underpricing (De Long et al, 1990). The stock price or index has two components: transitory known as the short-term cycle, and permanent known as the long-term trend. The cycle may account for a larger size of the aggregate index (or price), making harder to understand the behavior of this index. Moreover, the cycle volatility usually reverts to normal levels quickly, but this transitory volatility may sometimes linger on longer for some markets more than others and can cause tremendous damage to wealth and social wellbeing, as happened in Kuwait in 1982 and the Gulf countries in 2006. It is important to examine the transitory component of an aggregate index in order to understand its cycle’s central return and volatility behaviors. This becomes more important when the research that uses the aggregate market indices does not detect causal 2
  • 3. relations or information flows between markets. Moreover, the behaviors of the cycle and its volatility are relevant to all traders, investors and options traders, particularly to those in the MENA area. Methodologically, because the cycles are stationary they are used in forecasting to achieve more predictive accuracy. However, the long-run trends are random walks or nonstationary and thus are not predictable. Our research on the regions of this area show limited information flows between the GCC aggregate market indices but hardly any relations between the aggregate indices of the markets in North African.1 This has motivated us to study the short-run cycle components of the MENA aggregate indices. As the MENA markets become more internationalized, they are more subject to market herding, fads, and speculative shocks as capital starts to flow quickly into and out of these markets. The internationalization of stock markets in developed countries and many emerging economies is a well-established fact. However, in many of the stock markets in the Middle East and North Africa (MENA) area, internationalization is a new phenomenon that is still taking shape. With strong increases in oil prices and consequently strong economic growth, the MENA markets have concurrently witnessed varying degrees of liberalization in the forms of privatized state-owned enterprises and relaxed restrictions on cross-border firm listing, capital movements and foreign stock ownership. The confluence of the liberalization reforms with the sharp increases in oil revenues, excess liquidity in some of these countries and petrodollar spillovers to others have brought about impressive increases in their market capitalizations, turnovers, IPOs and returns. It is certain that the movements in the stock market prices have crossed national borders to other countries in 1 The results of VAR/VEC models for the market indices of the regions in the MENA area using the same sample data used in this study are available upon request. 3
  • 4. the MENA area. The collapse of the stock market in Dubai in 2006 had spread to Saudi Arabia and the rest of the GCC like a hurricane. The spillover affected other neighboring countries in varying degrees, hitting for example Egypt more then Jordan. It will also be interesting to discern whether the short term cyclical components of the index movements, which are influenced by psychological factors, spill over from one market to another in the regions. The MENA countries under study for which we have uninterrupted weekly data include four Gulf Cooperation Council (GCC) countries (Kuwait, Oman, Saudi Arabia and UAE),2 three Levant or Mediterranean countries (Jordan, Lebanon and Turkey) and three North African countries (Egypt, Morocco and Tunisia). Several studies have examined the dynamic long-run movements and lead/lag linkages among stock price indexes of MENA stock markets but without decomposing the indices into cycles and trends to separate the impacts of the psychological factors from the fundamental. To understand how the market indices behave, we should also understand how the indices’ components move in addition to understanding their aggregate movement. Darrat at el, (2000) examine a subset (Egypt, Jordan and Morocco) of the MENA stock markets, indices and find these countries to be globally segmented but highly integrated within the region. They also find Egypt to be a dominant force driving other markets in this region. Omran and Gunduz (20001) examine the dynamic behavior of the MENA subset (Turkey, Israel, Egypt, Morocco and Jordan) over the weekly period August 1997 through July 2000 and find no evidence of cointegration among those five MENA markets. In contrast, Bley (2007) investigates the dynamic 2 Bahrain changed its index in 2004 and Qatar’s uninterrupted data is available starting from 1999 which is shorter than our data. Palestine, Syria and Algeria do not have lengthy and adequate market index data. 4
  • 5. behavior for fourteen MENA stock markets and finds that these markets to have three cointegrating vectors over the daily period January 2000-June 2002 and eight vectors over the period July 2002-December 2004. Using also daily data, Hammoudeh and Aleisa (2004) analyze the dynamic linkages among five GCC countries (excluding Qatar) over the period January 1994-December 2001and find two long-run relationships and short- term lead/lag feedback among the market indices. Bley and Chen (2004) analyze the dynamic relationships among the six GCC countries for the daily period January 2000 to September 2004 and also find evidence of increasing market integration. Saadi-Sediq and Petri (2006) find that the Jordan stock market to be cointegrated with other Arab markets but not with emerging or developed markets over the weekly period March 17, 1998- December 25, 2006. They contend that two of the main GCC markets, namely Kuwait and Saudi Arabia, Granger cause the Jordanian stock market. More recent literature on MENA market volatility uses the GARCH models to study volatility behavior and volatility transmission. Hammoudeh and Choi (2007) employ the univariate GARH approach with Markov-switching to study the volatility behavior for the transitory and permanent components of the GCC market indices, while allowing for two volatility regimes to exist. Hammoudeh and Li (2008) examines sudden changes in volatility for five GCC stock markets using the iterated cumulative sums of squares (ICSS) algorithm and analyzes their impacts on the estimated persistence of volatility. They find that most of these stock markets are more sensitive to major global events than to local and regional factors. Malik and Hammoudeh (2007) use multivariate GARCH models to analyze return volatility transmission among GCC markets, the oil price and S&P 500 index. 5
  • 6. With the exception of Hammoudeh and Choi (2007), all those studies indicated above do not distinguish between the short run and long run components that make up the stock market index. The dynamic structures for non-stationary stock price and other economic time series respond to forces that operate in both the short run and long run. Most of these series are thought to be driven by at least two factors of consequence. One of these factors is non-stationary and its dynamic is represented in some form of a random walk structure. In the financial literature, this has been referred to as long-term component, permanent component, and trend. We are interested in the decomposed movements of the cycles and the trends that make up those indexes. In particular, we are keen on knowing whether the short-run cycles of MENA stock markets are linked and subject to cross border spillovers. We are also interested in knowing if oil prices affect these short cycles in any of the three regions that make up the MENA area. The findings should have implications for type of investment and hedging strategies that portfolio mangers should follow, particularly whether they should follow a diversification strategy or a spillover-based leapfrogging strategy. The decomposition of stationary economic or financial time series into a random walk, representing the permanent component, and a cycle, representing the transitory component, is originated in Beveridge and Nelson (1981). Stock and Watson (1988) and Gonzalo and Granger (1995) extended the B-N decomposition to a multivariate system where each variable in the system can be expressed as a linear combination of several random walks and cycles. Later work by Engle and Kozicki (1993) and Vahid and Engle (1997) has shown that it is possible that the variables in the multivariate system can have common stochastic trends and common stochastic cycles, with the total number of 6
  • 7. common trends and common cycles to always be equal to or greater than the number of the variables. While the V-E decomposition requires that the total number of common cycles and trends be equal to the number of the variables to have efficient estimates of the permanent and transitory components, Proetti (1997) and Hecq, Palm and Urbain (2000) extended the efficient estimation to the general case when the total number is greater than the number of the variables. Shirvani and Wilbratte (2007) adopted a component-based approach to decompose stock prices into long-term trends and short-term cyclical components. In turn, they are able to explain the long-run trend component with the help of fundamentals for stock valuations. However, they find the cyclical components to be unrelated to such fundamentals. The cyclical components for the different countries appear to be related to contagion spillovers. They then discuss the implications of these findings in term of international diversification strategies. As in Shirvani and Wilbratte (2007), our next step in this research would be to determine the relevant explanatory variables in these components. At this stage, although it is just a conjecture, we believe the long-term component of a stock price index would be determined by the supply and demand of stocks. However, the short-term component would most likely be governed by various transient events (e.g., geo-political events, psychological factors, etc). The paper makes the following contributions to the literature on emerging stock markets. It decomposes the market indices for the ten MENA markets into short term cycles and long term trends. The decomposition demonstrates that the proportion of the decomposed cycle component in the market index increases for all the MENA regions 7
  • 8. during crises, particularly during the 2006 collapse. The GARCH results show that for the GCC and Levant region, cycles feed on themselves suggestion there is cycle contagion in those regions. Strengthening in the decomposed trends can also reduce the size of the cycles, focusing the markets on the fundamentals. It is also interesting to note that the findings pins UAE as the source of cycle contagion in the GCC region, Turkey for the Levant region and Egypt for the North Africa region. The change in the oil price does not show a much direct impact on the cycles including those of the GCC. Cycles are usually sensitive to psychological factors. The impact of past cycle volatilities strongly dominates that of past cycle shocks for all the regions, implying that past cycle volatilities can be used to predict future ones. Oil and S&P 500 dispersions have mixed impacts on the cycles’ volatilities within the regions. The paper is organized as follows. After this introduction, section 2 discusses the relevance and merits of studying the MENA stock markets. Section 3 provides the descriptive statistics and the correlations for the MENA markets, two global stock market indices (S&P 500 and FSTE 100), US interest rate and oil price for comparison purposes. Section 4presents the decomposition methodology and the GARCH model for the MENA index cycles. Section 5 examines the results. Section 6 concludes and section 7 highlights the future research on the trend components of the market indices for the MENA area. 2. The Relevance and Merits of the Study We contribute to the literature on emerging markets by investigating some of the features initially observed in the developing markets (Bekaert et al. (2000; 2003); Achour et al (1999)). We deem that the merit of this study lays on the representation of a more 8
  • 9. thorough understanding of markets in the Middle East which denotes a part of the flow of studies in emerging market (e.g., Geert Bekaert et al (2007), G. Bekaert and H. Campbell (2003)) It is genuine that the study of MENA market presents some challenges for researchers due to the lack of database and because these markets are in transition and hence not stable, yet they offer an opportunity to investors aiming to diversify by adding risk and increasing reward. Moreover, some known models in finance literature may not capture the nature of these markets but the recent strong interest in emerging markets in general and frontier markets has provided momentum for future investigation in that direction and called for further study of current models to new conditions in these markets. Here in this study, we explore the financial nature of the MENA markets, and some specific circumstances arising in these markets. We also consider a host of other issues such as cycle contagion, changes in trends, political events and changes in oil prices and diversification strategies in some selected MENA markets. Surprisingly, the growing body of research on contagion and political events primarily focuses on the developing markets; arguably the most organized and established markets in the world. In contrast, our study focuses on markets where political events and contagion may be particularly strong, namely MENA markets. In fact, country-specific risks and various barriers to international investment caused by the lack of information, discriminatory taxes, and restrictions on funds flows or simply fear of expropriation were always mentioned as some of the reasons that prevented some foreign institutional investors from investing in MENA markets and thus, prompted the observed bias of investors toward their domestic stocks in the developed countries. 9
  • 10. However, we deem that the aforementioned risk premiums are important features of these markets; thus, the focus on MENA markets should help and maybe yield exceptionally powerful tests and valuable evidence. In line with the contention made by Bekaert and Harvey (2002), we believe that MENA market integration is in progress and it is surely a gradual process which will depend on the circumstances in each country and it is most likely that all barriers will disappear either within the MENA region or eventually with the rest of global market. Notably, that MENA equity returns are highly volatile and significantly less correlated with equity returns in the developed markets, making the construction of low-risk portfolios optimal and achievable . Furthermore, the globalization of equity markets including the MENA countries has been increased dramatically with the surge of access to various international products. For instance, as of June 2006 the Morgan Stanley Emerging Market index includes some of MENA countries used in our paper such as Egypt, Jordan, Morocco, Turkey3. Also, in 1996 the Standard and Poor’s started to track frontier market and since 2007 the S&P launched the first investable index called Select Frontier index (30 of the largest companies from 11 countries) and the Extended Frontier Index (150 companies from 27 countries). Four out of the six MENA countries included in the initial frontier index are used in the present paper such as Kuwait, Lebanon, Oman, Tunisia, and United Arab Emirates. 3. Descriptive Statistics 3 For instance Turkey and Morocco have begun to open up their markets and "emerge" onto the global scene. 10
  • 11. In this study, we use weekly data obtained from Global Financial Data for the closing prices of the market price indexes of ten stock markets in the MENA area. We used weekly data because the countries have different weekends. Saudi Arabia, for example, considers Thursday and Friday as its weekend. Jordan observes Friday and Saturday as the weekend, while Turkey’s weekend covers Saturday and Sunday. All the ten indexes are expressed in US dollars to facilitate the comparison of descriptive statistics. We also include the statistics for S&P 500 index and FTSE 100 index, US interest rate and the oil price for meaningful comparisons. The weekly sample covers the period January 6, 1998 to December 4, 2007 for the markets in the Levant and North Africa regions and the period from August 25, 1998 to December 4, 2007 for the GCC markets. The price for oil (WTI, hence after) is the spot price quoted for immediate delivery of WTI crude at Cushing, Oklahoma, and is expressed in U.S. dollars per barrel. Data for the WTI price is accessed from the EIA website. Table 1 confirms that Egypt is the one with the highest number of listed companies in the sample followed by Turkey and Jordan. As a result if the total market capitalization of all companies in the world was estimated to be around US$51 trillion in 2007, the result in Table 1 implies that the MENA market capitalizations represents (3.20%) of the total market capitalization which ranges from $518 billion for the Saudi Arabia followed by Turkey ($286 billion) and then Egypt with $138 billion. Saudi Arabia’s stock market remains the leading market in the MENA with about $147 billion of trading value in the last quarter of 2007 followed by Turkey and Dubai, and it is considered somewhat competitve, with the average price-earnings ratio hovering at about 23.2. 11
  • 12. It is clearly illustrated that the Dubai followed by Abu Dhabi, and Kuwait markets stand out in the MENA region for the high relative liquidity of their stocks. Dubai is ranked first for turnover velocity, an indicator which is defined as total trading volume divided by exchange market capitalization such that a high turnover rate may indicate excessive trading and commissions. In a context of growing international investment activities, another key liquidity indicator is the trading turnover which is the percentage of outstanding traded shares during the fourth quarter 2007. Table (1) indicates that Turkish stock market (129.7) followed by Dubai (39.1) and Saudi Arabia (28.4) stock markets are among the top three most liquid stock markets in the region. Nevertheless, the data confirm that the figures of trading turnover in the region are still significantly below those of the most liquid markets. With the exception of Turkey, all MENA countries reveal trading turnover rates which range from 39.1 (Dubai) to 3.7 (Lebanon), while Tokyo and NYSE have 138 and 167, respectively. W ith the exception of Turkey. the liquidity ratios have remarkably improved in between 1989-1999 in Morocco, GCC countries and Egypt but they have remained relatively in the two-digit range. Yet, by looking at some market characteristics between the first and the fourth quarter of the year 2007, we could suggest that these countries showed a noticeable growth in market capitalization, value of traded shares, turnover ratio, and number of listed companies. This growth is associated with the massive privatization plans introduced in the region; the sale of government assets to private foreign companies; and considerable efforts towards improving the efficiency, suggestiing that liquidity is improving in the MENA stock markets4. 4 For instance, it is widely noticeable the massive privatization plans introduced in Morocco, Egypt and Turkey and further significant efforts devoted towards enhancing the efficiency, depth, and liquidity of the three stock markets. 12
  • 13. In terms of earnings multiple ratio, Morocco stands out in the MENA region with a high rate followed by Jordan and Saudi Arabia stock markets which implies that their listed companies are considered expensive, and investors in these three countries are paying more for each unit of earnings. Whereas other markets such as Turkey, Tunisia, Dubai and Bahrain have low earnings multiple ratios suggesting that companies in those markets could be considered cheap and attractive investments; in fact those ratios are in the range of those of developed economies (e.g., P/E of S&P 500 was 15.32 in 2007 Q.4 ) The descriptive statistics for the log of the weekly ten MENA and the two world’s major market indices, US federal funds rate (FFR) and oil price are reported in Table 2. They MENA statistics suggest that Turkey and Kuwait have the highest weekly index mean, while Morocco has the lowest over the sample period. In terms of historical volatility as represented by the standard deviation, Kuwait and Egypt have the highest while Tunisia has the lowest. In terms of the risk-to-mean ratio as defined by the coefficient of variation (c.v.), Oman has the lowest while Egypt has the highest. What is most informative about these statistics is that both historical volatility and coefficient of variation are much higher in the MENA markets than for S&P 500 and FTSE 100 indices but not for FFR and oil price WTI. It is also interesting that all the MENA indices are skewed to the right, while both S&P 500 and FTSE 100 indices are skewed to the left. This means that there is a higher probability for investors to earn positive returns from the MENA area than to get negative returns. The highest skewed index in the MENA area is for Morocco and the lowest is for Tunisia. The Jarque-Bera statistic points to a distribution that is different from the normal distribution for all the MENA markets except Tunisia. 13
  • 14. The weekly contemporaneous correlation coefficients among the market indices, US interest rate and oil price over the sample period are displayed in Table 3. The highest index correlation MENA area is between UAE and Jordan (0.99), followed by the correlation between Kuwait and Jordan (0.96). This is not surprising because Jordan depends on remittances and direct and portfolio investments flowing from the GCC countries. These correlations are much higher than the correlations between the S&P 500 and FTSE 100 indices (0.89) and between these two world’s major indices and oil price (0.35 with S&P and 0.04 with FTSE). They are also much higher than the correlations between these world’s indices and the US federal funds rate. The MENA country that has the highest correlation with oil price is Saudi Arabia followed by Kuwait. Saudi Arabia is the world’s oil exporter and Kuwait is a major exporter as well. The highest positive correlation between MENA markets and US interest rate is with Lebanon. However, Kuwait and Saudi Arabia have a negative correction with the US federal fund rate, probably caused by their actual or effective peg to the US dollar. It is also noticeable that GCC countries have lower correlations with the two world’s major indices than the markets in the other regions of MENA. This is also not surprising because the GCC markets were segmented from the world markets but recently the correlation is rising after the recent liberalization and reforms in the GCC markets. It is also worth nothing that the markets in North Africa have higher correlations with S&P 500 and FSTE 100 than with any other countries in the MENA area. This could be due to their geographical proximity and business links with the European countries. Turkey has its highest correlation with Egypt. Those two countries are among the most populous in the area and similar in other economic aspects. 14
  • 15. It is worth mentioning that the index correlations among markets within the same region have changed significantly since the collapse of the MENA markets in early 2006. This is particularly true for correlations in the GCC countries where correlations are not only declined but also become negative in some cases. For example, the index correlation between Kuwait and Saudi Arabia changed from 0.95 over the sample period to -0.17 during the period since February 2006.5 The correlation between Kuwait and UAE also changed from 0.94 to -0.02 over the corresponding periods. The correlation also turned negative between Oman and each of Kuwait and Saudi Arabia. Interestingly, the index correlations between Saudi Arabia and UAE remained high. These drastic changes in correlations among the GCC stock market reflect unusual lack of policy coordination among policy makers in this region during crises, particularly the 2006 markets’ collapse. This is something those decision makers should stoke concerns about as they plan to form a monetary union which is scheduled for 2010 and a common stock market after that.6 Drastic changes in index correlations over the corresponding periods also occurred in the Levant region. These changes are pertinent to correlations between Jordan and Turkey, and Lebanon and Turkey. However, the correlation increased between Jordan and Lebanon since the 2006 collapse. In contrast, the correlations for the North Africa region increased since the 2006 collapse for most of the region. 4. Methodology We first present a state-space approach that cast the Beveridge -Nelson decomposition into a multivariate system representation for each region in the MENA 5 The correlations among the MENA markets since the early 2006 collapse are available upon request. 6 http://archive.gulfnews.com/business/Markets/10194636.html 15
  • 16. area. This approach is provided by Morley (2002), Proetti (1997) and Hecq et al (2000). Next, the methodology presents the univariate GARCH model for the generated cycle component of each stock market index in the MENA regions, where in its mean equation it depends on the lagged cycles and changes in the trends of market indices in its respective group, and on a dummy variable. State space representation Let xt be a vector of n I(1) variables which can be expressed as a VAR system in levels of order p by: xt = Π 0 + Π1 xt −1 + ... + Π p xt − p + ε t (1) If Eq. (1) can be most accurately described by a VAR system of its first difference with order p-1, then this system can be expressed as: ∆xt = Λ 0 + Λ1∆xt −1 + ... + Λ p −1∆xt − p +1 + µt (2) The AR(1) representation of the VAR in Eq. (1) using state-space is straightforward and can be given by state equation and vector: ∆xt = Zf t (3) f t = c + Ηf t −1 + Z ' ε t (4) 16
  • 17. The state vector and transition matrix are represented by:  Λ1 Λ2 L Λ t − p +1   ∆xt   I  ∆x   n 0 n× n L 0 n× n   ft =  t −1  and H =  0 In O M   M   n×n     M O O M   ∆xt − p + 2     0n×n  0 n× n L 0 n× n   where Z=[In, 0nxn, …, 0nxn] is an n× n(p-1) matrix, c’=[ Λ 0 ’, 01xn, …, 01xn] a 1×n(p-1) matrix, ft an n(p-1)×1 vector, and H an n(p-1× n(p-1) transition matrix. If xt is best described by a VECM system, then this system can be expressed as: ∆xt = Γ 0 + αβ ' xt −1 + Γ1∆xt −1 + ... + Λ p −1∆xt − p +1 + µt (5) where α and β ’ are two n× r matrices, β is the matrix of r cointegrating vectors and α is the matrix of adjustment coefficients. Following Hecq (2000), the state-space representation is the same as equations (3) and (4), but the definition of state vector and transition matrix are expanded:  ∆xt   Γ1 + αβ ' Γ 2 L Γ p −1 α   ∆x   I 0 n× n L 0n×n 0n×r   t −1   n  f t =  M  and H =  0n×n O O M M       ∆xt − p + 2   M O O M M   β ' xt −1     β'  0 r ×n L L Ir   ft then is an (n(p-1)+r)×1 vector, H an (n(p-1)+r)×(n(p-1)+r) matrix, Z=[In, 0nxn, …, 0- nxn ,0nxr] an n×(n(p-1)+r) matrix, and c’=[ Γ 0 ’, 01xn, …, 01xn,01xr] a 1×(n(p-1)+r) vector. Multivariate Beveridge-Nelson decomposition 17
  • 18. The multivariate Beveridge-Nelson decomposition is expressed as the sum of a random walk trend (Tt) and a stationary cycle (Ct): xt = Tt + Ct (6) The trend is defined as: k Tt = xt + lim ∑ [∆xt +i|t − E (∆xt )] % (7) k →∞ i =1 where ∆xt +i|t is the ith-step best unbiased linear predictor given all information available % at time t. the cyclical component is then defined by: k Ct = − lim ∑ [∆xt +i|t − E (∆xt )] % (8) k →∞ i =1 Both VAR representation in Eq. (2) and VECM representation in Eq. (5) have drifts, therefore the unconditional mean E ( ∆xt ) ≠ 0 . To simplify Eq. (8), we modify Eqs. (3) and (4) by distracting the mean of ∆xt from the state vector. Following Proetti (1997) and Hecq (2000), we assume the drift has a constant mean over time. Using Eq. (4) the t −1 expected mean of drift is ct = (∑ H )c . Under stability condition that all eigenvalues of * i i =0 H stay inside the unit circle, As t → ∞ , ∑ i =1 H →( I m×m − H ) H so that while t → ∞ , ∞ i −1 c* = ( I m×m − H ) −1 c , where m=n(p-1) for VAR system in Eq. (2) without cointegration and m=n(p-1)+r for VECM system in Eq. (5). Plugging the expected value of drift into equations (3) and (4), we get: ∆xt = Zf t * + Zc* (9) 18
  • 19. ft* = Ηft*1 + Z ' ε t − (10) where f t = f t − c . Therefore the best unbiased linear predictor of ∆xt +i is * * ∆xt +i|t = ZH i ft |*t % (11) * where f t|t is the updated estimate of the state vector based on Kalman filter. Since all elements of the state vector f t are observed at time t, we get f t|t = ft . On the other * * * hand, by taking the advantage of the stability conditions again, we plug Eq. (11) into Eq. (8) to get the cyclical component of Beveridge-Nelson decomposition for each market in the MENA area: Ct = − Z ( I m×m − H ) −1 Hf t * (12) The trend component for each market is: Tt = xt − Ct (13) GARCH models for cycles Following the derivation of the index cyclical and trend components for all the markets in the respective region, we can employ the GARCH model to examine the volatility clustering behavior of cycles, their linkages across markets within each of the three MENA regions, their responses to changes of trends within the same region, their sensitivity to changes in the oil price and their reaction to structural dummies. In the GARCH model, we construct the mean equation for the cycles of the ith country in a gives MENA region as: Cit = π i 0 + ∑ j =1 π ij C j ,t −1 + ∑ j ≠iν ij ∆T j ,t +γ i1dlwtit + γ i 2 dlsp500t + γ i 3 D + ε it N (14) 19
  • 20. and the variance equation for that cycles as7: σ it =ωi +α i ε it −1 +β iσ it −1+ i1 dlwtivt + ψ i 2 dlsp 500vt + i 2 D ψ ψ 2 2 2 (15) where dlwitvt = (dlwtit − sample mean of dlwti ) and 2 dlsp500vt = (dlsp500t − sample mean of dlsp500) 2 , i, j are country indexes, N is the number of countries in a zone, Cit is the cycle component of country i at week t as in Eq. (13) and C j ,t −1 is that of country j at week t-1. ∆T j ,t is the first difference of trend component of country j at week t. Both trends and cycles are derived with the multivariate Beveridge- Nelson decomposition discussed above. Moreover, dlwtit is the log price change of Nymex spot WTI price of week t, dlsp500t is the log change of S&P 500 index of week t, and D is the dummy variable representing a break as a break in the data. The structural dummies that have been identified are D00 representing the change in the OPEC oil pricing mechanism to a price band in February 2000, the D003 denoting the break as a result of the 2003 Iraq war and D911 for the 2001 attack on New York. In Eqs.14 and 15, π 0i is the long term drift, ε it is the error term for the ith country at week t, where ε it | It – 1 ~ N(0, σ it ) and where N (.) represents the conditional normal density with mean 0 and 2 variance σ it , and It – 1 is the information set available up to time t –1. We note that the 2 above mean equation has an AR(1) term because of the existence of significant serial correlation among cycles. 7 We also replaced S&P 500 alternatively by FTSE 100 and FFR in both the mean and volatility equations but the results were not as significant, and thus are not reported. 20
  • 21. In the variance equation, σ it stands for the conditional variance, and ε it are the 2 2 squared residuals from the mean equation, which is the ARCH term. The coefficient βj represents the GARCH or past volatility effect and αj captures the ARCH or the past shock effect. The sum of αj +βj measures the degree of convergence to long-run equilibrium or volatility persistence for cycle j in this model. A high value for this sum indicates slow convergence or high volatility persistence. The half-life of cycle volatility shocks, which are the sum of the AARCH and GARCH coefficients in the variance equation, is defined as Half-life = ln(1/2)/ln(α + β) This equation gives the time period (or number of weeks in our case) for the volatility to reach half of its life after a shock hits. The shorter the half life, the faster the volatility shock will vanish. In this study we expect the volatility shock to be transitory because we are dealing with short run cycles. Three exogenous variables are included in the variance equation, which are one of the three dummy variables: D00 for the OPEC price band, D01 for the 2001 New York City attack and D03 for the 2003 Iraq war used in the mean equation, and the squared deviations of oil price and S&P 500 index from their respective sample means. 5. Empirical results In this section, we present various empirical results for a large set of Middle East and North African countries combined into regions over the sample period. More than at any time in its long history, the Middle East today represents a real patchwork of individual regions and states, defying the attempts of outsiders to define it as a 21
  • 22. homogeneous entity. Based on the results of the return correlations, geographic and economic similarities, we will initially classify the MENA markets into three primary regions: The GCC, Levant (Mediterranean) and North Africa. The GCC in this study includes Kuwait, Oman, Saudi Arabia and UAE. These countries share geographic proximity and high dependence on oil export and labor imports and their stock market returns have relatively higher historical correlations. The Levant region includes Jordan, Lebanon and Turkey.8 These countries are some what close in terms of geographic proximity and are labor exporters. The return correlation coefficients show high correlation between Jordan and each of Lebanon and Turkey. Finally, the North Africa regions include Egypt, Morocco and Tunisia. They share geographic proximity and are labor exporters. Morocco and Tunisia and Egypt and Tunisia have relative high return correlations. The three primary regions will be augmented with stock markets from other MENA regions and two major world’s stocks markets to determine the impact of spillover across border whether a results of international trade, neighborhood or/and financial flows. 5.1. Decomposition of MENA Stock indices: Cycles and Trends Since we are interested in extracting the trend and cycle components and apply these to one-step-ahead forecasts, we simply specify the component model in discrete time directly. In this section, we will examine the behaviors of those decomposed cycles in each region and their proportions of market indices during periods of crises. In the following subsection, we will focus on finding explanatory variables for the extracted 8 We could not include Palestine and Syria in the Levant region and Algeria in the North Africa region because these countries do not adequate time series for their market indices. 22
  • 23. components in addition to exploring any relationships between the components of the stock indices. The GCC region All the decomposed index cycles in this region are highly volatile, but there is still a common pattern that characterizes all of them (see Fig. 1). The cycles increased in size in the late 1990s; a period that witnessed the 1997, 1998 and 1999 Asian, Russian and Argentinean crises, and the collapse of oil prices in 1999.9 The cycle patterns show that GCC cycles increased during the periods of global economic crises. These cycles also increased during the period 2005-2006 which overlaps with the regional stock market collapse. The proportions of the decomposed cycles in the actual stock indices in the respective markets of this region also increased in those periods (see Fig. 2). The long-run trends of the four GCC countries have also moved up since 2000 which marks the creation of the oil price band by OPEC, reflecting recent economic growth. These trends were later reinforced by the 2003 Iraq war and the consequent increase in commodity prices. Interestingly, the trend dropped for two of the GCC countries, namely Saudi Arabia and UAE, after the 2006 stock market collapse, but at the same time continued to grow for Oman and Kuwait, which were affected the least by the collapse. In sum, this index trend pattern shows that the GCC stock markets do not behave the same during crises. The Levant region Contrary to the VARs in GCC and North Africa regions, the index VAR for this region is cointegrated. The cycle pattern similarity in this region reflects the 9 http://economictimes.indiatimes.com/articleshow/msid-902394,prtpage-1.cms 23
  • 24. cointegrating trend that co-moves the markets (see Fig. 3). The trends generally move upward in this region, moving up again in 2005 and then down in 2006 as in the case of the GCC countries. But the cycles show general decline until 2005 when they reversed themselves in a strong jump compared to the GCC trends during the same period. The proportions of cycle to index in this region generally decline because of the upward cointegrating trend. But during the 2006 regional market collapse, the proportion of cycles in the indices increased similar to the case in the GCC region. The North Africa region The trends in this region started to move up in early 2003, long after they started to move in the GCC region. The trend seems to be more aggressive in Egypt than in the other two countries in this region and in the other regions. While the cycles in this region share with those in the GCC the up and down patterns that developed since 2005 they did not move erratically in late 1990s as the GCC did. This probably has to do with differential impact on the regions of the 1999 collapse in the oil price. The cycles in this region increased during the 2000-2002 period, which is not shared with the other two regions. 5.2. The Cycle GARCH Results We analyze the cycle volatility behavior for the markets in the three regions as specified in Eqs. (14) and (15) of the previous section. We discuss the results of both the cycle return mean and volatility equations, which make up the GARCH model, and pay particular attention to volatility persistence and the half-life. The GCC GARCH results 24
  • 25. In the GCC region, the estimates of the short-term market index cycles in the mean equations of the GARCH models are provided in Table 4. Kuwait and Saudi Arabia are the most affected by the past cycles, originating from GCC members, implying that GCC cycles feed on themselves and giving credence to the so-called friendship bias hypothesis which conjectures that many countries prefer to invest in the same geographic region (Berkel, 2007). Specifically, Kuwait and Saudi Arabia as well as Oman are positively affected by the past cycles coming from the UAE markets. The UAE cycle is however not affected by any past GCC cycle except its own. It seems that the UAE markets are a source of contagion in this region. Anecdotally, the bubble that burst and spread to other GCC markets in early 2006 originated from the UAE markets. This is not surprising because Dubai has experienced astronomical growth in its economy. These results suggest that the GCC index cycles are positively affected by contagion spillovers. Thus, based on the cycles’ behavior these markets are less suitable for diversification gains potential in the short run. Investors and traders in this region can be anticipatory and position themselves in a certain GCC market to benefit from the contagion spillover from other GCC markets. If the transition costs are not prohibitive, traders can switch from one market, particularly from Dubai’s to another or switch to other asset types or regions based on the relative persistence of the markets of interest. Interestingly, trends in the previous period in this region also affect the current cycle of any given market index. The impact is negative which suggests that any strengthening in past week’s trends of other GCC markets reduces the current week’s cycle contribution to the index for any market in the region. This implies that traders, 25
  • 26. investors and portfolio managers should give more weight in their decision-making to trends over cycles particularly when the trends are strengthening in this region. In terms of structural breaks in the data, the R–squared and AIC select the structural dummy D00 which represents a lift in the oil price to the central price parity of the oil price band over the 9/11 and 2003 Iraq war dummies, D01 and D03, respectively. The results show that this dummy variable is not statistically significant in affecting the cycles for the GCC countries, except Kuwait. Also, the change in the oil price does not show a significant direct impact on the cycles, which are usually sensitive to psychological factors. The global factor S&P 500 return reduces the cycle of Saudi Arabia only.10 In the variance equation, the impact of past cycle volatilities strongly dominates that of past cycle shocks for all four GCC markets, implying that past volatilities can be used to predict future ones. Cycle volatility has the least persistence and the fastest convergence is in the case of Oman followed by UAE. It seems that Oman restores stability faster than any market in this region. In fact, after the 2006 GCC stock market collapse, Oman restored stability and ended with a modest positive gain. Additionally, cycle volatility is more persistent for Saudi Arabia than for any market in this region. Saudi Arabia which is characterized by “too much money chasing too few stocks” experienced the second worst market collapse in 2006 and is still reeling from its aftermath in 2008. Correspondingly, the half-life for volatility shock in Saudi Arabia is about eight weeks compared to less than three weeks for Oman and UAE. These findings should be particularly relevant in options valuations which use current volatility as a 10 FTSE 100 and federal funds rate (FFR) have no impact on this region: the results are not reported but are available upon request. 26
  • 27. determinant in pricing options. They are also informative for policy makers of those countries as they plan to have a monetary union and a common currency. In terms of sensitivity to global stock market variations or risks, cycle volatility in each of Oman, Saudi Arabia and UAE is negatively sensitive to the global risks as representative by S&P 500 index. In January 24, 2008, the GCC markets were affected by varying degrees to the one-week drop in the S&P 500 index, showing more interaction with global world market and less decoupling than in the past. Interestingly, the oil price dispersion or variation affected the cycle volatility of only UAE and Oman, which is unusual for these oil-exporting countries. The Levant GARCH results Similar to the GCC markets, past market index cycles in the mean GARCH equations for the Levant region also expand current cycles (see Table 5). The major source of cycle contagion in this region is Turkey, which have been facing conflicts inside the country and around it as well as financial crises. Turkey has by far the largest market capitalization in this region and is also characterized by highest market capitalization density per company which narrows the investment opportunities.11 One difference with the GCC region is that cycle interaction among the markets in the Levant region is much less than in the GCC region. Thus, cycle contagion or cycles-feed-on- cycles behavior is less consistent in this region than in the GCC region. An increase in the permanent components of the other market indices in this region as represented by the long-run trends seem to reduce the index cycle component for almost all of the countries in this region as is the case in the GCC region. This implies 11 Turkey’s market capitalization at the end of 2007 is $286 billion and the number of the companies listed is 245 firms. 27
  • 28. that when the permanent component of the market index gains momentum, the transitory cycle component in the index return decreases as indicated in the previous region. The structural break dummy D00 is also selected in this region over the 9/11 and 2003 Iraq war dummies. Interestingly, the results for D00 suggest that that there has been a permanent increase in the oil price since 2000, giving rise to a statistically significant decline in the short term cycles for all countries in this region, a result which is not as pervasive in the GCC region. However, the oil price itself does not suggest that there is a significant direct impact on the cycles for any of the countries as is the case in the previous region. However, the S&P 500 return seems to increase the cycles in all the countries, indicating that these cycles are more susceptible to the global stock market than the GCC countries. FFR has no effect but FTSE 100 affected Lebanon and Jordan. Those two results are not reported. The past cycle volatilities in the Levant have stronger impacts than the past cycle shocks for all three markets in this region as is also the case in the GCC region. The half- life for a volatility shock for Jordan is about nineteen weeks, the highest in the ten markets, compared to surprisingly about five weeks for Lebanon. D00, the oil dispersion risk and S&P 500 risk do not have any significant impact on cycle variance for any of the countries. The North Africa GARCH results What distinguishes this region from the two previous ones is that in this region there is not much interaction between current cycle of a given and past cycle returns of the other markets in this region (see Table 6). The only exception is the interaction between Morocco and Tunisia. This finding qualifies this region for cycle portfolio 28
  • 29. diversification in the short run.12 However, there are strong impacts on the cycle component coming from long-term trends of the markets. It is interesting to note that the impact coming from Tunisia on Egypt and Morocco and from Morocco on Tunisia is positive, implying an expansion in the cycle component. This is different from what we have in the previous regions. However, the impacts from the Egyptian and Moroccan past cycles are negative. The R–squared and AIC select the structural dummy D03 which represents the 2003 Iraq war. The impact of this geopolitical event is marginal. Oil risk has a significant impact on Tunisia’s cycle volatility. S&P 500 risk has no effect on this region. The impact of past cycle volatilities also strongly dominates that of past cycle shocks for all three markets in this region as in the previous two regions. Cycle volatility has the most persistence and the slowest convergence as in the case of the Egyptian market. The half-life for volatility shock in this market is about twelve weeks. The half- life for Tunisia is five weeks. 6. Conclusions This study examines the cycle components of the indices for ten stock markets in three regions of the MENA area using weekly data in the presence of global shocks. In the GCC region, there is a cycle spillover from one country to another with the UAE market being the contagion source, suggesting that diversification within the region based on movements of cycles is not the best strategy in the short run. Therefore, investors could use leapfrogging from one market to another or get out in case of negative cycle 12 Hammoudeh et al (2008) also find no cointegration among the market indices of this region, suggesting the presence of diversification benefits potential in the long run. 29
  • 30. contagion once a problem starts in one of the markets particularly Dubai, weighing this strategy over diversification in the short run. The GCC policy makers should coordinate policies to deal with the negative spillovers. The 2000 adjustment in the oil pricing mechanism from a single price targeting to band targeting affected the mean cycle component of the stock index negatively only in the case of Kuwait but it did increase the cycle volatility for any country in the region. The oil price volatility does not have much direct effects on index cycle volatility. Moreover, since the half-life of cycle volatility in the GCC region is on the order of three to eight weeks, fluctuations in cycle volatility could certainly affect the values of financial derivatives such as options on futures contracts in these countries because those derivatives have typically a duration of several months. But those short-lived fluctuations should have not any considerable impact on real options or the related investment decisions. In the Levant region, the cycle-on-cycle behavior is present but weaker than in the case of the GCC region. What distinguishes this region from the other two is that the reinforcing feedback, giving cycles relatively more weight in decision-making. Moreover, volatility persistence and half-lifes are stronger in this region than in the other ones, making financial hedging more pressing for this region. The North Africa Region, there is no significant cycle-feed on cycle spillover among most of the country's mean cycles. The exception is between Tunisia and Morocco. There is however volatility persistence which linger much more for Egypt than for Tunisia and Morocco. Fluctuations in cycle volatility could also affect the values of financial derivatives in this region but with no significant impact on real options and related investment decisions. The 2003 Iraq war dummy marginally affected the mean 30
  • 31. cycle component of the stock indexes, showing this region’s weak sensitivity to political events. The oil volatility impacted the cycle volatility for Tunisia only. Tunisia turned from a net oil exporter to a net oil importer recently. As in the other regions, this region’s cycles are not sensitive to changes or volatility or S&P 500 index and the US federal funds rate. 7. Future Research This paper examines the mean and volatility behaviors of the cycle components of the MENA’s stock market indices. It should also be useful to study the same behavior for the market indices’ trends which are usually influenced by the fundamentals such as CPIs, interest rates …etc. The trends are however are nonstationary and thus are not predictable. This task will also be more challenging to be conducted at the weekly level because the data is not available. We plan to carry out a study at the monthly data to study the behavior of the trends and their volatility persistence. 31
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  • 34. Fig. 1: Trends and Cycles for the GCC markets Panel A: The GCC weekly long term trends 11 10 9 8 7 6 5 98 99 00 01 02 03 04 05 06 07 TRLKUWAIT TRLOMAN TRLNBAD TRLSAUD Panel b: GCC weekly short term cycles CLKUWAIT CLNBAD .06 .12 .04 .08 .02 .04 .00 .00 -.02 -.04 -.04 -.08 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 CLOMAN CLSAUD .08 .04 .03 .06 .02 .04 .01 .02 .00 .00 -.01 -.02 -.02 -.03 -.04 -.04 -.06 -.05 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 34
  • 35. Fig. 2: Proportions of Decomposed Cycles in Market Indices RTCLEGYPT RTCLJORDAN RTCLKUWAIT RTCLLEBANON .004 .16 .006 .25 .20 .003 .12 .004 .15 .002 .08 .002 .10 .001 .04 .05 .000 .00 .000 .00 -.05 -.002 -.001 -.04 -.10 -.002 -.08 -.004 -.15 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 RTCLMOROCCO RTCLNBAD RTCLOMAN RTCLSAUD .0015 .016 .008 .006 .012 .006 .004 .0010 .008 .004 .002 .0005 .004 .002 .000 .000 .000 .0000 -.002 -.004 -.002 -.0005 -.008 -.004 -.004 -.0010 -.012 -.006 -.006 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 RTCLTUNISIA RTCLTURKEY .0008 .16 .12 .0004 .08 .0000 .04 -.0004 .00 -.0008 -.04 -.0012 -.08 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 35
  • 36. Fig. 3: Trends and Cycles for the cointegrated Levant markets Panel A: long term trends 11 10 9 CLJORDAN 1.0 8 0.8 7 0.6 6 0.4 0.2 5 98 99 00 01 02 03 04 05 06 07 0.0 TRLJORDAN TRLLEBANON TRLTURKEY -0.2 -0.4 -0.6 98 99 00 01 02 03 04 05 06 07 Panel B: short term cycles CLJORDAN CLLEBANON 1.0 2.0 0.8 1.5 0.6 1.0 0.4 0.2 0.5 0.0 0.0 -0.2 -0.5 -0.4 -0.6 -1.0 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 CLLEBANON CLTURKEY 2.0 1.6 1.5 1.2 1.0 0.8 0.5 0.4 0.0 0.0 -0.5 -0.4 -1.0 -0.8 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 36 CLTURKEY 1.6
  • 37. Figure 4: Trends and Cycles for the North Africa markets Panel A: Long term trends 9.2 8.8 8.4 8.0 7.6 7.2 CLEGYPT 6.8 .03 6.4 .02 6.0 5.6 98 99 00 01 02 03 04 05 06 07 .01 TRLEGYPT TRLMOROCCO TRLTUNISIA .00 -.01 -.02 Panel B: Short term cycles 98 99 00 01 02 03 04 05 06 07 CLEGYPT CLMOROCCO .03 .010 .008 .02 .006 .01 .004 .002 .00 .000 -.002 -.01 -.004 -.02 -.006 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 CLMOROCCO CLTUNISIA .010 .006 .008 .004 .006 .002 .004 .000 .002 -.002 .000 -.004 -.002 -.004 -.006 -.006 -.008 98 99 00 01 02 03 04 05 06 07 98 99 00 01 02 03 04 05 06 07 CLTUNISIA .006 .004 37 .002 .000
  • 38. Table 1: Financial Indicators for the MENA Stock Markets (2007 Q4) No of Market Turnover Exchange Companies Cap T. Value T. Volume Ratio P/E P/B Div.yield GCC Abu Dhabi 64.0 121128.0 25730.0 23659.0 21.2 16.3 2.9 2.7 Bahrain 51.0 27016.3 464.8 413.5 1.7 13.1 2.1 3.7 Dubai 55.0 138179.0 53983.0 52868.4 39.1 16.5 2.9 1.7 Kuwait 196.0 135362.0 27878.0 16622.0 20.6 . . . Oman 125.0 23086.0 2747.0 1334.7 11.9 14.7 4.0 2.5 Qatar 40.0 95505.0 13487.6 1482.7 14.1 . . . S. Arabia 111.0 518984.0 147565.0 14132.5 28.4 23.2 4.3 . Levant Jordan 245.0 41232.5 4728.9 1264.5 11.5 28.0 3.0 1.8 Lebanon 15.0 10894.0 402.7 72.8 3.7 . . . Palestine 35.0 2473.6 183.9 62.8 7.4 . . . Turkey 319.0 286570.0 93255.0 129.7 13.59 1.96 North Africa Egypt 435.0 138828.0 23924.0 5824.0 17.2 . . . Morocco 73.0 75494.5 11869.7 140.5 15.7 33.8 . 2.1 Sudan 52.0 4931.1 191.4 18.7 3.9 . . . Tunisia 51.0 5338.9 310.0 25.5 5.8 16.2 1.7 2.9 Source: Arab Monetary Fund Notes: Algeria is not included in this table because it has only two firms listed. Market capitalization, trading value and trading volume are in million USD. Trading Turnover, which is a measure of liquidity, is 167 for NYSE, 303 NASDAQ, 154 London, 394 Shenzhen, and 138 Tokyo 38
  • 39. Table 2: Descriptive Statistics for Market Indices, Interest Rate and Oil prices   LKUWAIT LNBAD LOMAN LSAUD LJORDAN LLEBANON LTURKEY Mean 9.3838 7.2526 8.9311 6.9889 7.8808 6.5599 9.5995 Std. Dev. 0.8413 0.6651 0.4495 0.7628 0.6237 0.4165 0.5708 Skewness 0.2524 0.62 0.4318 0.4031 0.6615 0.4166 0.0533 C.V 0.0897 0.0917 0.0503 0.1091 0.0791 0.0635 0.0595 Jarque-Bera 55.4009 57.844 40.7808 40.4597 68.9661 39.4083 22.854 Observations 485 485 485 485 518 518 518   LEGYPT LMOROCCO LTUNISIA LFFR LFTSE LSP500 LWTI Mean 7.3207 6.2455 6.629 1.1566 8.5895 7.087 3.5059 Std. Dev. 0.8415 0.438 0.3458 0.6382 0.1645 0.1504 0.52 Skewness 0.3813 1.0964 0.0066 -0.6738 -0.6306 -0.4261 -0.0566 C.V 0.1149 0.0701 0.0522 0.5518 0.0191 0.0212 0.1483 Jarque-Bera 36.7481 112.0804 1.6236 65.3506 45.2176 18.5196 14.988 Observations 518 518 518 518 518 518 518 Notes: The statistics are for log of levels. The weekly sample period for the GCC markets spans the period from Aug. 25th, 1998 to Dec. 4th, 2007, while covers the period Jan. 6th, 1998 to Dec. 4th, 2007 for the markets in the Levant and North Africa regions. The GCC in this study includes Kuwait, Oman, and Saudi Arabia. The Levant covers Jordan, Lebanon and Turkey, while North Africa includes Egypt, Morocco and Tunisia. J-B statistic is significant for all courtiers except Tunisia 39
  • 40. Table 3: Contemporaneous Correlations among Market Indices, US Interest Rate and Oil Price over the Sample Period LKUWAIT LNBAD LOMAN LSAUD LJORDAN LLEBANON LTURKEY LEGYPT LMOROCCO LTUNISIA FFR LFTSE LS&P LWTI LKUWAIT 1.00 LNBAD 0.94a 1.00 a LOMAN 0.91 0.93a 1.00 a a LSAUD 0.94 0.93 0.83a 1.00 a a a LJORDAN 0.96 0.99 0.92 0.95a 1.00 a a a a LLEBANON 0.68 0.80 0.86 0.63 0.69a 1.00 a a a a a LTURKEY 0.61 0.67 0.79 0.62 0.67 0.76a 1.00 a a a a a a LEGYPT 0.80 0.88 0.95 0.76 0.86 0.89 0.89a 1.00 a a a a a a a LMOROCCO 0.74 0.73 0.90 0.59 0.73 0.84 0.79 0.90a 1.00 a a a a a a a a LTUNISIA 0.64 0.54 0.65 0.65 0.61 0.33 0.63 0.60 0.62a 1.00 a a a a a a LFFR -0.04 0.17 0.30 -0.01 0.07 0.64 0.55 0.50 0.45 0.14a 1.00 a a b a a a a a LFTSE -0.02 0.14 0.31 -0.01 0.10 0.59 0.63 0.52 0.52 0.27 0.90a 1.00 a a a a a a a a a a a LSP500 0.18 0.27 0.47 0.18 0.30 0.54 0.78 0.64 0.64 0.58 0.71 0.89a 1.00 a a a a a a a a a a LWTI 0.87 0.81 0.81 0.90 0.83 0.44 0.65 0.70 0.60 0.85 -0.01 0.04 0.35a 1.00 Notes: The correlations are for log of levels. The weekly sample period for the GCC markets spans the period from Aug. 25th, 1998 to Dec. 4th, 2007, while covers the period Jan. 6th, 1998 to Dec. 4th, 2007 for the markets in the Levant and North Africa regions. The GCC in this study includes Kuwait, UAE, Oman, and Saudi Arabia. The Levant covers Jordan, Lebanon and Turkey, while North Africa includes Egypt, Morocco and Tunisia. The symbols a, b and c represent statistical significance at the 1%, 5% and 10% levels, respectively 40
  • 41. Table 4: GARCH Models for GCC Region’s Stock Market Index Cycles   Kuwait UAE Oman S. Arabia Mean Equation C 0.001927 a 0.002362 0.001274 -0.000179 CLKUWA(-1) 0.139534 a 0.100718 0.509005 a 0.384683 a CLNBAD(-1) 0.164492 a 0.448341 b -0.104149 -0.144692 a CLOMAN(-1) 0.03111 0.050543 0.291779 b -0.091602 CLSAUD(-1) -0.119773 a 0.208013 0.18427 c 0.358489 a DTRLKUWA -0.159184 a -0.149054 a -0.042058 a DTRLNBAD -0.091678 a -0.104869 a 0.062588 a DTRLOMAN -0.11961 a -0.19591 a -0.042978 a DTRLSAUD -0.093106 a -0.183211 a -0.13638 a D00 -0.001363 a -0.000508 0.000397 0.000164 DLWTI 0.002993 0.000542 -0.010198 -0.001434 DLSP500 -0.009253 c -0.007845 0.011991 -0.010121 b Variance Equation C 2.15E-06 b 5.11E-05 c 2.24E-05 b 3.65E-06 a RESID(-1)^2 0.226198 a 0.148847 0.148019 0.226855 a GARCH(-1) 0.604085 a 0.596768 b 0.596019 a 0.688517 a D00 -6.22E-07 -1.48E-06 -5.08E-06 -1.17E-06 DLWTIV -9.82E-05 -0.001751 a -0.000558 a -4.86E-05 DLSP500V 0.00038 -0.004033 b -0.001681 a -0.000592 a α+β 0.83 0.75 0.74 0.92 Half-life(weeks) 3.73 2.36 2.34 7.84 Adj. R^2 0.91 0.84 0.87 0.29 Log-Likelihood 2155.81 1630.68 1838.76 1939.90 F-Stats 319.23 163.84 199.05 13.19 DW 2.06   1.84   2.05   2.06   Notes: a, b an c represent significance at 1%, 5% and 10%, respectively. DLWTI in the mean equations is the first log difference of oil price, while DLWTIV is the squared deviation between the oil price and its mean as a measure of oil volatility in the variance equations. D00 stands for the change in OPEC pricing mechanism in 2000 41
  • 42. Table 5: GARCH Models for Levant Region’s Stock Market Index Cycles   Jordan Lebanon Turkey Mean Equation C 0.007276 a 0.012047 a 0.011565 a CLJord(-1) 0.068116 0.026254 -0.010304 CLLeba(-1) 0.300653 0.395231 a 0.371849 CLTurk(-1) 0.303563 b 0.729863 a 0.519869 a DTRLJord -0.966445 a -0.365837 a DTRLLeba -0.950912 a -0.718531 a DTRLTurk 0.032234 a -0.133711 a D00 -0.005075 a -0.008895 b -0.008901 b DLWTI -0.004885 -0.009115 -0.008696 DLSP500 0.055236 b 0.112312 b 0.097001 b Variance Equation C 3.76E-06 3.17E-05 1.12E-05 RESID(-1)^2 0.080432 a 0.150188 a 0.078536 a GARCH(-1) 0.883098 a 0.724798 a 0.884769 a D00 2.57E-06 3.12E-05 c 8.10E-06 DLWTIV 5.68E-05 0.005496 0.000233 DLSP500V 0.001586 0.010242 0.004795 α+β 0.96   0.87   0.96   Half- 18.66 5.19 18.54 life(weeks) Adj. R^2 0.9983 0.9981 0.9971 Log-Likelihood 1507.12 1205.67 1217.68 F-Stats 21718.30 19507.87 12670.21 DW 2.10   2.17   2.11   Notes: See Table 2 for similar notes. 42
  • 43. Table 6: GARCH Models for North Africa Region’s Stock Market Index Cycles   Egypt Morocco Tunisia Mean Equation C 0.000608 a 0.000199 a 6.59E-05 CLEgyp(-1) 0.032554 -0.004924 -0.059269 CLMoro(-1) 0.104525 0.128245 0.130395 CLTuni(-1) -0.065636 -0.054247 b 0.060135 c DTRLEgyp -0.029937 a -0.013825 a DTRLMoro -0.079582 a 0.035726 a DTRLTuni 0.053211 a 0.007366 a D03 -0.001193 a -0.000163 b -0.000127 DLWTI -0.004574 0.00057 -0.001511 b DLSP500 -0.006008 0.002237 c -0.000552 Variance Equation C 3.82E-07 9.17E-08 a 1.93E-07 a RESID(-1)^2 0.126398 a 0.392473 a 0.220958 a GARCH(-1) 0.819177 a 0.43598 a 0.656107 a D03 2.27E-07 5.51E-08 c -7.29E-08 c DLWTIV 8.16E-05 -2.12E-06 -1.42E-05 b DLSP500V 1.97E-05 3.89E-05 c 6.69E-05 α+β 0.95   0.83   0.88   Half-life(weeks) 12.39 3.68 5.28 Adj. R^2 0.35 0.75 0.44 Log-Likelihood 2213.41 3009.54 2843.36 F-Stats 20.67 108.87 29.45 DW 2.00   2.21   2.16   Notes: The same as in Table 1. D03 stands for the 2003 Iraq war dummy. 43

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