Volatility Regime-Switching and Linkage among GCC Stock Markets

Transcription

1 Paper Prepared for the 11 th ERF Conference on December 16-18, 2004 Volatility Regime-Switching and Linkage among GCC Stock Markets Shawkat Hammoudeh Drexel University Philadelphia, PA Kyongwook Choi Ohio University Athens, OH Abstract. The GCC stock markets vary in terms of sensitivity to the magnitude of return volatility and the duration of volatility, regardless of the volatility regime and the return component. Among the GCC market, risk-averse investors and traders in the Oman and Saudi Arabia markets should particularly demand higher premiums for the extra volatility sensitivity during fad times than investors in the other markets. In terms of duration of volatility, investors and policy makers in the Kuwait, Bahrain and Saudi Arabia should be aware of the longer duration of this volatility during the fad times. All GCC returns move in the same direction whether in terms of total return, fundamentals or fads under both volatility regimes. Correlations of the stock returns and their components with each other and with the oil price return are also weak, suggesting that country particularities in addition to the oil price return influence the stock component returns. JEL Classification: C22; F3; Q49 Keywords: Volatility; Markov switching; Permanent and transitory components; Transition Probability 1

2 Volatility Regime-Switching and Linkage among GCC Stock Markets 1. Introduction Fads or speculative attacks are short-lived phenomena that affect the world s stock markets such as the October 1997 crash of the US market and the 1982 crash of the Kuwait market. In these crashes the markets experience large drop in stock prices and dramatic jump in volatility. Shocks in the fads are caused by noisy trades how bid prices away from the fundamentals due to changes in price misperceptions (De Long et al, 1990). Although the fad volatility usually reverts to normal levels quickly, this transitory volatility can cause tremendous damage to wealth and social wellbeing. Moreover, increases in risk would raise the cost of capital and may retard economic growth in the long-run. Therefore, it is important to consider an economic variable such as a stock return in terms of its permanent or fundamental component, and its transitory or fad components to determine the expected durability of the fad volatility relative to the fundamental volatility and examine the impact of each of these components on the volatility of the return. The recent literature also studies the decomposition of the stock return within the state-space framework that allows for volatility transition between regimes for the return itself and for each of its components. Several authors have proposed different methods of decomposing a time series into permanent and transitory components. Nelson and Plosser (1982) matched a model consisting of transitory and permanent components to an autocorrelation function to determine the relative sizes of these two components. Watson 2

3 (1986) and Clark (1987) used the conventional unobserved component model (without Markov-switching) to decompose GNP into these two components. Campbell and Mankiw (1987), employing an ARMA representation of a time series, estimated the impact of a shock on long-run forecasts to weigh up he relative importance of the two components. More recent methods examined the decomposition by focusing on mean reversion in stock returns. Fama and French (1988) used an autoregressive test and found mixed results on the existence of mean reversion in the transitory and permanent components. Kim and Kim (1996) and Kim and Nelson (1999) examined the relative importance of the two components within the framework of the space state model with Markov-switching heteroscedasticity. This model can capture the short term dynamics that might not otherwise be captured by the other methods such as the autoregression test of Fama and French (1988) and the conventional unobserved component models of Watson (1986) and Clark (1987). Bhar and Hamori (2004) applied Kim and Kim (1996) model to the some of OECD countries. Other studies that use the space-state model with a Markov regimeswitching process to model volatility and shifts in return regimes but without the component decomposition include Hamilton and Susman (1994) 1, McCarthy and Najand (1995), Chu et al (1996), Schaller and van Norden (1997) and among others. This study uses the empirical model of Kim and Kim (1996) and Bhar and Hamori (2004) to examine the volatility of the decomposed stock returns of members of the Gulf Cooperation Council (GCC). The six-member GCC includes: Bahrain, Kuwait, 1 For earlier research, see Hamilton (1989), Turner et al (1989) and Glosten et al (1993). 3

4 Oman, Qatar, Saudi Arabia and United Arab Emirates (UAE) 2. The market capitalization of the GCC markets as a group was about US$ 172 billion at the end of 2002, and since then has been rapidly rising because of high oil prices and of strong movement towards privatization. These markets have strong future gain potential because of their ownership of huge oil reserves. They together account for 16% of the world output and possess 47% of the world s oil reserves. In 2002 when most of the world s stock markets dropped drastically and realized huge losses, most of the GCC countries made substantial gains and have continued this strong gain through Recent research on the GCC stock markets uses the error-correction model to examine co-movements and interactions of the returns (Hammoudeh and Eleisa, 2004; and Malik and Hammoudeh, 2004). No attempt has been made to examine the mean reversion of the two components that make up the returns of these GCC markets while allowing for volatility regime switching. Therefore, the desired objectives of this paper can be summarized as follows: 1. To decompose the stock returns of the GCC stock markets into permanent and transitory components; 2. To measure the switch in volatility between the high and low variance regimes for both the permanent and transitory components of the stock returns; 3. To measure the expected duration of the volatility, in terms of trading weeks, of the high and low variance regimes of the transitory component. 2 Qatar is not included because its stock market was established in 1997, which does not provide an appropriate long enough time series. 4

5 4. To measure the correlation between the volatilities of the individual GCC stock markets to ascertain whether or not these markets move in the same or opposite direction; and 5. To measure the correlation of volatility of the individual GCC stock markets and oil markets to determine if they move in the same direction. The findings suggest that there are two significant volatility-switching regimes in both components of the stock returns for all the GCC countries. They also show differences in the sensitivity to the return volatility and the duration of volatility across regimes and return components and across the markets. In particular the results emphasize the sensitivity of the Oman and Saudi markets to shocks during fad times in the high volatility regime which is of particular interest to this study. Shocks also persist longer in the Kuwait, Bahrain and Oman markets in the high volatility fad regime. We should also note that shocks in the fundamentals have very long expected durations exceeding the durations of shocks in the fads. Overall, these results suggest that the GCC countries are very different when it comes to return on financial investment. Thus, investors should do their homework before investing in these countries. Moreover, the persistence and magnitude of extra volatility in certain markets (e.g., Oman and Kuwait) calls on policy makers to introduce financial hedge instruments (e.g., options, futures) to help investors ride the volatility waves that could persist for several months. They should reduce volatility because of its impact on the cost of capital and economic growth. Traders should also demand higher premiums for investing in stock markets that are relatively more volatile such as the Oman market. The findings will provide traders and 5

6 investors in the GCC markets with information that may enable them to distinguish between markets and ask for higher compensations in some markets than others. The results also indicate that the markets are not highly correlated in the return itself, and the permanent and transitory components between the GCC markets, compared for example with Germany, Japan, UK and the United States (Bhar and Hamori, 2004). These results suggest there are some gains from portfolio diversification among particular GCC markets particularly between Saudi Arabia and UAE, and between and Oman and Bahrain. The correlations of the returns and their components with the oil price for the countries are also weak, suggesting that country particularities also influence the component returns. The weakest correlation is between the oil price return and the Oman and Kuwait total returns. The Kuwait market has a negative correlation with the oil price return in the fads, emphasizing the importance of speculative attacks and the presence of hot hands in this market. This information is useful for local as well as for international investors 2. Descriptive Statistics The Gulf Cooperation Council (GCC) consists of six members including Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. We used in this study weekly time series on the Bahrain Stock Exchange index (BSE), Kuwait Stock Exchange index (KSE), the Oman Muscat Securities Market index (MSM), the Saudi stock market index (Tadawal) and the UAE National Bank of Abu Dhabi index (NBAD). As indicated above, the market capitalization of the GCC markets as a group was about US$ 172 6

7 billion at the end of These stock markets display low to moderate valuations compared to the stock markets in the United States and other major world markets (Hammoudeh and Eleisa, 2004) The series for these indices were directly obtained from the respective stock exchanges. The data for these series covers the period February 15, 1994 to December 25, The sample period was determined primarily by the availability of the data on the five Gulf equity markets. Still, this sample period includes the Mexican 1994 crisis, the July 1997 East Asian crisis, the 1998 collapse of oil prices, the 1999 oil price and Asian economy recovery, the adoption of the target zone oil pricing mechanism in February 2000, and the New York September 2001 bombing. The rate of return on for each country s each stock index is calculated as a log-differenced prices, rt = (log( Pt ) log( Pt 1)) 100, where Pt is the stock price index for each country. Comparing the volatilities of the five GCC stock indices and the Dow as defined by the coefficient of variation, Table 1 shows that the GCC stock returns on average are less volatile than the DOW which could be due to the isolation of these markets and the difference in the types of traders participating in them; the only exception is the Omani index (MSM) which is the most volatile of them all. It also shows that the six stock returns are generally more volatile than the five spot and futures oil price returns. However, the US DOW return is highly skewed to the left, while the GCC returns are moderately skewed to the right. This means that there is a higher probability for investors to get positive returns from the GCC markets rather than negative returns, as is the case in developed and some other emerging markets (Harvey and Siddique, 1999). An expectation of positive rather than negative returns in a portfolio of oil-sensitive GCC 7

8 stocks is a manifestation of anticipated higher compensation for the higher risk associated with a narrowly diversified portfolio. A stylized fact of individual financial time series is that they are non-stationary in levels and stationary in the first differences; that is, they are I(1). In particular, shocks in the level of an I(1) series are permanent whereas shocks to the first difference are transitory. Two unit root tests, namely the augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test, are utilized to test for the I(1) property 3. Both of these tests investigate the presence of a stochastic trend in the individual series. The tests are first conducted in the natural logarithms of the levels of the spot oil price variable and the five GCC stock index variables. Both tests show that all of the oil and financial series are non-stationary in levels at the 5% significance level. They are then carried out in first differences of the logarithms and the results of the tests suggest that all of the individual series in first differences are stationary at the 5% significance level 4. In conclusion, all the series have a single unit root or are integrated of degree one, I (1). Thus, all classical regressions using the level data, instead of first differences, will produce spurious estimation results. 3. Empirical Model As indicated above, we use the unobserved-component model with Markovswitching heteroskedasticity (UC-MS model) by Kim (1993) and Kim and Kim (1996). There are several benefits to adapting this model. First, we can incorporate regime shifts 3 We also used the KPSS test (1992), which confirmed that all the individual series are I(1), except MSMI. 4 The results of both the ADF and PP test are available on request. 8

9 in variance structures within the permanent and transitory framework. Second, Kim (1993) points out that the ARCH and Markov-switching heteroskedasticity is that in the case of the former the unconditional variance is constant but the latter for the unconditional variance itself is subject to the regime change. Kim (1993) applied the model to investigate the link between inflation and its uncertainty. He assumes that inflation consists of a permanent and transitory component and decomposes two components by UC-MS model. Kim and Kim (1996) also use the UC-MS heteroskedasticity of stock returns. Their model is as follows: P = P + z (1) *, t t t * * 2 Pt = µ + Pt 1 + et, et ~ N(0, σet ), (2) z =Ψ Lu u N σ (3) 2 t ( ) t, t ~ (0, ut ), where P t is the natural log of stock price and * Pt is the fundamental (permanent) component and zt is fad (transitory) component. The stock return is given by r = P P = µ + e + ( z z ). (4) t t t 1 t t t 1 Equation (4) suggests that the stock return series consists of a constant mean plus noise and transitory component z t. They assume that z t follows AR(2) process and propose a model using UC-MC model by r t zt = µ + [1 1] et, z + t 1 (5) zt φ1 φ2 zt 1 ut. z = t z t 2 0 (6) Equation (5) and (6) is called the measurement equation and transition equation respectively and we can rewrite equation (5) and (6) as the matrix form 9

10 y = µ + Hβ + e (7) t t t β t Fβt 1 vt = + (8) where Markov-switching variances are two shocks related to the permanent and transitory components. σ = (1 S ) σ + S σ, (9) et 1t et 1t e1 σ = (1 S ) σ + S σ, (10) ut 2t u0 ut 21 where the two independent unobserved state variables, S 1t and S 2t, evolve according to first order, discrete, two-state Markov processes with following transition probabilities which determine the regime. [ ] [ ] Pr S = 0 S, = 0 = p,pr S = 1 S, = 1 = p 1t 1 t t 1 t 1 11 Pr 0 0,Pr 1 1 S = S = q S S q 2t 2, t 1 = 00 = = = 2t 2, t 1 (11) 11 We can estimate the parameters by Kalman filter and Kim (1993) s mixed collapsing method. For more detail estimation procedure, refer to Kim and Kim (1996), and Kim and Nelson (1998). To identify the permanent and transitory component and its relationship within GCC countries, we set the model as follows: r = τ + c (12) t t t τ = µ + ( Q + QS ) ε, ε N(0,1), (13) t 0 1 1t t t φ ( Lc ) = ( h + hs ) e, e N(0,1), (14) t 0 1 2t t t where τ t is the permanent part of the return and ct is the transitory (or temporary) part of the return and we assume that it follows AR(1) process. The parameter h1 and Q1 indicate 10

11 the variance changes during periods of high variance state. The two independent unobserved state variables, S1t and S 2t, evolve according to first order, discrete, two-state Markov processes with following transition probabilities which determine the regime. [ ] [ ] Pr S = 0 S, = 0 = p,pr S = 1 S, = 1 = p 1t 1 t t 1 t 1 11 Pr 0 0,Pr 1 1 S = S = q S S q 2t 2, t 1 = 00 = = = 2t 2, t 1 (15) 11 The difference between our model and Kim and Kim (1996) model is that we assume the AR(1) process of transitory components because AR(2) parameter is not statistically significant for all GCC countries Empirical Results The estimates of the models suggest that two volatility return regimes exist in the spot oil market and the stock markets of all the GCC countries except for Oman in the low volatility state. This finding confirms the validity of using the Markov-switching process in examining the return volatility in these markets. This finding is evident from the statistical significance of the variances for both the low and high volatility regimes of the two components as shown in Table 2. The estimates of transition probabilities for the two regimes of both the permanent and transitory components are statistically significant at the 1% level for all the countries. We find that the permanent component high volatility regime variance, Q 1, is statistically significant for all countries. That is, when the economy moves into the high volatility state, the variance of the permanent 5 More recently Bhar and Hamori (2004) use the same model as ours and applied their model to the stock markets of Germany, Japan, UK and the US. 11

12 component of the return increases for those countries significantly. We can evaluate the magnitude of the overall variance of the permanent component variances by the adding the low and high state variances. The permanent component of oil price return shows the highest variance. The estimates of the parameters of the weekly transitory component suggest several implications. First, the transitional probability for both high and low volatility regimes are statistically significant for all countries and the oil price returns. Second, in our sample, the transitory low variance state, q00 is higher than q 11 which suggest that the low volatility state dominates the high volatility state. In other words, the transitory shock is short lived. The expected durations of the high volatility state (of transitory component) for Bahrain, Kuwait, Oman, UAE, and Saudi are 22.2, 62.5, 7.0, 4.6 and 3.4 weeks, respectively as shown in Table 3, having an average of about On the other hand, the expected durations of the low volatility state (of transitory component) for Bahrain, Kuwait, Oman, UAE, and Saudi are 66.7, 250.0, 15.2, 18.5 and 27.0 weeks, respectively, having an average of about 75.4 weeks. The high volatility state variance is higher than the low volatility state variance for all countries and it is clear that during the high volatility state the uncertainty is much higher for those stock markets. Table 4 shows the weekly correlation patterns for the return itself and its permanent and transitory components between individual GCC countries. The correlation patterns are positive for all the three returns measures, implying that these returns move together in the short- and long-runs. This should not be surprising because these countries are located in the same geographical region and share many common social and economic characteristics including high dependence on the oil revenues. The highest 12

13 correlation in returns is between Bahrain and UAE (0.229), and between Bahrain and Kuwait (0.227), implying that these two countries have the highest, same direction movements in returns which makes these markets the least eligible candidates among the GCC markets for portfolio diversification. Kuwaiti and UAE companies are listed on the Bahrain stock Exchange. Still these return correlations are significantly low if compared to those between Germany, Japan, UK and the US as reported in Bhar and Hamori (2004), in the light of the GCC s high dependence on oil. The lowest return correlation is between Saudi and UAE (0.054), implying that these two countries are better candidate to be combined in diversification-based portfolios when returns are in the high volatility regime. However, the correlations for the permanent components provide different results. The highest fundamental correlation is between Kuwait and Saudi Arabia (0.173). This may be explained by the relatively high correlation between these countries fundamentals and that of the oil price. The lowest fundamental correlation is between Saudi Arabia and UAE. In contrast to Saudi Arabia, the UAE fundamental has very low correlation with the oil price returns. In terms of the transitory component, the highest correlation is between Bahrain and Kuwait (0.250), and the lowest is between Oman and Saudi Arabia (0.038). As mentioned above, the GCC weekly return correlations with the oil spot price return are surprisingly low for all GCC countries. This means that the oil price is only one factor that moves the GCC stock markets on a weekly basis. The highest correlation with the oil price return is for Saudi Arabia, which is the largest oil exporter in the world. This correlation in terms of: the stock return itself is 0.203; the fundamental is 0.166; and the transitory is The lowest oil correlation for the fundamental is for UAE which is 13

14 also affected by regional tourism as well as by oil revenues. UAE has only one emirate among its six united emirates that is a major oil exporter. It is surprising that Kuwait has the lowest correlation for the return itself. However, it is possible that on weekly basis and in a market that is highly sensitive to fads that the oil connection is weak. 5. Conclusions Since the study clearly shows that there exist two volatility regimes in the two components of all stock returns of the GCC countries, then risk-averse investors should demand different compensations depending on the state of the economy and the shocks in the components. Those GCC investors should ask for higher compensation in the high volatility state regardless whether the shock hits the fundaments or the fads. Moreover, sensitivity to return volatility during fad times is much higher than the volatility sensitivity due to shocks in the fundamentals regardless of the return regime. It seems that at times of increases in fads and speculative attacks noisy traders experience changes in price misperceptions and that considerably increases the risk in all the GCC markets. Thus those investors should ask for much higher premiums during fad times.. Since sensitivity to return volatility varies across the GCC markets depending on the state of the economy and the component of the return, the risk-averse investors in the Oman market should particularly ask for the highest premium among the five GCC markets during the high volatility state of the fad component, followed by investors in the Saudi and the UAE markets. The Omani market should consider introducing financial hedge instruments that can protect investors during fad times. The lowest fad premium 14

15 should go to investors in the Bahrain market which is more integrated with the world stock market than the other GCC markets. The spot oil market plays a very important factor in determining the returns of the GCC stocks particularly during changes in the fundamentals and the fads. The additional oil variance for both components is higher than that for most of the GCC markets. This may explain why Bahrain, which is basically a non oil producing country, has the lowest volatility sensitivity during fads. The GCC markets also vary in the duration of volatility across regimes and for the two components. Oman and Saudi Arabia have longer volatility durations as a result of shocks in the fundaments such as the oil market than all those markets. In this case risk averse investors and traders in these two countries should opt for longer term investments than in the other market to ride the volatility. Macroeconomic policy makers in these countries should also be aware of the longer volatility and makes policies in times of fads that stabilize the stock markets especially during speculative attacks in the oil market. In terms of movements of the returns, all GCC returns move in the same direction whether in terms of total return, fundamentals or fads under both volatility regimes, suggesting that they are commoved by a common factor such as political stability, liquidity and/or the oil price in the short and long-runs. The highest movements are between Kuwait and Bahrain, and Bahrain and UAE which makes these markets the least eligible candidates for portfolio diversification among the GCC markets. There are also relatively high correlation between Kuwait and Saudi Arabia. Overall the correlations among the GCC whether in terms of to the return itself, the fundamental and the fad are low, suggesting that these countries are very different when it comes to return on 15

16 financial investment. Thus, investors should do their homework before investing in these countries. Saudi Arabia has the highest correlation with the spot oil price but in general the oil correlation weak, confirming the above point that there are country particularities that influence the stock returns in addition to the oil price. 16

17 References: Bhar, R. and Hamori, S Empirical characteristics of the permanent and transitory components of stock returns: Analysis in a Markov-switching heteroscedasticity framework. Economic letters, forthcoming. Campbell, J. Y and Mankiew, N. G Are output fluctuation transitory? Quarterly Journal of Economics 102, Chu, C.-S., Santoni, G. J., and Liu, T Stock market volatility and regime shifts in returns. Information Sciences 94, Clark, P. K The cyclical component of the US economic activity, Quarterly Journal of Economics 102, De Lond, J. B., Shleifer, A., L. H., Summes, L. H. and Waldmann, R. J., Noise trader risk in financial markets. Journal of political economy, 98, Fama, E. F. and French, K. R Permanent and temporary components of stock prices. Journal of political economy, 96, Glosten, L. K., Jagannathan, R. and Runkle, D. E On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48(5), Hamilton, J. D A new approach to the economic analysis of nopnstationary time series and the business cycle. Econometrica 57(2), Hamilton J.D. and Susmel, R., Autoregressive conditional heteroscedasticity and changes in regime. Journal of Econometrics 64, Hammoudeh, S. and Elesia, E Dynamic relationships among GCC stock markets and NYMEX oil futures. Contemporary Economic Policy 22 (2), Hammoudeh, S. and Malik, F. 2004, Shock and volatility transmission in the NYMEX oil, US and Gulf equity markets. Paper presented at the Middle East Economic Association Meeting, San Diego, CA. Harvey, C. R. znd A. Siddique, Autoregressive conditional skewness. Journal of Financial Quantitative Analysis 43, Kim, C. J Unobserved-component time series models with Markov-switching: Changes in regime and the link between inflation rates and inflation uncertainty. Journal of Business and Economic Statistics 11,

18 Kim, C.J., and Kim, M. J Transient fads and the crash of 87. Journal of Applied Econometrics 11, Kim, C. J., and Nelson, C. R State Space Models with Regime Switching, Classical and Gibbs Sampling Approach with Application. The MIT Press, Cambridge, MA. Nelsson, C. R., and Plosser, C. I Trends and Random walks in macroeconomic time series: Some evidence and implicatio ns. Journal of Monetary Economics 10, McCarthy, J. and Najand, N State space modeling of linkages among international markets. Journal of Multinational Financial Management 5, 1-9. Porterba, J. M. and Summers, L. H Mean reversion in stock prices: Evidence and implications. Journal of Financial Economics 22, Schaller, H. and van Norden, S Regime-switching in stock market returns. Applied Financial Economics 7, Turner, R. F., Startz, R. and Nelson, C. F A Markov model of heteroskedasticity, risk, and learning in the stock market, Journal of Financial Economics. 25, Watson, M. W Univariate detrending methods with stochastic trends. Journal of Monetary Economics 18,

19 Table 1: Descriptive Statistics of the GCC Stock Index and Spot Oil Price Returns Statistics BSEI KSEI MSMI NBADI SAUDI DOWI WTIS Mean Maximum Minimum Std. Dev C.V a Skewness Kurtosis Jarque-Bera Probability Notes: All variables are first differences of logs, and thus they represent rates of return. KSEI: The Kuwait Stock Exchange Index, MSMI: The Muscat Stock Market Index for Oman s Stock Market, NBADI: The National Bank of Abu Dhabi Index for the UAE Stock Market, SAUDI: The Saudi Stock Market Index, BSEI: The Bahrain Stock Exchange Index. WTIS: Spot Price of WTI Crude Oil. a C.V. is called Coefficient of Variation, which is defined as the standard deviation divided by the mean. 19

20 Table 2: Estimation of Permanent and Transitory Components of GCC Stock Return Parameters BSEI KSEI MSMI NBADI SAUDI WTIS ˆµ (0.049) (0.090) (0.093) (0.065) (0.089) (0.207) φ a b a a b (0.090) (0.136) (0.084) (0.066) (0.208) (0.109) Q a a c (0.001) (0.335) (0.083) (0.038) (0.528) (1.177) Q a a a a a b (0.226) (0.241) (0.083) (0.137) (0.289) (0.913) h a a a c c (0.045) (0.231) (0.001) (0.026) (0.528) (0.919) h a a a a a a (0.258) (0.372) (0.361) (0.372) (0.703) (0.849) ˆp a a a a a a (0.126) (0.029) (0.004) (0.033) (0.010) (0.005) ˆp a a a a a a (0.038) (0.031) (0.011) (0.028) (0.005) (0.020) ˆq a a a a a a (0.042) (0.016) (0.070) (0.101) (0.119) (0.018) ˆq a a a a a a (0.016) (0.005) (0.028) (0.022) (0.022) (0.015) Log Likelihood Note that a, b and c denotes rejection of the hypothesis at the 1%, 5% and 10% levels respectively. Standard errors are given in parentheses below the parameter estimates. BSEI: The Bahrain Stock Exchange Index, KSEI: The Kuwait Stock Exchange Index, MSMI: The Muscat Stock Market Index for Oman s Stock Market, NBADI: The National Bank of Abu Dhabi Index for the UAE Stock Market, SAUDI: The Saudi Stock Market Index, WTIS: Spot Price of WTI Crude Oil. 20

21 Table 3: Volatility Duration for Permanent and Transitory Components Duration BSEI KSEI MSMI NBADI SAUDI WTIS Permanent Components Low volatility High volatility Transitory Components Low volatility High volatility Notes: Duration is measured in weeks as 1/(1- probability). BSEI: The Bahrain Stock Exchange Index., KSEI: The Kuwait Stock Exchange Index, MSMI: The Muscat Stock Market Index for Oman s Stock Market, NBADI: The National Bank of Abu Dhabi Index for the UAE Stock Market, SAUDI: The Saudi Stock Market Index, WTIS: Spot Price of WTI Crude Oil. 21

22 Table 4: Correlation Statistics for Return, Permanent and Transitory Components Parameters BSEI KSEI MSMI NBADI SAUDI WTIS Return BSEI 1 KSEI MSMI NBADI SAUDI OIL Permanent Components BSEI 1 KSEI MSMI NBADI SAUDI OIL Transitory Components BSEI 1 KSEI MSMI NBADI SAUDI OIL KSEI: The Kuwait Stock Exchange Index, MSMI: The Muscat Stock Market Index for Oman s Stock Market, NBADI: The National Bank of Abu Dhabi Index for the UAE Stock Market, SAUDI: The Saudi Stock Market Index, BSEI: The Bahrain Stock Exchange Index. WTIS: Spot Price of WTI Crude Oil. 22