1. Presented by: Habiba Al-Mughairi School of Social Sciences Brunel University 1

2. Outline of the Presentation Introduction Motivation Contribution Econometric Method Data Description Empirical results Conclusion 2

3. Introduction This paper investigates the asymmetric conditional correlations between the oil market and Gulf Cooperation Council (GCC) stock market returns using Asymmetric Dynamic Conditional Correlation multivariate GARCH model. 3

4. Saudi Arabia, Oman, Kuwait, Qatar, Bahrain, and United Arab Emirates (UAE). Major exporter of crude oil & heavily depend on oil revenues. The GCC stock markets are also likely to be exposed to shocks transmission (economic and political similarities). Strong recovery in oil prices, Marginal tax on capital gains, Low interest rates, Ample liquidity from petro-dollars Recent financial development towards lower restrictions on foreign ownership Overview of the (GCC) countries' economy and stock markets 4

5. Motivation Is the time varying correlation between oil and GCC stock markets be asymmetric? Are GCC stock market returns strongly correlated overtime? and how the recent global financial crisis have affected their correlation dynamics? If increasing assets’ correlation exists, then what are the consequences on international and domestic portfolio diversification? 5

6. Contribution First: Asymmetric Dynamic Conditional Correlations (ADCC-GARCH) model is used. (see Cappiello et al., 2006) Second: All the GCC stock markets are considered when investigating the asymmetric property in conditional correlations in order to better understand the investor’s portfolio and manager’s asset decisions. Third: Extreme global events are considered. Ignoring such events could affect the correlation analysis as the GCC stock markets can be highly affected by global shocks (see Hammoudeh and Li, 2008). 6

7. Econometric Method Two types of multivariate conditional correlations GARCH models are used: The DDC model Engle (2002). The ADCC model Cappiello et al.(2006) The DCC model is estimated even for high-dimensional data set using two-step procedures: 1 st step: the conditional variances are obtained by estimating a series of univariate GARCH models. 2 nd step: coefficients of conditional correlations are estimated. Cappiello et al., (2006) adjust the DCC model by taking into consideration the possibility of occasionally observed events in which the conditional correlation of stock returns is more significantly impacted by negative shocks than it is by positive shocks. 7

8. Why asymmetric conditional correlations? Studies over the past decade have found an empirical evidence of asymmetric time-varying correlation between different classes of assets. The asymmetric dynamic co-movements are mostly due to a rise in correlations of returns between stock market indices during extreme downturn market movements, whereas during upward movements play a marginal role. Asymmetric correlations are increasingly required in financial applications including risk management, asset pricing models, option pricing, hedging, and optimal portfolio allocations (see e.g. Cappiello et at., 2006; Ang and Chen, 2002; Longin and Solnik, 2001). 8

9. Data Description Seven GCC stock indices: Saudi Arabia Stock Exchange (Tadawul), Kuwait Stock Exchange (KSE), Bahrain Stock Exchange (BSE), Muscat Securities Market (MSM), Qatar Exchange (QE), Dubai Financial Market (DFM), Abu Dhabi Securities Exchange (ADX) The Brent crude oil price index. The weekly data covers the period from 07/07/2004 to 27/12/2012, 443 observations. 9

10. Preliminary results Table 1 10

11. Figure 1 Volatility clustering of weekly returns for stock-oil returns 11

12. Preliminary results Variable ARCH LM Test 12 Table 2. ARCH LM Test The null hypothesis of no ARCH effect of Engle LM test (1988) is rejected at lags (2, 5, 10), respectively, for all indices of return series, ARCH effect is present in the data. Justify the use of GARCH-family models. Saudi Arabia F(2,438) = 10.546[0.0000] F(5,432) = 12.367 [0.0000] Kuwait F(2,438) = 22.897 [0.0000] F(5,432) = 18.140 [0.0000] Bahrain F(2,438) = 11.087 [0.0000] F(5,432) = 5.7673 [0.0000] Qatar F(2,437) = 10.786 [0.0000] F(5,431) = 19.199 [0.0000] Oman F(2,437) = 11.240 [0.0000] F(5,431) = 15.937 [0.0000] Abu Dhabi F(2,437) = 4.7312 [0.0093] F(5,431) = 8.7825 [0.0000] Dubai F(2,437) = 1.6327 [0.1966] F(5,431) = 11.786 [0.0000] Brent oil F(2,437) = 27.466 [0.0000] F(5,431) = 35.567 [0.0000]

13. Preliminary results Table 3 13

14. Empirical results Table 4. DCC and ADCC estimated results between GCC stocks and oil returns 14

15. Empirical results The asymmetric term (γ) captured by the ADCC model is statistically significant at 5% level for the stock markets of Dubai, Oman, Qatar, and Saudi Arabia (the correlation with the oil market tends to increase more after a negative shock rather than after a positive shock). As for the symmetric effect, results show the only for the Kuwaiti market the estimated parameters α and β are significant at 5% level The results for the two UAE stock markets (Dubai and Abu Dhabi) are different. The estimated parameters (α, β, γ) of the ADCC model are statistically significant at 5% level only for Dubai. As for the Bahrain stock market, no significant results are found for both symmetric and asymmetric models 15

16. Empirical results Table 5 DCC model estimated results among GCC stock returns 16

17. 17 Empirical results Table 5 DCC model estimated results among GCC stock returns

18. Empirical results Panel A Shows that GCC stock returns exhibit approximately low to medium positive correlations ranging from 0.16 to 0.48, with the exception of Abu Dhabi and Dubai markets which display the highest conditional correlation of 0.75. In addition, we observe that GCC stock markets' correlation with the Dubai stock market is positively higher in magnitude than that in any other market of the region Panel B When controlling for extreme events, the correlations are now lower than in the previous case. In addition, the persistence of shocks to correlations (+) are relatively moderate, and it is slightly lower when the dummies are included in the DCC estimated equation. 18

19. Figure 2 Dynamic correlations among selected GCC stock markets 19

20. Results Implication The results have important economic and financial implications. The low correlations among some of the GCC equity return indices may be an important signal for those investors who want to maximize their profit Investor should be aware of the uncertainty which features these markets given the negative impact that the oil shocks may play. The risk managers should be fully aware of the fact that these markets are not safe from oil shocks 20

21. Conclusion The findings indicate that only four GCC equity indices, Dubai, Oman, Qatar, and Saudi Arabia, display asymmetric movements with the oil market with downward co-movements are more frequent than upward co-movements. All the stock indices considered are positively correlated and exhibit time-dependent movements, especially during the global crisis period. However, the correlation is rather low for some of the stock markets 21

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