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

1. Re-examining the role of oil price shocks in the GCC stock markets: new evidence from asymmetric DCC-GARCH model Presented by: Habiba Al-Mughairi School of Social Sciences Brunel University

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

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.

4. Overview of the(GCC) countries' economy and stock markets • 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

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?

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).

7. Econometric Method • Two types of multivariate conditional correlations GARCH models are used: • The DDC model Engle (2002). The ADCC modelCappielloet al.(2006) • The DCC model is estimated even for high-dimensional data set using two-step procedures: • 1st step: the conditional variances are obtained by estimating a series of univariate GARCH models. • 2nd 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.

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).

9. Data Description Seven GCC stock indices: • Saudi Arabia Stock Exchange (Tadawul), Kuwait Stock Exchange (KSE), Bahrain Stock Exchange (BSE), MuscatSecurities 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.

10. Preliminary resultsTable 1

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

12. Preliminary results Variable ARCH LM Test 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.

13. Preliminary resultsTable 3

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

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

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

17. Empirical resultsTable 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 (𝛼+𝛽) are relatively moderate, and it is slightly lower when the dummies are included in the DCC estimated equation.

19. Figure 2 Dynamic correlations among selected GCC stock markets

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

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

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