By Sabur Mollah, Asma Mobarek
Inventory industry integration among constructing and rising markets has a number of merits for making a international - but sturdy - international economic system. It raises pageant and the potency of neighborhood markets, in flip lowering rate volatility and the price of capital between built-in markets. It additionally generates capital flows, which complement monetary balance and spur monetary progress. At its center, inventory marketplace integration has a big function to play in either constructing and rising markets nonetheless reeling from the worldwide monetary crisis.
Global inventory marketplace Integration analyzes the monetary make-up of constructing and rising markets round the area, offering empirical insights into marketplace integration, co-movements in rate, crises, and potency linkages. Mobarek and Mollah argue that the courting among industry integration and industry potency inside of constructing and rising nations isn't the purely degree important for effecting genuine monetary progress. This paintings brings the evaluate of theories and empirical learn at the subject updated and expands the prevailing literature with new views on built and rising markets.
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Additional resources for Global Stock Market Integration: Co-Movement, Crises, and Efficiency in Developed and Emerging Markets
They concluded that the time series from the ISE were a random walk. Moustafa (2004) found that the prices of stocks in the United Arab Emirates stock market were consistent with the weak form of market efﬁciency. The empirical evidence produced on African equity markets is not as abundant as for other emerging markets, with the additional drawback that most of the empirical research has focused on the Johannesburg Stock Exchange Limited (JSE Limited). Thomson and Ward (1995) collected the results from previous studies on the JSE in one extensive review providing mixed evidence; however, they leaned toward concluding that the JSE was actually weak-form-efﬁcient.
As reported in Smith (2008), the maximum value of each test statistic, that is, R1 ( q), R2 ( q), and S1 ( q), is selected and joint test statistics are created, such as the following: JR1 = max |R1 ( qi ) | JR2 = max |R2 ( qi ) | JS1 = max |S1 ( qi ) | The critical values are obtained by simulation. When the calculated joint statistic exceeds the critical value, the null hypothesis is rejected. Empirical Results and Analysis In this section, the empirical results from each test are presented and analyzed in terms of theoretical framework and critical comparison with existing ﬁndings.
The test statistic Z ( q) is formulated under the hypothesis of homoskedasticity, that is, random walk increments have constant variance, whereas Z ∗ ( q) allows for heteroskedasticity and non-normality, which are characteristics often present in stock markets’ returns series, and it is therefore particularly useful for testing the RWH in this case. However, as indicated by Smith (2008), these two test statistics suffer from two limitations: ﬁrst, the Lo and MacKinlay approach tests that an individual VR for a speciﬁc interval q is equal to 1 whereas it is required by the RWH that VR( q) = 1 for all q; second, this approach has low power and poor size properties in small samples.