# Table panels a amp b reports

Table 6 (panels a & b) reports the estimation results of a GARCH (1,1) process for all the returns series under the assumption that Fosaprepitant dimeglumine salt the innovations follow either a normal distribution, Student-t or skewed Student-t . The coefficients of the conditional variance equation are significant at 1% level implying a strong support for the ARCH and GARCH effects. Furthermore, the condition for the existence of conditional variance is justified since for all the index returns we have that α1+β1<1α1+β1<1. Interestingly, all the model selection criteria (i.e. maximized log-likelihood function, AIC and SIC) indicate that AR (1)-GARCH (1,1) model under a skewed Student-t distribution provides the best fit for each of the seven index returns. It is noteworthy, however, that the sum of the parameters estimated by the variance equation is close to one. A sum α1+β1α1+β1 near one is an indication of a covariance stationary model with a high degree of persistence in the conditional variance. The sum α1+β1α1+β1 is also an estimation of the rate at which the response function decays on daily basis. Since the rate is high, the response function to shocks is likely to die slowly. For instance, in the case of Dow Jones Index, under skewed Student-t distribution, α1+β1=0.99α1+β1=0.99 which means nektonic organisms a month after an initial shock 74% (or 0.9930) of the impact remains in effect. Even six months later, 16% (or 0.99180) of initial shock remains persistent. The evidence of high volatility persistence and long memory in the GARCH (1,1) model suggest that a FIGARCH (p,d,q) model may be more adequate to describe the data.