-4.710556 Durbin-Watson stat 2.099147 Since we are talking about asset returns, a standard GARCH model may not be the best choice as we would expect there to be an asymmetry in volatility (Brooks 2008, page 404). The EGARCH model would allow negative shocks to have a larger effect on conditional variance than positive shocks. As we can see in the Eviews output below, it is true that negative shocks have a larger effect because the C(4) coefficient is negative. Because we are estimating the logarithm of the conditional variance, unlike the standard GARCH model, it can be more difficult to interpret the exact meaning of all parameters.Dependent Variable: RLSP500 Method: ML - ARCH (Marquardt) - Normal Distribution Date: 07 /29 /12 Time: 20:08 Sample (adjusted): 01/10/2005 01/31/2011 Observations included: 317 after modifications Convergence reached after 35 iterations Presample variance: backcast (parameter = 0.7) LOG(GARCH) = C (2) + C(3)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(4) *RESID(-1)/@SQRT(GARCH(-1)) + C (5)*LOG(GARCH(-1)) Variable coefficient Std. Statistic error z