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Essay / The Ljung Box Test - 1720
-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 expect that there is an asymmetry in volatility (Brooks 2008, p.404). The EGARCH model would allow negative shocks to have a greater effect on conditional variance than positive shocks. As we can see in the Eviews results below, negative shocks have a greater effect because the coefficient C(4) is negative. As we estimate the log of the conditional variance, unlike the standard GARCH model, it may 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: 8:08 p.m. Sample (adjusted): 01/10/2005 01/31/2011 Observations included: 317 after adjustments Convergence obtained after 35 iterations Presampling 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. z-statistical error