GARCH(1,1) with dummies

[Katerinaki]

Hello, i have run a garch (1,1) model as you see below in order to find the day of the week effect. May i use this variance equation to explain the leverage effect or i need to run a different one? Thank you.

Dependent Variable: RGD
Method: ML ARCH - Normal distribution (BFGS / Marquardt steps)
Included observations: 1499 after adjustments
Convergence achieved after 29 iterations
Coefficient covariance computed using outer product of gradients
Presample variance: backcast (parameter = 0.7)
GARCH = C(6) + C(7)*RESID(-1)^2 + C(8)*GARCH(-1)

Variable Coefficient Std. Error z-Statistic Prob.

@WEEKDAY=1 -0.000390 0.000563 -0.691554 0.4892
@WEEKDAY=2 5.51E-05 0.000491 0.112297 0.9106
@WEEKDAY=3 0.001360 0.000564 2.411333 0.0159
@WEEKDAY=4 0.001710 0.000520 3.288525 0.0010
@WEEKDAY=5 0.001806 0.000618 2.920736 0.0035

Variance Equation

C 4.72E-06 1.20E-06 3.947249 0.0001
RESID(-1)^2 0.108738 0.014132 7.694219 0.0000
GARCH(-1) 0.854302 0.020214 42.26344 0.0000

R-squared 0.006938 Mean dependent var 0.000369
Adjusted R-squared 0.004279 S.D. dependent var 0.011447
S.E. of regression 0.011423 Akaike info criterion -6.288164
Sum squared resid 0.194929 Schwarz criterion -6.259811
Log likelihood 4720.979 Hannan-Quinn criter. -6.277601
Durbin-Watson stat 1.900195

[Katerinaki]

I would really appreciate if someone gives me an answer.Thanks!