# AR(k)-GARCH(1,1) model. Why am I getting same Log-likelihoods and AICs?

I am trying to for loop an AR(k)-GARCH(1,1) model, however it seems that I am getting same log-likelihoods and AICs. I believe that my code is fine, since I manually checked the iterations. Is there any statistically theoretical reason why is this happening?

garch_models = pd.DataFrame(columns=['AIC','Log-Likelihood'],index=garch_columns)
for i in np.arange(len(garch_models)):
AEX_GARCH_0 = arch_model(log_r['AEX'], lags=i, vol='Garch', p=1, o=0, q=1, dist='t')
res_AEX = AEX_GARCH_0.fit()
res_AEX.summary()
garch_models.iloc[i,0]= res_AEX.aic
garch_models.iloc[i,1]= res_AEX.loglikelihood


Here's what I am getting in the data frame:

Here are two of the iterations (so I can save some space):

arch_model(log_r['AEX'], mean='AR', lags=i, vol='Garch', p=1, o=0, q=1, dist='t')