I'm getting bigger values of AIC and BIC after applying the p and q (obtained from auto.arima() function) to the standard GARCH model [closed]

I tried to find p and q by using auto.arima function in RStudio. It gave ARIMA(1,1,0). However, after applying p=1 and q=0 to the GARCH model, ar1 became insignificant and AIC became higher. p=0 and q=0 gave the best results according to the Information Criteria. I want to know whether I am doing something wrong. library(quantmod) library(forecast) library(rugarch) getSymbols("BNTX", to="2022-12-31") returns_BNTX=CalculateReturns(BNTX$BNTX.Adjusted)[-1] auto.arima(returns_BNTX) mod_specify=ugarchspec(mean.model=list(armaOrder=c(0,0)), variance.model=list(model="sGARCH", garchOrder=c(1,1)), distribution.model='sstd') mod_fitting_ssstd_BNTX=ugarchfit(data=returns_BNTX, spec=mod_specify, out.sample=20) mod_fitting_ssstd_BNTX The AIC with ARMA(0,0)= -3,0605 The AIC with ARMA(1,0)= -3.0595 (insignificant ar1) +not sure how to apply drift in ARFIMA(1,1,0) to the GARCH model.