Lbq test and ARMA results, Should I include lags? - r

I have a return time series in daily frequency, which is stationary(proofed by ADF test) , has no autocorrelation up to lag10(proofed by lbq test with lag10) and has ARCH effect(proofed by LM test). My initial though is just directly applying GARCH model. Rather than the usual procedure: first using ARMA(p,q) to get the residuals, and then fit GARCH to this ARMA residuals.
However, out of curiosity, I still use ARMA(p,q) model loop through (p,q) lags with range [0,1,..,10] to see whether ARMA(0,0) has the smallest AIC among all. After looping though those 121 (p,q) combinations, I find the smallest AIC does NOT belong to ARMA(0,0) model, but ARMA(2,7). Then I check the coef of this ARMA(2,7) model and find the may lags included are significant. The two AR lags are both significant at 1% level.
Now, I am quite confusing. Based on the result of lbq(10) test, I should use ARMA(0,0). Based on the results of smallest AIC of ARMA models, I should use ARMA(2,7). May I please ask, in this case, should I use ARMA(0,0) or ARMA(2,7)? My preference is to use ARMA(2,7), but how can I explain to others when they ask: why still use ARMA model when lbq test shows no autocorrelation?
Any of your kind thoughts is greatly appreciated!
Please see the code and results below
lbqtest(returns,'Lags',1:10)
I could also use the following code to only get autocorrelation up to lag10
lbqtest(returns,'Lags',10)
The p results of lbq(1) till lbq(10) are:
p =
0.3425 0.5612 0.4180 0.5356 0.6637 0.7696 0.7770 0.8448 0.8995 0.9198
The AIC results of ARMA(2,7) and ARMA(0,0) are
AIC AR MA
-1498.252431 2 7
-1494.028 0 0
The estimation result of ARMA(2,7) using R is
arima(x = returns, order = c(2, 0, 7))
Coefficients:
ar1 ar2 ma1 ma2 ma3 ma4 ma5 ma6 ma7 intercept
-1.6786 -0.8756 1.6808 0.8128 -0.1691 -0.1736 -0.1065 0.0419 0.0411 -0.0006
s.e. 0.0381 0.0308 0.0660 0.1044 0.1078 0.1097 0.1082 0.1017 0.0642 0.0015

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Series: total[, "total"]
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Coefficients:
ar1 ar2 mean
1.1055 -0.4207 138805.107
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In the auto.arima() function in R, how do I find the p,d,q values for that arima

I used an R code with an auto.arima function on a time series data set to forecast. From here, Id like to know how to find the p,d,q values for the arima. Is there a quick way to determine that, thank you.
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Note that I did this naively here for demonstration purposes. I didn't check whether this is an accurate representation of the dependency structure in the lynx data set.
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model1
Series: log(mydata_ts)
ARIMA(2,1,1)(1,0,0)[12]
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