# How to interpret standardized residuals tests in Ljung-Box Test and LM Arch test?

I am new in econometric and I am confused to make conclusion with Ljung-Box test and LM arch test. I used auto.arima and garch to find out relative orders for the TS and got the result as below. It seems all coefficients are fine, however, I cannot conclude whether the modle is fit by analyzing standardised residuals test as they have vary p-value. I would like to learn from you guys what your criteria would be in this case. Thank you for your sharing.

Title:
GARCH Modelling

Call:
garchFit(formula = ~arma(1, 1) + garch(1, 0), data = intc2, trace = F)

Mean and Variance Equation:
data ~ arma(1, 1) + garch(1, 0)

[data = intc2]

Conditional Distribution:
norm

Coefficient(s):
mu ar1 ma1 omega alpha1
0.0060629 0.4602704 -0.5562739 0.0034931 0.1270859

Std. Errors:
based on Hessian

Error Analysis:
Estimate Std. Error t value Pr(>|t|)
mu 0.0060629 0.0019137 3.168 0.00153 **
ar1 0.4602704 0.1464651 3.143 0.00167 **
ma1 -0.5562739 0.1419436 -3.919 8.89e-05 (3*)
omega 0.0034931 0.0002361 14.792 < 2e-16 (3*)
alpha1 0.1270859 0.0495262 2.566 0.01029 (1*)

Signif. codes: 0 ‘(3*)’ 0.001 ‘(2*)’ 0.01 ‘(1*)’ 0.05 ‘.’ 0.1 ‘ ’ 1

Log Likelihood:
1012.501 normalized: 1.350001

Description:
Tue Nov 28 00:02:34 2017 by user: ac

Standardised Residuals Tests:

Jarque-Bera Test R Chi^2 :Statistic: 52.88447 p-Value: 3.283152e-12
Shapiro-Wilk Test R W 0.9904564 ;8.781227e-05
Ljung-Box Test R Q(10) 14.35376 ;0.1574584
Ljung-Box Test R Q(15) 22.47159 ;0.09602165
Ljung-Box Test R Q(20) 25.98429 ;0.1663314
Ljung-Box Test R^2 Q(10) 16.05112 ;0.09817849
Ljung-Box Test R^2 Q(15) 24.24917 ;0.06098122
Ljung-Box Test R^2 Q(20) 30.81672 ;0.05765708
LM Arch Test R TR^2 16.61766 ;0.1645545

Information Criterion Statistics:
AIC BIC SIC HQIC
-10.704096

I think that $R$ in the output stands for residuals and $R^2$ for squared residuals. Given that, the function computes three Ljung-Box p-value for the residuals and three for the squared residuals. As you can see, all the p-values are higher than 0.05 (which is the classical level of significance assumed) therefore you cannot reject the null hypothesis of absence of serial dependence neither in residuals nor in squared residuals. In other terms, your specification is good to capture autocorrelation and time-varying volatility in the data series. By default the function considers lag up to 10, 15 and 20, you can manually specified the test for other lags via the "LjungBoxTest" function on standardized residuals.