time series - How GARCH/ARCH models are useful to check the volatility?

Below a R code wrote by the moderator @richardh (whom I want to thank again) about ARCH/GARCH models.

library(quantmod)
library(tseries)
getSymbols("MSFT")
ret <- diff.xts(log(MSFT$MSFT.Adjusted))[-1]
arch_model <- garch(ret, order=c(0, 3))
garch_model <- garch(ret, order=c(3, 3))
plot(arch_model)                                  

plot(garch_model)

My focus is to understand if the volatility of the returns is constant during all the series. I don't understand how ARCH/GARCH models could help me understading this kind of aspect, at the moment the operations I do are:

• Calculate the % returns of the stocks


• Linear regressione like: lm(A~B) where A and B are the stocks returns (%)


• Passing the residuals of the linear regression to the unit root tests.

now the problem is to understand if the volatility is constant (take a look at the chart below, that problem is clearly visible), so the question is:

How can I understand if the volatility is not constant reading ARCH/GARCH model

EDIT:

garch_model <- garch(rnorm(1000), order=c(3, 3))
> summary(garch_model)

Call:
garch(x = rnorm(1000), order = c(3, 3))

Model:
GARCH(3,3)


Residuals:
      Min        1Q    Median        3Q       Max 
-3.394956 -0.668877 -0.008454  0.687890  3.221826 

Coefficient(s):
    Estimate  Std. Error  t value Pr(>|t|)
a0 7.133e-01   7.156e+00    0.100    0.921
a1 1.752e-02   3.750e-02    0.467    0.640
a2 6.388e-03   1.924e-01    0.033    0.974
a3 6.486e-14   1.711e-01    0.000    1.000

b1 7.396e-02   1.098e+01    0.007    0.995
b2 8.052e-02   1.120e+01    0.007    0.994
b3 8.493e-02   4.279e+00    0.020    0.984

Diagnostic Tests:
        Jarque Bera Test

data:  Residuals 
X-squared = 1.4114, df = 2, p-value = 0.4938



        Box-Ljung test

data:  Squared.Residuals 
X-squared = 0.0061, df = 1, p-value = 0.9377

> 

garch_model$fitted.values