How to automatically takeout only the p-values of the optimal parameter coefficients in "ugarchfit" in R

Question

My final aim is to derive the time series data of GARCH (1,1) volatility values over time corresponding to my existing return data.

To do this as a first step, I am trying to derive the volatility (i.e., sigma) value from the GARCH (1,1) model I run from the code "ugarchfit" in R.

From here, I know how to take out the optimal parameters coefficient estimates by using the command "coef()"

However, I am not able to pull out their corresponding p-values as well.

Is there an automatic way to do so? Or is there an alternative way to produce the same corresponding p-values if I am able to pull out the corresponding standard errors or t-test values instead?

The below is the full output I get.

 *---------------------------------*
 *          GARCH Model Fit        *
 *---------------------------------*

 Conditional Variance Dynamics  
 -----------------------------------
 GARCH Model    : sGARCH(1,1)
 Mean Model : ARFIMA(0,0,0)
 Distribution   : norm 

 Optimal Parameters
 ------------------------------------
         Estimate  Std. Error  t value Pr(>|t|)
 mu     -0.001183    0.008642 -0.13686 0.891143
 omega   0.006368    0.000713  8.92639 0.000000
 alpha1  0.334860    0.150390  2.22662 0.025973
 beta1   0.000000    0.187089  0.00000 1.000000

 Robust Standard Errors:
         Estimate  Std. Error  t value Pr(>|t|)
 mu     -0.001183    0.009319 -0.12692 0.899008
 omega   0.006368    0.002321  2.74405 0.006069
 alpha1  0.334860    0.175326  1.90993 0.056142
 beta1   0.000000    0.259065  0.00000 1.000000

 LogLikelihood : 90.52192 

 Information Criteria
 ------------------------------------
                
 Akaike       -1.8607
 Bayes        -1.7518
 Shibata      -1.8642
 Hannan-Quinn -1.8167

 Weighted Ljung-Box Test on Standardized Residuals
 ------------------------------------
                         statistic p-value
 Lag[1]                     0.1237  0.7251
 Lag[2*(p+q)+(p+q)-1][2]    0.1237  0.9033
 Lag[4*(p+q)+(p+q)-1][5]    2.2145  0.5687
 d.o.f=0
 H0 : No serial correlation

 Weighted Ljung-Box Test on Standardized Squared Residuals
 ------------------------------------
                         statistic p-value
 Lag[1]                      1.158  0.2819
 Lag[2*(p+q)+(p+q)-1][5]     1.868  0.6500
 Lag[4*(p+q)+(p+q)-1][9]     3.152  0.7337
 d.o.f=2

 Weighted ARCH LM Tests
 ------------------------------------
             Statistic Shape Scale P-Value
 ARCH Lag[3]    0.5813 0.500 2.000  0.4458
 ARCH Lag[5]    0.7021 1.440 1.667  0.8228
 ARCH Lag[7]    0.9818 2.315 1.543  0.9165

 Nyblom stability test
 ------------------------------------
 Joint Statistic:  0.7391
 Individual Statistics:              
 mu     0.09335
 omega  0.18718
 alpha1 0.07268
 beta1  0.29205

 Asymptotic Critical Values (10% 5% 1%)
 Joint Statistic:        1.07 1.24 1.6
 Individual Statistic:   0.35 0.47 0.75

 Sign Bias Test
 ------------------------------------
                    t-value   prob sig
 Sign Bias          0.25257 0.8012    
 Negative Sign Bias 0.08968 0.9287    
 Positive Sign Bias 0.22847 0.8198    
 Joint Effect       0.48299 0.9226    


 Adjusted Pearson Goodness-of-Fit Test:
 ------------------------------------
   group statistic p-value(g-1)
 1    20     34.53      0.01591
 2    30     37.00      0.14622
 3    40     51.09      0.09308
 4    50     49.47      0.45423

Answer 1

If modelfit is your model (i.e. modelfit <- ugarchfit(...)), then the p-values for the optimal parameters are in

modelfit@fit$matcoef[,4]

or for the robust parameters...

modelfit@fit$robust.matcoef[,4]