Garch problem

The fit is essentially saying that the half-life
of a shock is infinite.  This generally occurs
when the in-sample volatility has a general
trend.  One solution is more data.  There are
doubtless other paths as well.

RON70 wrote:

I have following dataset as monthly percentage return for a stock :

0.173741362
-0.062237174

  

[ ... ]

-0.001652893
-0.092301325

Now I fit a GARCH (1,1) model on that :

  

garch(Delt(dat)[-1], c(1,1))
    

 ***** ESTIMATION WITH ANALYTICAL GRADIENT ***** 


     I     INITIAL X(I)        D(I)

     1     4.331103e-03     1.000e+00
     2     5.000000e-02     1.000e+00
     3     5.000000e-02     1.000e+00

    IT   NF      F         RELDF    PRELDF    RELDX   STPPAR   D*STEP  
NPRELDF
     0    1 -4.507e+02
     1    6 -4.508e+02  2.00e-04  3.20e-04  1.5e-03  6.3e+06  1.5e-04 
1.01e+03
     2    7 -4.508e+02  1.57e-05  1.69e-05  1.4e-03  2.0e+00  1.5e-04 
3.19e-01
     3   13 -4.521e+02  2.85e-03  4.72e-03  5.6e-01  2.0e+00  1.3e-01 
3.16e-01
     4   16 -4.602e+02  1.76e-02  4.41e-03  8.1e-01  6.7e-01  5.1e-01 
1.99e-02
     5   23 -4.607e+02  1.13e-03  2.77e-03  1.6e-04  7.4e+00  1.8e-04 
8.48e+00
     6   24 -4.607e+02  4.81e-05  4.37e-05  1.6e-04  2.0e+00  1.8e-04 
1.77e+01
     7   30 -4.638e+02  6.60e-03  8.81e-03  9.8e-02  2.0e+00  1.2e-01 
1.84e+01
     8   31 -4.645e+02  1.52e-03  7.73e-03  8.2e-02  1.3e+00  1.2e-01 
1.39e-02
     9   33 -4.688e+02  9.18e-03  6.28e-03  6.8e-02  0.0e+00  1.2e-01 
6.94e-03
    10   35 -4.693e+02  9.32e-04  9.33e-04  8.9e-03  1.9e+00  1.8e-02 
2.86e-02
    11   37 -4.699e+02  1.34e-03  1.59e-03  1.6e-02  1.8e+00  3.5e-02 
5.99e-02
    12   38 -4.704e+02  1.05e-03  1.43e-03  1.6e-02  1.6e+00  3.5e-02 
9.10e-03
    13   40 -4.705e+02  1.84e-04  2.85e-04  5.3e-03  1.2e+00  1.3e-02 
7.52e-04
    14   42 -4.705e+02  3.71e-05  5.18e-05  2.4e-03  8.1e-01  5.0e-03 
7.09e-05
    15   44 -4.705e+02  8.51e-07  3.04e-06  4.9e-04  8.2e-01  9.5e-04 
5.29e-06
    16   57 -4.705e+02 -7.73e-15  1.09e-15  5.0e-15  4.4e+06  9.1e-15 
2.87e-07

 ***** FALSE CONVERGENCE *****

 FUNCTION    -4.704848e+02   RELDX        4.961e-15
 FUNC. EVALS      57         GRAD. EVALS      16
 PRELDF       1.088e-15      NPRELDF      2.867e-07

     I      FINAL X(I)        D(I)          G(I)

     1    2.824235e-05     1.000e+00     5.619e+01
     2    8.649332e-02     1.000e+00    -5.899e-01
     3    9.175397e-01     1.000e+00    -6.866e-01


Call:
garch(x = Delt(dat)[-1], order = c(1, 1))

Coefficient(s):
       a0         a1         b1  
2.824e-05  8.649e-02  9.175e-01  

Warning message:
In sqrt(pred$e) : NaNs produced

What we see that sum of alpha and beta coef is more than 1. Therefore
probably I choose a wrong model on my dataset. Can anyone please guide me
how to modify that model?

Regards,