[R-SIG-Finance] [R-sig-finance] Garch problem

[RON70]

Dear Patrick, thank you so much for this reply. You said one solution is to
increase the data point. However at this point I can not get more. Therefore
if you please tell more about "doubtless other paths" I will be truly
grateful.

Regards,


Patrick Burns-2 wrote:
> 
> 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,
>>
> 
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