[R-SIG-Finance] [R-sig-finance] Garch problem
alexios
alexios at 4dscape.com
Wed Mar 18 10:42:26 CET 2009
You could try to use a different distribution. For example, and using
the rgarch package from r-forge:
spec=ugarchspec(variance.model=list(model="sGARCH",
garchOrder=c(1,1)),mean.model=list(armaOrder=c(0,0),
include.mean=TRUE),distribution.model="std")
fit=ugarchfit(data, spec, solver="nlminb", control=list(trace=1))
This seems to converge, though with such little data I would be cautious
in making predictions/inferences from the model.
-Alexios
Patrick Burns wrote:
> I was hoping to leave the "Doubtless" [1] as
> an exercise for the reader -- mainly as I'm not
> at all well versed in what is available in R for
> garch these days.
>
> One idea would be to try a components model
> (may not be available).
>
> Another idea would be to try a Bayesian estimate
> (may not be available).
>
> A method that certainly is available is to pick a
> "reasonable" set of parameters (no estimation).
>
> The course of action may well depend on the use
> to which the model is to be put.
>
> [1] Stephen Crane "The Wayfarer"
>
> Pat
>
>
> RON70 wrote:
>> 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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>>>
>>
>>
>
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