Hi all,

Could you please help me?

I am fitting the GJRGARCH model to 60000 data points of 15min bar
log-return data... and I got the following error message... with lots of NA
results...

What shall I do?

Thanks a lot!


> spec = ugarchspec(variance.model = list(model = "gjrGARCH"),
distribution.model = "sged")
> fit = ugarchfit(data = dLogRets, spec = spec)
>
> fit

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

Conditional Variance Dynamics
-----------------------------------
GARCH Model : gjrGARCH(1,1)
Mean Model : ARFIMA(1,0,1)
Distribution : sged

Optimal Parameters
------------------------------------
        Estimate  Std. Error  t value Pr(>|t|)
mu     -0.000012          NA       NA       NA
ar1     0.038942          NA       NA       NA
ma1     0.038334          NA       NA       NA
omega   0.000000          NA       NA       NA
alpha1  0.050000          NA       NA       NA
beta1   0.900000          NA       NA       NA
gamma1  0.050000          NA       NA       NA
skew    1.000000          NA       NA       NA
shape   2.000000          NA       NA       NA

Robust Standard Errors:
        Estimate  Std. Error  t value Pr(>|t|)
mu     -0.000012          NA       NA       NA
ar1     0.038942          NA       NA       NA
ma1     0.038334          NA       NA       NA
omega   0.000000          NA       NA       NA
alpha1  0.050000          NA       NA       NA
beta1   0.900000          NA       NA       NA
gamma1  0.050000          NA       NA       NA
skew    1.000000          NA       NA       NA
shape   2.000000          NA       NA       NA

 failed to invert hessian
LogLikelihood : -1.1

Information Criteria
------------------------------------

Akaike       0.00037884
Bayes        0.00187841
Shibata      0.00037879
Hannan-Quinn 0.00084718

Q-Statistics on Standardized Residuals
------------------------------------
      statistic   p-value
Lag10     26.17 0.0009835
Lag15     27.71 0.0099253
Lag20     34.01 0.0125447

H0 : No serial correlation

Q-Statistics on Standardized Squared Residuals
------------------------------------
      statistic p-value
Lag10     11.53  0.1736
Lag15     15.75  0.2630
Lag20     20.97  0.2810

ARCH LM Tests
------------------------------------
             Statistic DoF P-Value
ARCH Lag[2]      2.860   2  0.2394
ARCH Lag[5]      6.334   5  0.2751
ARCH Lag[10]    12.032  10  0.2829
Error in t.default(grad) : argument is not a matrix

Enter a frame number, or 0 to exit

1: function (object)
2: function (object)
3: .nyblomTest(object)
4: t(grad)
5: t.default(grad)

Selection:

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