Michael,

it may well be the case that specifying the conditional variance as a Smooth
Transition GARCH (STGARCH) model can provide a good description for the
conditional variance. Have a look at the paper by Lundbergh and Teräsvirta:
http://swopec.hhs.se/hastef/abs/hastef0291.htm

Regards,
Hannu

On 8/17/06, michael mathews <muckjail@yahoo.com> wrote:
>
> Hi folks,
> I have been playing with garch models to model the volatility in
> physical natural prices.
> Here is the issue I have a dataset of 801 daily returns (attached).
> If I run
>
> garchall<-garch(hsc)
>
> ***** ESTIMATION WITH ANALYTICAL GRADIENT *****
>
>
> > summary(garchall)
>
> Call:
> garch(x = hsc)
>
> Model:
> GARCH(1,1)
>
> Residuals:
>     Min      1Q  Median      3Q     Max
> -4.3424 -0.5734  0.0000  0.6037  4.0501
>
> Coefficient(s):
>     Estimate  Std. Error  t value Pr(>|t|)
> a0 2.507e-05   9.200e-06    2.726  0.00642 **
> a1 1.218e-01   2.085e-02    5.840 5.21e-09 ***
> b1 8.759e-01   1.937e-02   45.212  < 2e-16 ***
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Diagnostic Tests:
>         Jarque Bera Test
>
> data:  Residuals
> X-squared = 62.7291, df = 2, p-value = 2.387e-14
>
>
>         Box-Ljung test
>
> data:  Squared.Residuals
> X-squared = 0.0384, df = 1, p-value = 0.8447
>
> Now if we run the same model on a subset say the last 351 days we get
> > garch351<-garch(tail(hsc,351))
>
> ***** ESTIMATION WITH ANALYTICAL GRADIENT *****
>
>
> > summary(garch351)
>
> Call:
> garch(x = tail(hsc, 351))
>
> Model:
> GARCH(1,1)
>
> Residuals:
>       Min        1Q    Median        3Q       Max
> -4.171521 -0.424628  0.008727  0.532158  3.962116
>
> Coefficient(s):
>     Estimate  Std. Error  t value Pr(>|t|)
> a0 2.511e-05   1.589e-05    1.580 0.114167
> a1 1.043e-01   2.950e-02    3.536 0.000406 ***
> b1 8.957e-01   2.567e-02   34.896  < 2e-16 ***
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Diagnostic Tests:
>         Jarque Bera Test
>
> data:  Residuals
> X-squared = 76.3704, df = 2, p-value < 2.2e-16
>
>
>         Box-Ljung test
>
> data:  Squared.Residuals
> X-squared = 1.2806, df = 1, p-value = 0.2578
>
> still ok. Now finally we get t the point of this email lets look at 352
> days of data:
>
> garch352<-garch(tail(hsc,352))
>
> ***** ESTIMATION WITH ANALYTICAL GRADIENT *****
>
>
> Warning message:
> NaNs produced in: sqrt(pred$e)
> > summary(garch352)
>
> Call:
> garch(x = tail(hsc, 352))
>
> Model:
> GARCH(1,1)
>
> Residuals:
>      Min       1Q   Median       3Q      Max
> -4.16377 -0.58155  0.01454  0.70886 12.41242
>
> Coefficient(s):
>     Estimate  Std. Error  t value Pr(>|t|)
> a0 2.428e-05   1.556e-05    1.561 0.118632
> a1 1.043e-01   2.947e-02    3.540 0.000400 ***
> b1 8.962e-01   2.556e-02   35.058  < 2e-16 ***
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Diagnostic Tests:
>         Jarque Bera Test
>
> data:  Residuals
> X-squared = 10993.57, df = 2, p-value < 2.2e-16
>
>
>         Box-Ljung test
>
> data:  Squared.Residuals
> X-squared = 0.1831, df = 1, p-value = 0.6687
>
> whats up? Any Ideas.
> I have also tried using garchFit from the fSeries package but it locks
> up completely left it running last night and it was still spinning this
> morning when I got back to the office.
>
> thanks in advance
>
> michael
>
> _______________________________________________
> R-SIG-Finance@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
>

	[[alternative HTML version deleted]]

