[R-SIG-Finance] Fwd: fGarch

[Spencer Graves]

Your model "~garch(1,1)" specifies a constant + heteroscedastic noise.  
The slot you printed 'fit at fitted' is the estimated (constant) mean of 
the series.  To see the estimated noise standard deviation, try the 
following: 

 > fit at sigma.t
 [1] 0.4942740 0.3945277 0.3415809 0.3210639 0.1287955 0.2227873 0.2152462
 [8] 0.2816325 0.1532941 0.1202254 0.1291714 0.1460944 0.1200281 0.1303487
[15] 0.1341825 0.2970904 0.2980062 0.2529998 0.2168916 0.1197274 0.1221059
[22] 0.1997035 0.1257199 0.2609890 0.3220154 0.2870766 0.1438314 0.1280727
[29] 0.1270258 0.1597462 0.2861451 0.2407515 0.2998393 0.2143946 0.3378366
[36] 0.7663652 1.1671236 1.1711026 1.2397624 1.2168715
 >
      For a similar example, see section 3.5 of Tsay (2005) Analysis of 
Financial Time Series (Wiley) and "scripts\ch03" in the FinTS package. 

      hope this helps.
      Spencer Graves

babel at centrum.sk wrote:
> ______________________ Pôvodná správa: ________________________
>   
>> Od: babel at centrum.sk
>> Komu: <r-sig-finance at stat.math.ethz.ch>
>> Datum: 29.01.2008 14:28
>> Předmět: [R-SIG-Finance] fGarch
>>
>>     
> Hello.
> I have this problem. Why do I have all fitted values the same??
>   
>>  y
>>     
>    43.097 43.041 43.019 42.769 42.533 42.542 42.466 42.817 42.734 42.770
>    42.637 42.710 42.669 42.782 42.993 42.994 42.944 42.902 42.714 42.746
>    42.881 42.760 42.489 42.422 42.460 42.641 42.675 42.678 42.827 42.981
>    42.930 42.996 42.899 43.037 43.478 43.882 43.886 43.955 43.932 43.998
>
>   
>> library(fGarch)
>>     
>
>   
>> fit = garchFit(~garch(1, 1), data = y)
>>     
>
>   
>> show.fGARCH(fit)
>>     
>
>
> Title:
>  GARCH Modelling 
>
> Call:
>  garchFit(formula = ~garch(1, 1), data = y) 
>
> Mean and Variance Equation:
>  ~arma(0, 0) + ~garch(1, 1) 
>
> Conditional Distribution:
>  dnorm 
>
> Coefficient(s):
>          mu        omega       alpha1        beta1  
> 42.30131209   0.00394317   0.99198425   0.05514354  
>
> Error Analysis:
>         Estimate  Std. Error  t value Pr(>|t|)    
> mu     42.301312    0.008606 4915.050   <2e-16 ***
> omega   0.003943    0.001626    2.426   0.0153 *  
> alpha1  0.991984    0.061981   16.005   <2e-16 ***
> beta1   0.055144    0.052464    1.051   0.2932    
> ---
> Signif. codes:  0 `***´ 0.001 `**´ 0.01 `*´ 0.05 `.´ 0.1 ` ´ 1 
>
> Log Likelihood:
>  3405.273    normalized:  1.706904 
>
>
>
>   
>> fit at fitted
>>     
>  42.30131 42.30131 42.30131 42.30131 42.30131 42.30131 42.30131 42.30131
>  42.30131 42.30131 42.30131 42.30131 42.30131 42.30131 42.30131 42.30131
>
>   
>> predict(fit, n.ahead = 10)
>>     
>    meanForecast meanError standardDeviation
> 1      42.30131  2.581365          8.109771
> 2      42.30131  2.581365          8.298906
> 3      42.30131  2.581365          8.492442
> 4      42.30131  2.581365          8.690480
> 5      42.30131  2.581365          8.893126
> 6      42.30131  2.581365          9.100487
> 7      42.30131  2.581365          9.312673
> 8      42.30131  2.581365          9.529796
> 9      42.30131  2.581365          9.751972
> 10     42.30131  2.581365          9.979319
>
> I want to count RMSE and choose which Garch model, is better, but I am not
> able to make a garch model.Thank you
>
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