fGarch
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:
______________________ P?vodn? spr?va: ________________________
Od: babel at centrum.sk
Komu: <r-sig-finance at stat.math.ethz.ch>
Datum: 29.01.2008 14:28
P?edm?t: [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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