predict returns with the fSeries package
Ricardo Zambrano Aguilera wrote:
Ther is no new data! It's like in the Arima case of R! You model and fit the time series up to the end, and then you start with your forecast at position n+1. since there can't is be no newdata, you must get always the same result, as you did. I hope this helps Diethelm Wuertz
Dear List how i can predict returns with new data??
ajuste1
Title:
GARCH Modelling
Call:
garchFit(formula.mean = ~arma(2, 0), formula.var = ~garch(1,
1), series = r_peso, cond.dist = "dnorm")
Mean and Variance Equation:
~arma(2, 0) + ~garch(1, 1)
Conditional Distribution:
dnorm
Coefficient(s):
mu ar1 ar2 omega alpha1 beta1
-1.25313e-04 6.10406e-02 -7.06526e-02 2.11754e-06 9.47503e-02 8.45540e-01
Error Analysis:
Estimate Std. Error t value Pr(>|t|)
mu -1.253e-04 1.466e-04 -0.855 0.3925
ar1 6.104e-02 2.765e-02 2.208 0.0272 *
ar2 -7.065e-02 2.785e-02 -2.537 0.0112 *
omega 2.118e-06 1.114e-06 1.902 0.0572 .
alpha1 9.475e-02 2.411e-02 3.929 8.51e-05 ***
beta1 8.455e-01 5.195e-02 16.277 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Log Likelihood:
-5473.802 normalized: -3.751749
Description:
Thu Sep 07 11:50:50 2006
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################## then....############################
predict(ajuste1)
meanForecast meanError standardDeviation 1 0.0001657992 0.005873028 0.004574582 2 -0.0002516247 0.005883959 0.004668490 3 -0.0001535909 0.005897073 0.004755100 4 -0.0001181148 0.005897279 0.004835123 5 -0.0001228757 0.005897331 0.004909178 6 -0.0001256727 0.005897333 0.004977807 7 -0.0001255071 0.005897334 0.005041485 8 -0.0001252994 0.005897334 0.005100636 9 -0.0001252984 0.005897334 0.005155636 10 -0.0001253130 0.005897334 0.005206823 ######### now if a put newdata it?s the same??, how i can know the returns for the next week if i put the returns of the last week????############
predict(ajuste1,newdata=returns[1451:1460,1])
meanForecast meanError standardDeviation 1 0.0001657992 0.005873028 0.004574582 2 -0.0002516247 0.005883959 0.004668490 3 -0.0001535909 0.005897073 0.004755100 4 -0.0001181148 0.005897279 0.004835123 5 -0.0001228757 0.005897331 0.004909178 6 -0.0001256727 0.005897333 0.004977807 7 -0.0001255071 0.005897334 0.005041485 8 -0.0001252994 0.005897334 0.005100636 9 -0.0001252984 0.005897334 0.005155636 10 -0.0001253130 0.005897334 0.005206823 My best regards Ricardo Z.
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