similar to: garch prediction

Hi, I am having some confusion.It has been said that we can only estimate the future values using meanForecast +/- 2*standardDeviation. with 95% confidence.This means using this garch model we can only have a upper and lower limit of the values within which the next actual value is expected to lie.Then how come in research papers they plot the actual and predicted value so neatly.The simple

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-

Hi, i am working in the forecast of the daily price crude . The last prices of this data are the following: 100.60 101.47 100.20 100.06 98.68 101.28 101.05 102.13 101.70 98.27 101.00 100.50 100.03 102.23 102.68 103.32 102.67 102.23 102.14 101.25 101.11 99.90 98.53 96.76 96.12 96.54 96.30 95.92 95.92 93.45 93.71 96.42 93.99 93.76 95.24 95.63 95.95 95.83 95.65

Hello, This must be really simple, but I can't find it on R Site search. I need to generate a random normally distributed series with mean 0 and sd 1. In Matlab, this code is randn(n). The closest I found is runif(20,-1,1) but this forces a maximum and a minimum, and there's no way to specify a standard deviation of 1. >sd(runif(20,-1,1)) [1] 0.578164

For three of the beta distribution functions in R the parameters defining the distribution are alpha, beta and 'ncp'. Pretty trivially, there is no bijection between these three and the mean and variance, but for the special case of ncp = 0, I think there is. Rather than just write it down, it's probably a good idea to see how to get it. Note that mu = alpha/(alpha+beta) s2 =

Hi, could anyone tell me how predict() predicts the meanError or standardDerivation of a garchFit(1,1)-model, knowing the coefficients mu, omega, alpha1, beta1 and of course all datapoints? Thanks and sorry for my poor english. -- View this message in context: http://r.789695.n4.nabble.com/predict-of-garchFit-tp3826335p3826335.html Sent from the R help mailing list archive at Nabble.com.