similar to: GARCH AIC

Hi everybody, I have fitted an ar(1),Garch(1,1) model to some observations with the help of the garchFit function which is in the fGarch package. Here what I've done: library("fGarch") fit = garchFit(formula=~ar(1)+~garch(1,1), data=garat) Now I want to count AIC for this model. How can I do it? I cannot do it with the AIC function of stats package, because R tells me: "Error

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 <-

Hello I want to predict the future values of time series with Garch When I specified my model like this: library(fGarch) ret <- diff(log(x))*100 fit = garchFit(~arma(1,0,0)+garch(1, 1), data =ret) predict(fit, n.ahead = 10) meanForecast meanError standardDeviation 1 0.01371299 0.03086350 0.03305819 2 0.01211893 0.03094519 0.03350248

Hallo, i want to fit a GARCH model with a extern regressor (without arma components), so i found the following function in package fGarch. I tryed out a lot of things but usually I get this Error. > garchFit(formula=y~x, formula.var=~garch(1,1),data=w) Error in .garchFit(formula.mean, formula.var, series = x, init.rec, delta, : Algorithm only supported for mci Recursion I think i use the

Dear R experts, I am trying to estimate an ARMA 2,2 model with garch errors. I used the following code on R 2.9. #library library(fGarch) #data data1<-ts(read.table("C:/Users/falcon/Desktop/Time Series/exports/goods1.csv"), start=c(1992,1), frequency=12) head(data1) #garch garchFit(formula.mean= ~arma(2,2),formula.var=~garch(1,1), data=data1) but get this error: >

Hello, I have a few questions concerning the DCC-GARCH model and its programming in R. So here is what I want to do: I take quotes of two indices - S&P500 and DJ. And the aim is to estimate coefficients of the DCC-GARCH model for them. This is how I do it: library(tseries) p1 = get.hist.quote(instrument = "^gspc",start = "2005-01-07",end =

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta

Dear all, I'm using garchFit from fSeries package and I am not getting the desired results (error message : could not find function "garchFit" ). Would you please advise as to how I can build an ARIMA(p, d, q) - GARCH(p,q) model using R see the attached data and R-output. Thanking you in advance Kind regards Mangalani Peter Makananisa Statistical Analyst South

Dear expeRts, How can I specify the order p,q of a GARCH(p,q) model within a loop? Here's a minimal example showing that an "Formula and data units do not match"-error appears: library(fGarch) spec <- garchSpec(model = list(alpha = 0.1, beta = c(0.4, 0.4))) data <- garchSim(spec, n = 100) x <- list() for(q in 1:3){ print(q) x[q] <-

Hi, as i suppose to know in a stationary GARCH(1,1) model the sum of alpha and beta has to be smaller than 1. But if i use the garchfit() function from the package fGarch for my timeseries the sum is bigger than 1. The adf.test tells me a p-value smaller than 0.01 instead. What does this mean for me? Can i trust in the coefficients in this case? mfg user84 -- View this message in context:

Hello, I have a rather complex problem... I will have to explain everything in detail because I cannot solve it by myself...i just ran out of ideas. So here is what I want to do: I take quotes of two indices - S&P500 and DJ. And my first aim is to estimate coefficients of the DCC-GARCH model for them. This is how I do it: library(tseries) p1 = get.hist.quote(instrument =

Hi there, I'm trying to fit a GJR-GARCH Model using fGarch. I wanted to try that by fitting an APARCH model with a fixed delta of 2 and a non-fixed gamma. So I was simply trying to use: spec <- garchFit(~aparch(1,1),data=garchSim(),delta=2) coef(spec) And sometimes, it's working like a charm and delta is indeed exactly 2 in the resulting coefficient vector. Frequently, though, the

I'm trying to estimate the parameters for GARCH(1,1) process. Here's my code: loglikelihood <-function(theta) { h=((r[1]-theta[1])^2) p=0 for (t in 2:length(r)) { h=c(h,theta[2]+theta[3]*((r[t-1]-theta[1])^2)+theta[4]*h[t-1]) p=c(p,dnorm(r[t],theta[1],sqrt(h[t]),log=TRUE)) } -sum(p) } Then I use nlminb to minimize the function loglikelihood: nlminb(

Dear R-helpers, I need to read some data from output of garchFit in fGarch. my model is garch(1,1) and i want to read coefficients(omega,alpha,beta) and timeseries(x) and conditional SD(s). because i need them to use in other formula. for example :omega+x[1]+s[3] and maybe i have several simulation then i need a general way to read them, not to read with my eyes for example the quantity of

Dear list members, I am trying to estimate parameters of the AR(1)-GARCH(1,1) model. I have one additional dummy variable for the AR(1) part. First I wanted to do it using garchFit function (everything would be then estimated in one step) however in the fGarch library I didn't find a way to include an additional variable. That would be the formula but, as said, I think it is impossible to add

Hello, I was using garchFit {fGarch} to fit some GARCH processes. I noticed that the result contains "Log Likelihood" value (right above "Description"), but when I use .. at fit$llh to retrieve Log Likelihood value, the sign switched. I am confused about which value I should choose to report... Any help here? Thanks a lot! Ted -- View this message in context:

Hello. I am using fGarch to estimate the following model: Call: garchFit(formula = fmla, data = X[, i], trace = F) Mean and Variance Equation: data ~ arma(1, 1) + garch(1, 1) Conditional Distribution: norm Coefficient(s): mu ar1 ma1 omega alpha1 beta1 -0.94934 1.00000 -0.23211 54.06402 0.45709 0.61738 Std. Errors: based on Hessian Error Analysis:

Hello, I’ve fitted a Garch(2,1) model with function 'garchFit()' from the package 'fGarch': > m1 <- garchFit(formula = ~garch(2,1),data = X,trace = F) * See 'summary(m1)' OUTPUT BELOW * PROBLEM: My alpha1 term is not significant and I would like to make a NEW model, say m2, that does not contain alpha1, but I am not sure how to specify this with the garchFit()

Good day everyone, I fitted a GARCH model to a time series and R estimated the model and provide me with the estimates. However, when I tried to extract the fitted values from the estimated model I got the following error message: "Error in .local(object, ...) : object "fit" not found"   I used the following to extract the fitted values fitted_TASI <- fitted(garchFit(~

Hello, I'm quite new to R but tried to learn as much as possible in the last few months. My problem is that I would like to estimate the model of Leon et al. (2005). I have shortly summarised the most important equations in the following pdf file: http://hannes.fedorapeople.org/leon2005.pdf My main question is now how could I introduce these two additional terms into the Likelihood