similar to: fGarch: how to use garchFit() in loop?

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

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

Greetings, I am using fGarch package to estimate and simulate GARCH models. What I would like to do is to perform Monte Carlo simulation. Unfortunately I cannot figure how to modify the code to achieve this. I use the following code to run a single simulation: spec=garchSpec(model=list(ar= 0.440270860, omega=0.000374365,alpha=0.475446583 , mu=0, beta=0)) sim<-garchSim(spec,

Good morning everyone, I use for the first time the package fSeries and i try to run the example given by Diethelm Würtz. But when i run its example which is the following # # Example: # Model a GARCH time series process # # Description: # PART I: Estimate GARCH models of the following type ARCH(2) # and GARCH(1,1) with normal conditional distribution functions. # PART II: Simulate

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:

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

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(~

Hi All, I,m trying to do a GARCH simulation in R 2.3.0 release in Windows XP. I've seen garchsim function but that is for garch (1,1) and ?garch gives an example for ARCH simulation. Can anyone help me how can i extend the help shown in ?garch to GARCH simulation? Please help me in this regard. Thanks, Sumanta Basak.

Hi, I am attempting to fit a ARMA/GARCH regression model without success. ### ARIMA-GARCH model with regressor ### ### Time series data: A multivariate data set. cov.ts.dq = cov.ts[1:4,"dq1"][!is.na(cov.ts[,"dq1"])] cov.ts.day = ts.intersect(dq = diff(q.ts), day = lag(q.ts, -1)) ### The following R scripts work: (summary(no.day.fitr <- garchFit(dq ~ arma(0,3) +

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

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:

Dear all- Package /fGarch/ version 2100.78 in R version 2.8.1 (2008-12-22) running on linux 2.6.22.9-91.fc7 In trying to fit garch models in above environment. I am getting "reasonable" fitted coefficients, but the fitObject@fitted are all the same. This is true even for the help page example: library(fGarch) R> X.timeSeries = as.timeSeries(msft.dat) R> head( +

I use fGarch package to estimate AR(1)-ARCH(1) process for a vector of returns. Then, using the estimated parameters I want to simulate 10 000 sample paths where each path has the same length as the vector of returns. So the first line of the code is: spec=garchSpec(model=list(ar= 0.440270860, omega=0.000374365,alpha=0.475446583 , mu=0, beta=0))---- The only way I can think of generating 10 000

Dear all, I've recently switched from EViews to R with RMetrics/fSeries (newest version of july 10) for my analysis because of the much bigger flexibility it offers. So far my experiences had been great -prior I had already worked extensively with S-Plus so was already kind of familiar with the language- until I got to the fSeries package. My problem with the documentation of fSeries is that

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

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 =

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 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 =

I need to change the code of Garch to the FCGARCH (a non-linear multi-regime GARCH). I don't know nothing about R. I'd like to know how can I get the code of the garch in order to change it and make the fit for the FC-GARCH. Any non-linear code will be helpfull because if doesn't help in the programming it helps in getting familiar with R. Thank you Renato -- PhD Student Renato

hello everybody, I would like to fit a model to a times series (testing set) for out of sample predictions using garchFit(). I would like to keep the coefficients of ARMA/GARCH model fixed (as found by fitting the model to my training set). The arima fitting function has such an option for that (fixed=NULL) but the garchFit() doesnt. It is very important for me to keep the same coefficients