similar to: Any GARCH-in-mean Implementation?

Hi, Can someone technically explain, why does it take so long with loops in R? Thanks, Vehbi Sinan Tunalioglu

Good day everyone,   I tried to find a multivariate GARCH package and failed to find one. Although when I searched R I found the following link which describes the package:   http://www.r-project.org/user-2006/Slides/Schmidbauer+Tunalioglu.pdf   can any one help me with this issue.   Thank you in advance [[alternative HTML version deleted]]

Hello Following some standard textbooks on ARMA(1,1)-GARCH(1,1) (e.g. Ruey Tsay's Analysis of Financial Time Series), I try to write an R program to estimate the key parameters of an ARMA(1,1)-GARCH(1,1) model for Intel's stock returns. For some random reason, I cannot decipher what is wrong with my R program. The R package fGarch already gives me the answer, but my customized function

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 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, 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

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: >

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 =

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()

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, I have the similar issue in estimating a GARCH model with exogenous variables in the mean equation. Currently, to my understanding, the garch function in tseries package can handle univariate model, and garchFit in fGarch can handle ARMA specification. I wonder if there is any R function that can handle exogenous variables in estimating GARCH? Thank you a lot. Edwin -- View this

Hello, I have a univariate data set, and the unconditional variance is 1. I would like to fit a GARCH(1,1) model to the data set with a constraint: \omega (the constant parameter in GARCH(1,1)) is equal to 1-\alpha-\beta. So the unconditional variance can be controled to be \omega /(1-\alpha-\beta) = 1. I was using garchFit (fGARCH package) but did not find the way to control. Any help?

Hi R-users Can the users let me know how to do MGARCH estimate (Bivariate GARCH) and volatility forecast for 2 variables in R. thanks and regards snvk

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 =

Hello, I was wondering if anyone knew how to fit a series using a Garch-M (Garch in the mean) model. From what I gathered from the documentation, it does not seem to be implemented in either fGarch, fSeries, or tSeries. Perhaps there is an option that allows this functionality. Otherwise, if it's possible to modify an existing function I would appreciate any guidance on how to go about

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

I am estimating daily electricity prices using GARCH (1,1). What I would like to see is whether there is some kind of daily or seasonal effect in variance of the price series. For instance, variance of electricity prices might be different (higher) during weekdays as opposed to during weekend. Thus, I would like to include some dummy variables in variance equation -but I don't know how to

Dear all, I am using GARCH (1,1) model to simulate volatility. But seems that I am missing something about how it works in R. The following code produces negative results, though vola cannot be. What is wrong here? library("fSeries") library("fGarch") spec = garchSpec(model = list(omega = 0.01, alpha = 0.13, beta = 0.86)) gat <- garchSim(spec, n = 10) Thanks a lot!

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model