Alexios,

Thanks for the quick reply.  But to understand this better, both the alpha
and beta you describe are in terms of the variance equation correct?  How
would I determine the 'in-mean' conditional variance or volatility?  I am
trying to use your model to determine a the time varying risk premium using
historical monthly S&P 500 returns adjusted by a risk free rate.

Thanks,

Nick




On Thu, Apr 3, 2014 at 1:31 PM, Alexios Ghalanos <alexios@4dscape.com>wrote:

> 1. See archm and archpow options for ARCH in mean.
> 2. Don't use model='fGARCH' and submodel='GARCH'. Use model='sGARCH'.
> 3. Read the vignette to see the notation used (alpha is the ARCH
> coefficient and beta the GARCH coefficient).
>
>
> Alexios
>
> > On 3 Apr 2014, at 19:54, Nick Phillips <spillihp.kcin.61@gmail.com>
> wrote:
> >
> > Hi,
> >
> > Im trying to compare some GARCH moedling outputs from EVIEWS to the
> rugarch
> > package, specifically what EVIEWS refers to as the 'GARCH coefficient'
> when
> > modelling a GARCH-M(1,1) process.
> >
> > Is anyone familiar with this and if so how to calculate it from the
> rugarch
> > or anther garch package in R?
> >
> > Any help would be appreciated
> >
> > Thanks,
> >
> > Nick
> >
> >
> >
> > my R code to fit the rugarch garch-m (1,1) model is
> >
> > rm(list = ls())
> >
> > library(PerformanceAnalytics)
> >
> > library(quantmod)
> >
> > library(forecast)
> >
> > library(rugarch)
> >
> > ###############################################
> >
> > # Get Data ##
> >
> > ###############################################
> >
> > data.df = read.table(file = 'RP_Orig.csv', sep = ',',
> >
> >                     header = TRUE, as.is = TRUE)
> >
> > data.xts <- xts(data.df[,2:ncol(data.df)], order.by =
> as.Date(data.df[,1]))
> >
> >
> > spec = ugarchspec(variance.model = list(model = "fGARCH", submodel =
> > 'GARCH', garchOrder =                   c(1, 1), variance.targeting =
> > FALSE),
> >
> >                  mean.model = list(armaOrder = c(0,0), archm = TRUE),
> >
> >                  distribution.model = "std")
> >
> >
> > fit = ugarchfit(data = na.omit(data.xts['/2013-10-01','sp500']), spec =
> > spec)
> >
> >    [[alternative HTML version deleted]]
> >
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>

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