[R-SIG-Finance] GARCH-M Modeling in R (rugarch) vs EVIEWS

[Alexios Ghalanos]

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 at 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)
> 
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> 
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