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\name{volatility-methods}
\docType{methods}
% \alias{volatility-methods}
% \alias{volatility,ANY-method}
% \alias{volatility,fGARCH-method}
\alias{volatility.fGARCH}
\title{Extract GARCH Model Volatility}
\description{
Extracts volatility from a fitted GARCH object.
}
\usage{
% \S4method{volatility}{fGARCH}(object, type = c("sigma", "h"), \dots)
\method{volatility}{fGARCH}(object, type = c("sigma", "h"), \dots)
}
\arguments{
\item{object}{
an object of class \code{fGARCH} as returned from the function
\code{garchFit()}.
}
\item{type}{
a character string denoting if the conditional standard
deviations \code{"sigma"} or the variances \code{"h"} should
be returned.
}
\item{\dots}{
additional arguments to be passed.
}
}
\section{Methods}{
\describe{
\item{object = "ANY"}{
Generic function.
}
\item{object = "fGARCH"}{
Extractor function for volatility or standard deviation from
an object of class \code{"fGARCH"}.
}
}
}
\details{
The function extracts the \code{@volatility} from the slots
\code{@sigma.t} or \code{@h.t} of an object of class \code{"fGARCH"}
as returned by the function \code{garchFit}.
The class of the returned value depends on the input to the
function \code{garchFit} who created the object. The returned
value is always of the same class as the input object to the
argument \code{data} in the function \code{garchFit}, i.e. if
you fit a \code{"timeSeries"} object, you will get back from
the function \code{fitted} also a \code{"timeSeries"} object,
if you fit an object of class \code{"zoo"}, you will get back
again a \code{"zoo"} object. The same holds for a \code{"numeric"}
vector, for a \code{"data.frame"}, and for objects of class
\code{"ts", "mts"}.
In contrast, the slot itself returns independent of the class
of the data input always a numceric vector, i.e. the function
call r\code{slot(object, "fitted")} will return a numeric vector.
}
\note{
\code{volatility} is a generic function which extracts volatility values
from objects returned by modeling functions.
}
\author{
Diethelm Wuertz for the Rmetrics \R-port.
}
\examples{
## Swiss Pension func Index -
x = as.timeSeries(data(LPP2005REC))
## garchFit
fit = garchFit(LPP40 ~ garch(1, 1), data = 100*x, trace = FALSE)
fit
## volatility -
# Standard Deviation:
volatility = volatility(fit, type = "sigma")
head(volatility)
class(volatility)
# Variance:
volatility = volatility(fit, type = "h")
head(volatility)
class(volatility)
## slot -
volatility = slot(fit, "sigma.t")
head(volatility)
class(volatility)
volatility = slot(fit, "h.t")
head(volatility)
class(volatility)
}
\keyword{models}
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