Volatility: Volatility filtering.

In MSGARCH: Markov-Switching GARCH Models

Volatility filtering.

Description

Method returning the in-sample conditional volatility.

Usage

Volatility(object, ...)

## S3 method for class 'MSGARCH_SPEC'
Volatility(object, par, data, ...)

## S3 method for class 'MSGARCH_ML_FIT'
Volatility(object, newdata = NULL, ...)

## S3 method for class 'MSGARCH_MCMC_FIT'
Volatility(object, newdata = NULL, ...)

Arguments

object	Model specification of class MSGARCH_SPEC created with CreateSpec or fit object of type MSGARCH_ML_FIT created with FitML or MSGARCH_MCMC_FIT created with FitMCMC.
...	Not used. Other arguments to Volatility.
par	Vector (of size d) or matrix (of size nmcmc x d) of parameter estimates where d must have the same length as the default parameters of the specification.
data	Vector (of size T) of observations.
newdata	Vector (of size T*) of new observations. (Default newdata = NULL)

Details

If a matrix of MCMC posterior draws is given, the Bayesian predictive conditional volatility is calculated.

Value

In-sample condititional volatility (vector of size T + T*) of class MSGARCH_CONDVOL.
The MSGARCH_CONDVOL class contains the plot method.

Examples

# create specification
spec <- CreateSpec()

# load data
data("SMI", package = "MSGARCH")

# in-sample volatility from specification
par <- c(0.1, 0.1, 0.8, 0.2, 0.1, 0.8, 0.99, 0.01)
vol <- Volatility(object = spec, par = par, data = SMI)
head(vol)
plot(vol)

# in-sample volatility from ML fit
fit <- FitML(spec = spec, data = SMI)
vol <- Volatility(object = fit)
head(vol)
plot(vol)

## Not run: 
# in-sample volatility from MCMC fit
set.seed(1234)
fit <- FitMCMC(spec = spec, data = SMI)
vol <- Volatility(object = fit)
head(vol)
plot(vol)

## End(Not run)

MSGARCH documentation built on Dec. 6, 2022, 1:06 a.m.