coef.tvgarch {tvgarch}

Extraction functions (S3 methods) for univarate 'tvgarch' objects

Description

Extraction functions (S3 methods) for objects of class 'tvgarch'.

Usage

  ## S3 method for class 'tvgarch'
coef(object, spec = c("tvgarch", "garch", "tv"), ...)
  ## S3 method for class 'tvgarch'
fitted(object, spec = c("tvgarch", "garch", "tv"), 
                            as.zoo = TRUE, ...)
  ## S3 method for class 'tvgarch'
logLik(object, ...)
  ## S3 method for class 'tvgarch'
nobs(object, ...)
  ## S3 method for class 'tvgarch'
plot(x, spec = c("tvgarch", "garch", "tv"), ...)                            
  ## S3 method for class 'tvgarch'
predict(object, n.ahead = 10, newxtv = NULL, 
                             newxreg = NULL, newindex = NULL, n.sim = 5000, 
                             as.zoo = TRUE, verbose = FALSE, ...)
  ## S3 method for class 'tvgarch'
print(x, ...)
  ## S3 method for class 'tvgarch'
quantile(x, probs = 0.025, names = TRUE, type = 7, 
                              as.zoo = TRUE, ...)
  ## S3 method for class 'tvgarch'
residuals(object, as.zoo = TRUE, ...)
  ## S3 method for class 'tvgarch'
summary(object, ...)
  ## S3 method for class 'tvgarch'
toLatex(object, digits = 4, ...)
  ## S3 method for class 'tvgarch'
vcov(object, spec = c("tvgarch", "garch", "tv"), ...)

Arguments

object

an object of class 'tvgarch'.

spec

specifies whether the function should extract specific results. If "tv", extracts results for the TV component and if "garch" extracts results for the GARCH-X component of TV-GARCH-X model. If "tvgarch", extracts results for TV-GARCH-X model. Only relevant for TV-GARCH-X models. Otherwise, extracts results for GARCH-X models.

x

an object of class 'tvgarch'.

as.zoo

logical. If TRUE, then the returned result is of class zoo.

n.ahead

integer that determines how many steps ahead predictions should be generated.

newxtv

NULL or vector with the out-of-sample transition variable. If NULL, out-of-sample component g equals intercept.g. Only relevant for TV-GARCH-X models.

newxreg

vector or matrix with the out-of-sample regressor values.

newindex

a zoo-index for the out-of-sample predictions. If NULL (default), then 1:n.ahead is used.

n.sim

integer, the number of simulations.

verbose

logical. If TRUE, then the simulations - in addition to the predictions - are returned.

probs

vector of probabilities.

names

logical, whether to return names or not.

type

integer that determines the algorithm used to compute the quantile, see quantile.

digits

integer, the number of digits in the printed LaTeX code.

...

additional arguments.

Value

coef:

parameter estimates.

fitted:

fitted conditional variance.

logLik:

optimised log-likelihood (normal density) value.

nobs:

the number of observations used in the estimation.

plot:

plot of the fitted conditional volatility.

predict:

variance predictions.

print:

print of the estimation results.

quantile:

fitted quantiles, i.e. the conditional standard deviation times the empirical quantile of the standardised innovations.

residuals:

volatility standardised residuals.

summary:

summary of estimation results.

vcov:

coefficient variance-covariance matrix.

Author(s)

Susana Campos-Martins

References

Cristina Amado and Timo Terasvirta (2013) Modelling volatility by variance decomposition, Journal of Econometrics 175, 142-153.

Cristina Amado and Timo Terasvirta (2014) Modelling changes in the unconditional variance of long stock return series, Journal of Empirical Finance 25, 15-35.

Susana Campos-Martins and Genaro Sucarrat (2024) Modeling Nonstationary Financial Volatility with the R Package tvgarch, Journal of Statistical Software 108, 1-38.

See Also

tvgarchTest, tvgarch, tvgarchSim, zoo

Examples

set.seed(123)

## Simulate from a TV(1)-GARCH(1,1) model (default):
ySim <- tvgarchSim(n = 1500)

## Estimate a TV(1)-GARCH(1,1) model:
yEst <- tvgarch(y = ySim)

## Print estimation results:
print(yEst)

## Extract and store conditional variances:
sigma2Est <- fitted(yEst)

## Plot:
plot(yEst)

## Generate predictions:
predict(yEst)