lgarch (version 0.2)

lgarchSim: Simulate from a log-GARCH model

Description

Simulate the y series (typically interpreted as a financial return or the error in a regression) from a log-GARCH model. Optionally, the conditional standard deviation and the standardised error (z), together with their logarithmic transformations, are also returned.

Usage

lgarchSim(n, constant = 0, arch = 0.05, garch = 0.9, xreg = NULL,
  backcast.values = list(lnsigma2 = NULL, lnz2 = NULL, xreg = NULL),
  check.stability = TRUE, innovations = NULL, verbose = FALSE)

Arguments

n

integer, length of y (i.e. no of observations)

constant

the value of the intercept in the log-volatility specification

arch

a vector with the arch coefficients

garch

a vector with the garch coefficients

xreg

a vector or a matrix with the conditioning variables

backcast.values

backcast values for the recursion (chosen automatically if NULL)

check.stability

logical. If TRUE (default) then the roots of arch+garch are checked for stability

innovations

Etiher NULL (default) or a vector of length n with the standardised errors (i.e. z). If NULL, then the innovations are N(0,1)

verbose

logical. If FALSE (default), then only the vector y is returned. If TRUE, then a matrix with all the output is returned

Value

• A zoo vector of length n if verbose = FALSE, or a zoo matrix with n rows if verbose = TRUE.

Details

Empty

References

Sucarrat, Gronneberg and Escribano (2013), 'Estimation and Inference in Univariate and Multivariate Log-GARCH-X Models When the Conditional Density is Unknown', MPRA Paper 49344: http://mpra.ub.uni-muenchen.de/49344/

See Also

mlgarchSim, lgarch, zoo

Examples

##simulate 500 observations w/default parameter values:
set.seed(123)
y <- lgarchSim(500)

##simulate the same series, but with more output:
set.seed(123)
y <- lgarchSim(500, verbose=TRUE)
head(y)

##plot the simulated values:
plot(y)

##simulate from an integrated log-GARCH:
y <- lgarchSim(500, arch=0.1, garch=0.9)

##simulate w/conditioning variable:
x <- rnorm(500)
y <- lgarchSim(500, xreg=0.05*x)

##simulate from a log-GARCH with a simple form of leverage:
z <- rnorm(500)
zneg <- as.numeric(z < 0)
zneglagged <- glag(zneg, pad=TRUE, pad.value=0)
y <- lgarchSim(500, xreg=0.05*zneglagged, innovations=z)