lgarchSim: Simulate from a univariate log-GARCH model

In lgarch: Simulation and Estimation of Log-GARCH Models

Description

Simulate the y series (typically a financial return or the error in a regression) from a log-GARCH model. Optionally, the conditional standard deviation, the standardised error (z) and 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,
  c.code=TRUE)

Arguments

n	integer, length of y (i.e. number of observations)
constant	the value of the intercept in the log-volatility specification
arch	numeric vector with the arch coefficients
garch	numeric vector with the garch coefficients
xreg	numeric vector (of length n) with the conditioning values
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 normal with mean zero and unit variance
verbose	logical. If FALSE (default), then only the vector y is returned. If TRUE, then a matrix with all the output is returned
c.code	logical. If TRUE (default), then compiled C-code is used for the recursion (faster)

Details

Empty

Value

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

Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

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, mlgarch and 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 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)

##simulate from a log-GARCH w/standardised t-innovations:
set.seed(123)
n <- 500
df <- 5
z <- rt(n, df=df)/sqrt(df/(df-2))
y <- lgarchSim(n, innovations=z)

lgarch documentation built on May 1, 2019, 6:33 p.m.