lgarchSim {lgarch}

Simulate from a univariate log-GARCH model

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)