Simulate the y series (typically a collection of financial returns or regression errors) from a logGARCH model. Optionally, the conditional standard deviation and the standardised error, together with their logarithmic transformations, are also returned.
1 2 3 4 5 
n 
integer, i.e. number of observations 
constant 
vector with the values of the intercepts in the logvolatility specification 
arch 
matrix with the arch coefficients 
garch 
matrix with the garch coefficients 
xreg 
a vector (of length n) or matrix (with rows n) with the values of the conditioning variables. The first column enters the first equation, the second enters the second equation, and so on 
backcast.values 
backcast values for the recursion (chosen automatically if NULL) 
check.stability 
logical. If TRUE (default), then the system is checked for stability 
innovations 
Either NULL (default) or a vector or matrix of length n with the standardised errors. If NULL, then the innovations are multivariate N(0,1) with correlations equal to zero 
innovations.vcov 
numeric matrix, the variancecovariance matrix of the standardised multivariate normal innovations. Only applicable if innovations = NULL 
verbose 
logical. If FALSE (default), then only the matrix with the y series is returned. If TRUE, then also additional information is returned 
Empty
A zoo
matrix with n rows.
Genaro Sucarrat, http://www.sucarrat.net/
Sucarrat, Gronneberg and Escribano (2013), 'Estimation and Inference in Univariate and Multivariate LogGARCHX Models When the Conditional Density is Unknown', MPRA Paper 49344: http://mpra.ub.unimuenchen.de/49344/
lgarchSim
, mlgarch
and zoo
1 2 3 4 5 6 7 8 9 10 11 12  ##simulate 1000 observations from a multivariate
##cccloggarch(1,1) w/default parameter values:
set.seed(123)
y < mlgarchSim(1000)
##simulate the same series, but with more output:
set.seed(123)
y < mlgarchSim(1000, verbose=TRUE)
head(y)
##plot the simulated values:
plot(y)

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