Description Usage Arguments Details Value Author(s) References Examples
Specifies an ARMA-GARCH or ARMA-APARCH model.
1 2 3 4 |
model |
a list of ARMA-GARCH/APARCH model parameters: |
presample |
presample - a numeric "matrix" with 3 columns and
with max(m,n,p,q) rows.
The first culumn are the innovations, the second
the conditional variances, and the last the time series.
When the presample matrix is missing, it is constructed
as [z,h,y] where z ~ Normal(0,1), h = "uev" recursion
initialization described in Wuertz et al. (2009) and
y = |
cond.dist |
a character string naming the conditional distribution of innovations. The package was created to accept the following distributions: |
rseed |
the seed for the intitialization of the random number generator for the innovations. |
This functions uses the interface of the garchSpec
routine from package fGarch to simulate random values of the ARMA-GARCH/APARCH model with conditional GEV or stable distribution.
The returned value is an object of class "GEVSTABLEGARCHSPEC"
.
Thiago do Rego Sousa for the latest modifications
Diethelm Wuertz for the original implementation of the garchSim function from package fGarch
Wuertz, D., Chalabi, Y., with contribution from Miklovic, M., Boudt, C., Chausse, P., and others (2013). fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling, R package version 3010.82, http://CRAN.R-project.org/package=fGarch.
Wuertz, D., Chalabi, Y., Luksan, L. (2009). Parameter Estimation of ARMA Models with GARCH/ APARCH Errors: An R and SPlus SoftwareImplementation. Journal of Statistical Software, forthcoming, http://www-stat.wharton.upenn.edu/~steele/...WurtzEtAlGarch.pdf.
1 2 3 4 5 6 7 8 9 10 | # stable-GARCH from Curto et al. (2009) for the DJIA dataset
spec.stable = gsSpec(model = list(mu = 0.0596, omega = 0.0061,
alpha = 0.0497, beta = 0.9325, skew = -0.9516, shape = 1.9252),
cond.dist = "stableS1")
sim.stable = gsSim(spec = spec.stable, n = 1000)
# GEV-GARCH model from Zhao et al. (2011)
spec.gev = gsSpec(model = list(mu = 0.21, a = 0.32, omega = 0.01,
alpha = 0.45, beta = 0.08, shape = 0.08), cond.dist = "gev")
sim.gev = gsSim(spec = spec.gev, n = 1000)
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