This function is used as the default for the univariate model fitting i.e.
the marginal models and can be used to easily specify a different
individual marginal model specification or default in
marginal_settings().
The ARMA(ar,ma)-GARCH(arch,garch) is fitted with the distribution
given by dist that specifies the conditional density used for the
innovations.
Arguments
- ar
integer for the autoregressive order
- ma
integer for the moving average order
- arch
integer for the ARCH order
- garch
integer for the GARCH order
- dist
a single character value of the possible distributions allowed in
rugarch::ugarchspec
Value
object of class rugarch::ugarchspec
Examples
# the default is then just using
default_garch_spec()
#>
#> *---------------------------------*
#> * GARCH Model Spec *
#> *---------------------------------*
#>
#> Conditional Variance Dynamics
#> ------------------------------------
#> GARCH Model : sGARCH(1,1)
#> Variance Targeting : FALSE
#>
#> Conditional Mean Dynamics
#> ------------------------------------
#> Mean Model : ARFIMA(1,0,1)
#> Include Mean : TRUE
#> GARCH-in-Mean : FALSE
#>
#> Conditional Distribution
#> ------------------------------------
#> Distribution : sstd
#> Includes Skew : TRUE
#> Includes Shape : TRUE
#> Includes Lambda : FALSE
#>
# to specify a ARMA(2,2)-GARCH(1,1) model with normal residual distribution
default_garch_spec(ar = 2, ma = 2, dist = "norm")
#>
#> *---------------------------------*
#> * GARCH Model Spec *
#> *---------------------------------*
#>
#> Conditional Variance Dynamics
#> ------------------------------------
#> GARCH Model : sGARCH(1,1)
#> Variance Targeting : FALSE
#>
#> Conditional Mean Dynamics
#> ------------------------------------
#> Mean Model : ARFIMA(2,0,2)
#> Include Mean : TRUE
#> GARCH-in-Mean : FALSE
#>
#> Conditional Distribution
#> ------------------------------------
#> Distribution : norm
#> Includes Skew : FALSE
#> Includes Shape : FALSE
#> Includes Lambda : FALSE
#>