Class for the univariate GARCH Parameter Distribution.

A virtual Class: No objects may be created from it.

Class `"GARCHdistribution"`

, directly.
Class `"rGARCH"`

, by class "GARCHdistribution", distance 2.

- as.data.frame
`signature(x = "uGARCHdistribution")`

: Extracts various values from object (see note).- plot
`signature(x = "uGARCHdistribution", y = "missing")`

: Parameter Distribution Plots.- show
`signature(object = "uGARCHdistribution")`

: Parameter Distribution Summary.

The `as.data.frame`

function takes optionally 2 additional arguments, namely `window`

which
indicates the particular distribution window number for which data is required (is usually just 1 unless
the recursive option was used), and `which`

indicating the type of data required. Valid values
for the latter are “rmse” for the root mean squared error between simulation fit and actual parameters,
“stats” for various statistics computed for the simulations such as log likelihood, persistence,
unconditional variance and mean, “coef” for the estimated coefficients (i.e. the parameter distribution
and is the default choice), and “coefse” for the estimated robust standard errors of the
coefficients (i.e. the parameter standard error distribution).

The plot method offers 4 plot types, namely, Parameter Density Plots (take window as additional argument),
Bivariate Plots (take window as additional argument), Stats and RMSE (only when recursive option used) Plots.
The standard option for `which`

is used, allowing for a numeric arguments to one of the four plot types
else interactive choice via “ask”.

Alexios Ghalanos

Classes `uGARCHforecast`

, `uGARCHfit`

and
`uGARCHspec`

.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## Not run:
data(sp500ret)
spec = ugarchspec(variance.model=list(model="gjrGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(1,1), arfima=FALSE, include.mean=TRUE,
garchInMean = FALSE, inMeanType = 1), distribution.model="std")
fit = ugarchfit(data=sp500ret[, 1, drop = FALSE], out.sample = 0,
spec = spec, solver = "solnp")
dist = ugarchdistribution(fit, n.sim = 2000, n.start = 50, m.sim = 5)
## End(Not run)
``` |

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