R: Methods for Fitted GARCH Models

garch-methods {tseries} | R Documentation

## Methods for Fitted GARCH Models

### Description

Methods for fitted GARCH model objects.

### Usage

    ## S3 method for class 'garch'
    predict(object, newdata, genuine = FALSE, ...)
    ## S3 method for class 'garch'
    coef(object, ...)
    ## S3 method for class 'garch'
    vcov(object, ...)
    ## S3 method for class 'garch'
    residuals(object, ...)
    ## S3 method for class 'garch'
    fitted(object, ...)
    ## S3 method for class 'garch'
    print(x, digits = max(3, getOption("digits") - 3), ...)
    ## S3 method for class 'garch'
    plot(x, ask = interactive(), ...)
    ## S3 method for class 'garch'
    logLik(object, ...)

### Arguments

`object, x` | an object of class `"garch"`; usually, a result of a call to `garch`.
`newdata` | a numeric vector or time series to compute GARCH predictions. Defaults to `eval(parse(text=object$series))`.
`genuine` | a logical indicating whether a genuine prediction should be made, i.e., a prediction for which there is no target observation available.
`digits` | see `printCoefmat`.
`ask` | Should the `plot` method work interactively? See `interactive`.
`...` | further arguments passed to or from other methods.

### Details

`predict` returns +/- the conditional standard deviation predictions from a fitted GARCH model.

`coef` returns the coefficient estimates.

`vcov` the associated covariance matrix estimate (outer product of gradients estimator).

`residuals` returns the GARCH residuals, i.e., the time series used to fit the model divided by the computed conditional standard deviation predictions for this series. Under the assumption of conditional normality the residual series should be i.i.d. standard normal.

`fitted` returns +/- the conditional standard deviation predictions for the series which has been used to fit the model.

`plot` graphically investigates normality and remaining ARCH effects for the residuals.

`logLik` returns the log-likelihood value of the GARCH(p, q) model represented by `object` evaluated at the estimated coefficients. It is assumed that first max(p, q) values are fixed.

### Value

For `predict` a bivariate time series (two-column matrix) of predictions.

For `coef`, a numeric vector, for `residuals` and `fitted` a univariate (vector) and a bivariate time series (two-column matrix), respectively.

For `plot` and `print`, the fitted GARCH model object.

### Author(s)

A. Trapletti

[Package tseries version 0.10-47 Index]