predict.bayesDccGarch: Bayesian forecast for volatilities and coditional...

In bayesDccGarch: Methods and Tools for Bayesian Dynamic Conditional Correlation GARCH(1,1) Model

Bayesian forecast for volatilities and coditional correlations

Description

Bayesian forecast for volatilities and coditional correlations

Usage

## S3 method for class 'bayesDccGarch'
predict(object, ..., n_ahead = 5, bayes = T)

Arguments

object	a bayesDccGarch object
...	default argument of predict function, not used
n_ahead	number of steps ahead forecast
bayes	a boolean. If True, then the forecast is calculated as being the average of the forecasts across all states in the Markov chain (much slower). If False then predictions are calculated using estimation parameters (much faster).

Value

A list with elements H and R

References

Engle, R.F. and Sheppard, K. Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, 2001, NBER Working Paper.

Examples

out = bayesDccGarch(DaxCacNik)
predict.bayesDccGarch(out, n_ahead=5)

bayesDccGarch documentation built on April 22, 2023, 9:08 a.m.