Forecasting with an ARMA–GARCH Model in R

Summary

The answer shows how to combine an autoregressive moving-average model for the conditional mean with a generalized autoregressive conditional heteroskedasticity model for changing variance. It points to the R package fGarch, where a specified ARMA–GARCH model can be fitted to a time series and used to produce forecasts for a chosen horizon. The example also displays a forecast plot and uses a criterion value for intervals.

This is a compact software pointer rather than a tutorial: it gives no explanation of model identification, estimation assumptions, diagnostics, or how to interpret the forecast outputs. It reports no forecast accuracy or comparison with separate ARMA and GARCH forecasts. The code’s data object and model orders are illustrative choices, so users would need to select and validate specifications for their own data before relying on predictions.

Key ideas

• An ARMA component can model the conditional mean while a GARCH component models changing conditional variance.
• The fGarch package provides functions to fit the combined model and generate forecasts in R.
• The example specifies model orders and a forecast horizon but does not explain how to choose them.
• The answer gives no diagnostics or evidence about out-of-sample forecast performance.

Tags

# How to use ARMA GARCH to do forecasting in R?


# How to use ARMA GARCH to do forecasting in R?












How to use ARMA GARCH to do forecasting in R? I only know how to use ARMA to do the prediction and GARCH to do volatility forecasting but how can we use ARMA GARCH to do forecasting in R. Can anyone solve the problem with R code. THX

## Answer by Kevin (score 0, accepted)

https://quant.stackexchange.com/a/48648

```
library(fGarch)

fit = garchFit(~ arma(2,1)+garch(1,1), data = y,include.mean=FALSE)
predict(fit, n.ahead = 10, plot=TRUE, crit_val=2)
```

see here (page 30) for more details

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