Exercise

# Specify and taste the GARCH model flavors

In the next chapters, you will see that GARCH models come in many flavors. You thus need to start off by specifying the **mean** model, the **variance** model and the **error distribution** that you want to use. The best model to use is **application-specific**. A realistic GARCH analysis thus involves specifying, estimating and testing various GARCH models.

In R, this is simple thanks to the `rugarch`

package of Alexios Ghalanos. This package has already been loaded for you. You will apply it to analyze the daily returns in `sp500ret`

.

Instructions

**100 XP**

- Use
`ugarchspec()`

to specify that you want to estimate a standard GARCH(1,1) model with constant mean and a normal distribution for the prediction errors. - Use
`ugarchfit()`

to estimate the model by maximum likelihood. - Use the method
`sigma()`

to retrieve the estimated volatilities. - Plot the volatility predictions for 2017.