# How to forecast monthly volatility with daily gjrGarch estimates

I'm currently writing a paper and need to regress the 22 days realized volatility of the following month on its GARCH estimate and the 126days realized volatility up to t=1

The paper im referring to describes its procedure as:

"first fit a GARCH process on the daily returns. then regress the future realized 22day return volatility(o22,t+1) on the Gauch estimate (o(march,t) and the 126 days volatility and a constant"

so i did the GARCH process on my Returns:

spec<-ugarchspec(variance.model = list(model="gjrGARCH"),mean.model =    list(armaOrder=c(0,0)))
fit<-ugarchfit(spec = spec,data = FF4F\$WML)
fit


the input data contains the daily log returns of my portfolio.

but how do i get the forecast for the next months volatility out of that ? or how do i get the daily forecasted values at least for every day of my data set, so i can form the realized 22 day volatility out of them?

do i have to use ugarchforecast or ugarchroll ?

• You can use the estimated coefficients for creating a forecasting function – Vitomir Jun 20 at 9:35