I've fit a GARCH(1,1) model in R and would like to create a plot similar to the one in this question: Is this the correct way to forecast stock price volatility using GARCH
Could someone direct me to a guide or literature that I could use to achieve this?
Here's a reproducible example using the package fGarch, I hope you can adapt it to your situation:
library("fGarch")
# Create specification for GARCH(1, 1)
spec <- garchSpec(model = list(omega = 0.05, alpha = 0.1, beta = 0.75), cond.dist = "norm")
# Simulate the model with n = 1000
sim <- garchSim(spec, n = 1000)
# Fit a GARCH (1, 1)
fit <- garchFit(formula = ~ garch(1, 1), data = sim, include.mean = F)
# Predict 40 steps ahead
pred <- predict(fit, n.ahead = 40)
# Concatenate the fitted model with the prediction, transform to time series
dat <- as.ts(c(sqrt([email protected]), pred = pred$standardDeviation))
# Create the plot
plot(window(dat, start = start(dat), end = 1000), col = "blue",
xlim = range(time(dat)), ylim = range(dat),
ylab = "Conditional SD", main = "Prediction based on GARCH model")
par(new=TRUE)
plot(window(dat, start = 1000), col = "red", axes = F, xlab = "", ylab = "", xlim = range(time(dat)), ylim = range(dat))
A thank you to the writer of the answer in this thread, I used it to get the graphics right.
Also note that you can create a plot of the predicted series, rather than the predicted conditional standard deviation, by using the plot = T argument in the call to predict.
