EGARCH stands for exponential GARCH. EGARCH is an improved form of GARCH and models some of the market scenarios better.
For example, negative shocks (events, news, and so on) tend to impact volatility more than positive shocks.
This model differs from the traditional GARCH in structure due to the log of variance.
Let us take an example to show how to execute EGARCH in R. First define spec for EGARCH and estimate the coefficients, which can be done by executing the following code on the snp data:
> snp <- read.zoo("DataChap4SP500.csv",header = TRUE, sep = ",",format="%m/%d/%Y")
> egarchsnp.spec = ugarchspec(variance.model=list(model="eGARCH",garchOrder=c(1,1)),
+ mean.model=list(armaOrder=c(0,0)))
> egarchsnp.fit = ugarchfit(egarchsnp.spec, snp$Return)
> egarchsnp.fit
> coef(egarchsnp.fit)
This gives the coefficients as follows:
Figure 4.19: Parameter estimates of EGARCH
Now let us try to forecast, which can be done by executing the following...