jairamjana
Member
So I link this video which explains GARCH(1,1) as a measure to forecast future volatility.
Now we know EWMA is a special case of GARCH which sums alpha and beta equal to 1 and therefore ignores any impact on long run variance, implying that variance is not mean reverting.. Again when we substitute in the formula we get E(Variance(n+t)) = Variance(n) since alpha + beta = 1.. So future volatility will always be a constant of current estimated volatility.. Now we don't know the parameters alpha , beta and gamma in practise.. we estimate them with the help of maximum likelihood method and there is a possibility that alpha + beta > 1 which according to common understanding means GARCH measure will be unstable so practitioners should use EWMA instead..
Once we are forced to abandon GARCH as a measure isn't it the case that we can never estimate future volatility henceforth and hence there is no point in forecasting..
Is my interpretation right?
Now we know EWMA is a special case of GARCH which sums alpha and beta equal to 1 and therefore ignores any impact on long run variance, implying that variance is not mean reverting.. Again when we substitute in the formula we get E(Variance(n+t)) = Variance(n) since alpha + beta = 1.. So future volatility will always be a constant of current estimated volatility.. Now we don't know the parameters alpha , beta and gamma in practise.. we estimate them with the help of maximum likelihood method and there is a possibility that alpha + beta > 1 which according to common understanding means GARCH measure will be unstable so practitioners should use EWMA instead..
Once we are forced to abandon GARCH as a measure isn't it the case that we can never estimate future volatility henceforth and hence there is no point in forecasting..
Is my interpretation right?