I use a standard GARCH model:

rt=σtϵtσ2t=γ0+γ1r2t−1+δ1σ2t−1I have different estimates of the coefficients and I need to interpret them. Therefore I am wondering about a nice interpretation, so what does γ0,γ1 and δ1 represent?

I see that γ0 is something like a constant part. So it represents kind of an “ambient volatility”. The γ1 represents the adjustment to past shocks. Also, the δ1 is not very intuitively for me: It represents the adjustment to pas volatility. But I would like to have a better and more comprehensive interpretation of these parameters.

So can anyone give me a good explanation of what those parameters represent and how a change in the parameters could be explained (so what does it mean if e.g. the γ1 increases?).

Also, I looked it up in several books (e.g. in Tsay), but I could not find good information, so any literature recommendation about the interpretation of these parameters would be appreciated.

Edit: I would be also interested in how to interpret the persistence.

So what is exactly persistence?In some books I read, that the persistence of a GARCH(1,1) is γ1+δ1, but e.g. in the book by Carol Alexander on page 283 he talks about only the β parameter (my δ1) being the persistence parameter. So is there a difference between persistence in volatility (σt) and persistence in shocks (rt)?

**Answer**

Campbell et al (1996) have following interpretation on p. 483.

γ1 measures the extent to which a volatility shock today feeds through into next period’s volatility and γ1+δ1 measures the rate at which this effect dies over time.

According to Chan (2010) persistence of volatility occurs when γ1+δ1=1 ,and thus at is non-stationary process. This is also called as IGARCH (Integrated GARCH). Under this scenario, unconditional variance become infinite (p. 110)

Note: GARCH(1,1) can be written in the form of ARMA (1,1) to show that the persistence is given by the sum of the parameters (proof in p. 110 of Chan (2010) and p. 483 in Campbell et al (1996). Also, a2t−1−σ2t−1 is now the volatility shock.

**Attribution***Source : Link , Question Author : Stat Tistician , Answer Author : Richard Hardy*