What is difference between ARCH and Garch models?
In the ARCH(q) process the conditional variance is specified as a linear function of past sample variances only, whereas the GARCH(p, q) process allows lagged conditional variances to enter as well. This corresponds to some sort of adaptive learning mechanism.
What is a Garch model?
generalized autoregressive conditional heteroskedasticity
The generalized autoregressive conditional heteroskedasticity (GARCH) process is an approach to estimating the volatility of financial markets. Financial institutions use the model to estimate the return volatility of stocks, bonds, and other investment vehicles.
Which is the best interpretation of GARCH parameters?
Campbell et al (1996) have following interpretation on p. 483. γ 1 measures the extent to which a volatility shock today feeds through into next period’s volatility and γ 1 + δ 1 measures the rate at which this effect dies over time.
How are GARCH models different from one step forecasts?
long run average variance than the one step forecast and ultimately, the distant horizon forecast is the same for all time periods as long as a+b<1. This is just the unconditional variance. Thus the GARCH models are mean reverting and conditionally heteroskedastic but have a constant
What does the first number mean in GARCH model?
GARCH(1,1) model. The (1,1) in parentheses is a standard notation in which the first number refers to how many autoregressive lags or ARCH terms appear in the equation, while the second number refers to how many moving average lags are specified which here is often called the number of GARCH
What does the γ 1 in GARCH mean?
The γ 1 represents the adjustment to past shocks. Also, the δ 1 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.