## What is persistence in GARCH model?

Volatility is said to be persistence if today’s returns has a large effect on the forecast variance many periods in the future. The half-life of volatility= log(0.5)/log(ARCH coefficient +GARCH coefficient), where the units would be the frequency of returns.

## What is Egarch model?

An EGARCH model is a dynamic model that addresses conditional heteroscedasticity, or volatility clustering, in an innovations process. Volatility clustering occurs when an innovations process does not exhibit significant autocorrelation, but the variance of the process changes with time.

**What is alpha and beta in GARCH?**

Alpha (ARCH term) represents how volatility reacts to new information Beta (GARCH Term) represents persistence of the volatility Alpha + Beta shows overall measurement of persistence of volatility.

**What is Tarch model?**

The idea of the Threshold ARCH (TARCH) models is to divide the distribution of the innovations into disjoint intervals and then approximate a piecewise linear function for the conditional standard deviation, see Zakoian (1991), and the conditional variance respectively, see Glosten et al. ( 1993).

### What is alpha and beta in Garch model?

Alpha (ARCH term) represents how volatility reacts to new information Beta (GARCH Term) represents persistence of the volatility Alpha + Beta shows overall measurement of persistence of volatility. https://stats.stackexchange.com/questions/61824/how-to-interpret-garch-parameters/457298#457298.

### What is Alpha in ARCH model?

The value of alpha parameter (ARCH) is interpreted as a measure of (past) innovation effect on volatility (small alpha=small impact of innovation), while beta (GARCH) – as an impact of past value of volatility on today’s volatility. = persistency in volatility and IGARCH model should be used.

**What is Egarch used for?**

**What is the difference between GARCH and Egarch?**

EGARCH vs. GARCH. There is a stylized fact that the EGARCH model captures that is not contemplated by the GARCH model, which is the empirically observed fact that negative shocks at time t-1 have a stronger impact in the variance at time t than positive shocks.