(EViews 10) How to perform ARCH and GARCH model with interpretation model 2

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The classical theories and asset pricing model assume Volatility is constant over the period. However, most of the financial data exhibited any one or more of the following features:
1. Volatility clustering
• Some periods are high volatile while others are less.
• Big shocks (residuals) tend to follow big shocks in either direction and small shocks (residuals) are follow small shocks (residuals),( This applies a strong Autocorrelation).
ARCH (1) Model:
σ2t = αo +β1Ut-12 (1)
This is (variance equation in ARCH Model)
ARCH Model are highly useful in many situations where the prediction of volatility is important . For example, asset holders want to forecast both MEAN and VARIANCE of the return.
 MEAN EQUATION: is the forecasting equation
rt = mt + ut
Where: rt= is the actual return
mt = is the MEAN return and
ut = is the directional actual return from its mean (error Term).
 VARIANCE EQUATION OF ARCH:
ARCH Model says that the variance of the error term at a time (t) called (conditional variance) depends on the square error term(U2t-1 ) from previous period as:
The general specification of the ARCH Model:
σ2t = αo +β1Ut-1 2+….+βpUt-12
What Is a GARCH Model?
Generalized Autoregressive Conditional Heteroskedasticity, or GARCH, is an extension of the ARCH model that incorporates a moving average component together with the autoregressive component.
Specifically, the model includes lag variance terms (e.g. the observations if modeling the white noise residual errors of another process), together with lag residual errors from a mean process.
As such, the model introduces a new parameter “p” that describes the number of lag variance terms:
p: The number of lag variances to include in the GARCH model.
q: The number of lag residual errors to include in the GARCH model.
the p and q parameters GARCH (p, q); for example, GARCH (1, 1) would be a first
order GARCH model.
Now the question is: why does it make GARCH (1,1) Model more than the ARCH (1) Model?
GARCH Model (1.1):
σ2t = αo +β1Ut-12 + β2 σt-12 (2)
The main difference between ARCH (1) and GARCH (1,1) Model. The variance of two models is exactly the same [α0 and β1Ut-12] where the variance of the error term(σt-12) is also captured or predicted using the variance value of the error term in the previous time period.
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