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Create GARCH conditional variance model object

Create a `garch`

model object to represent
a generalized autoregressive conditional heteroscedastic (GARCH) model. The GARCH(*P*,*Q*)
conditional variance model includes *P* past conditional
variances composing the GARCH polynomial, and *Q* past
squared innovations composing the ARCH polynomial.

Use `garch`

to create a model with known or
unknown coefficients, and then estimate any unknown coefficients from
data using `estimate`

.
You can also simulate or forecast conditional variances from fully
specified models using `simulate`

or `forecast`

, respectively.

For more information about `garch`

model objects,
see `garch`

.

`Mdl = garch`

`Mdl = garch(P,Q)`

`Mdl = garch(Name,Value)`

creates
a GARCH model with additional options specified by one or more `Mdl`

= garch(`Name,Value`

)`Name,Value`

pair
arguments. For example, you can specify a conditional variance model
constant, the number of ARCH polynomial lags, and the innovation distribution.

[1] Tsay, R. S. *Analysis of Financial
Time Series*. 3rd ed. Hoboken, NJ: John Wiley & Sons,
Inc., 2010.

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