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Create EGARCH conditional variance model object

Create an `egarch`

model object to
represent an exponential generalized autoregressive conditional heteroscedastic
(EGARCH) model.
The EGARCH(*P*,*Q*) conditional
variance model includes *P* past log conditional
variances composing the GARCH polynomial, and *Q* past
standardized innovations composing the ARCH and leverage polynomials.

Use `egarch`

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 `egarch`

model objects,
see `egarch`

.

`Mdl = egarch`

`Mdl = egarch(P,Q)`

`Mdl = egarch(Name,Value)`

creates
an EGARCH model with additional options specified by one or more `Mdl`

= egarch(`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.

An EGARCH(1,1) specification is complex enough for most applications. Typically in these models, the GARCH and ARCH coefficients are positive, and the leverage coefficients are negative. If you get these signs, then large unanticipated downward shocks increase the variance. If you get signs opposite to those expected, you might encounter difficulties inferring volatility sequences and forecasting. A negative ARCH coefficient is particularly problematic. In this case, an EGARCH model might not be the best choice for your application.

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

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