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Create GJR conditional variance model object

Create a `gjr`

model object to represent
a Glosten, Jagannathan, and Runkle (GJR) model. The GJR(*P*,*Q*)
conditional variance model includes *P* past conditional
variances composing the GARCH polynomial, and *Q* past
squared innovations composing the ARCH and leverage polynomials.

Use `gjr`

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 `gjr`

model objects,
see `gjr`

.

`Mdl = gjr`

`Mdl = gjr(P,Q)`

`Mdl = gjr(Name,Value)`

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

= gjr(`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] Glosten, L. R., R. Jagannathan, and D. E.
Runkle. “On the Relation between the Expected Value and the
Volatility of the Nominal Excess Return on Stocks.” *The
Journal of Finance*. Vol. 48, No. 5, 1993, pp. 1779–1801.

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

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