Specify EGARCH Models

Default EGARCH Model

The default EGARCH(P,Q) model in Econometrics Toolbox™ is of the form

$ε_{t} = σ_{t} z_{t},$

with Gaussian innovation distribution and

$\log σ_{t}^{2} = κ + \sum_{i = 1}^{P} γ_{i} \log σ_{t - i}^{2} + \sum_{j = 1}^{Q} α_{j} [\frac{| ε_{t - j} |}{σ_{t - j}} - E {\frac{| ε_{t - j} |}{σ_{t - j}}}] + \sum_{j = 1}^{Q} ξ_{j} (\frac{ε_{t - j}}{σ_{t - j}}) .$

The default model has no mean offset, and the lagged log variances and standardized innovations are at consecutive lags.

You can specify a model of this form using the shorthand syntax egarch(P,Q). For the input arguments P and Q, enter the number of lagged log variances (GARCH terms), P, and lagged standardized innovations (ARCH and leverage terms), Q, respectively. The following restrictions apply:

P and Q must be nonnegative integers.
If P > 0, then you must also specify Q > 0.

When you use this shorthand syntax, egarch creates an egarch model with these default property values.

Property	Default Value
`P`	Number of GARCH terms, P
`Q`	Number of ARCH and leverage terms, Q
`Offset`	`0`
`Constant`	`NaN`
`GARCH`	Cell vector of `NaN`s
`ARCH`	Cell vector of `NaN`s
`Leverage`	Cell vector of `NaN`s
`Distribution`	`"Gaussian"`

To assign nondefault values to any properties, you can modify the created model using dot notation.

To illustrate, consider specifying the EGARCH(1,1) model

$ε_{t} = σ_{t} z_{t},$

with Gaussian innovation distribution and

$\log σ_{t}^{2} = κ + γ_{1} \log σ_{t - 1}^{2} + α_{1} [\frac{| ε_{t - 1} |}{σ_{t - 1}} - E {\frac{| ε_{t - 1} |}{σ_{t - 1}}}] + ξ_{1} (\frac{ε_{t - 1}}{σ_{t - 1}}) .$

Mdl = egarch(1,1)

Mdl = 
  egarch with properties:

     Description: "EGARCH(1,1) Conditional Variance Model (Gaussian Distribution)"
    Distribution: Name = "Gaussian"
               P: 1
               Q: 1
        Constant: NaN
           GARCH: {NaN} at lag [1]
            ARCH: {NaN} at lag [1]
        Leverage: {NaN} at lag [1]
          Offset: 0

The created model, Mdl, has NaNs for all model parameters. A NaN value signals that a parameter needs to be estimated or otherwise specified by the user. All parameters must be specified to forecast or simulate the model

To estimate parameters, input the model (along with data) to estimate. This returns a new fitted egarch model. The fitted model has parameter estimates for each input NaN value.

Calling egarch without any input arguments returns an EGARCH(0,0) model specification with default property values:

DefaultMdl = egarch

DefaultMdl = 
  egarch with properties:

     Description: "EGARCH(0,0) Conditional Variance Model (Gaussian Distribution)"
    Distribution: Name = "Gaussian"
               P: 0
               Q: 0
        Constant: NaN
           GARCH: {}
            ARCH: {}
        Leverage: {}
          Offset: 0

Specify Default EGARCH Model

Open Live Script

This example shows how to use the shorthand egarch(P,Q) syntax to specify the default EGARCH(P, Q) model, $ε_{t} = σ_{t} z_{t}$ with a Gaussian innovation distribution and

By default, all parameters in the created model have unknown values.

Specify the default EGARCH(1,1) model:

Mdl = egarch(1,1)

Mdl = 
  egarch with properties:

     Description: "EGARCH(1,1) Conditional Variance Model (Gaussian Distribution)"
    Distribution: Name = "Gaussian"
               P: 1
               Q: 1
        Constant: NaN
           GARCH: {NaN} at lag [1]
            ARCH: {NaN} at lag [1]
        Leverage: {NaN} at lag [1]
          Offset: 0

The output shows that the created model, Mdl, has NaN values for all model parameters: the constant term, the GARCH coefficient, the ARCH coefficient, and the leverage coefficient. You can modify the created model using dot notation, or input it (along with data) to estimate.

Using Name-Value Pair Arguments

The most flexible way to specify EGARCH models is using name-value pair arguments. You do not need, nor are you able, to specify a value for every model property. egarch assigns default values to any model properties you do not (or cannot) specify.

The general EGARCH(P,Q) model is of the form

$y_{t} = μ + ε_{t},$

where $ε_{t} = σ_{t} z_{t}$ and

The innovation distribution can be Gaussian or Student’s t. The default distribution is Gaussian.

In order to estimate, forecast, or simulate a model, you must specify the parametric form of the model (e.g., which lags correspond to nonzero coefficients, the innovation distribution) and any known parameter values. You can set any unknown parameters equal to NaN, and then input the model to estimate (along with data) to get estimated parameter values.

egarch (and estimate) returns a model corresponding to the model specification. You can modify models to change or update the specification. Input models (with no NaN values) to forecast or simulate for forecasting and simulation, respectively. Here are some example specifications using name-value arguments.

Model	Specification
$y_{t} = ε_{t}$ $ε_{t} = σ_{t} z_{t}$ z_t Gaussian $\begin{array}{l} \log σ_{t}^{2} = κ + γ_{1} \log σ_{t - 1}^{2} + \dots \\ α_{1} [\frac{\| ε_{t - 1} \|}{σ_{t - 1}} - E {\frac{\| ε_{t - 1} \|}{σ_{t - 1}}}] + ξ_{1} (\frac{ε_{t - 1}}{σ_{t - 1}}) \end{array}$	`egarch('GARCH',NaN,'ARCH',NaN,... 'Leverage',NaN)` or `egarch(1,1)`
$y_{t} = μ + ε_{t}$ $ε_{t} = σ_{t} z_{t}$ z_t Student’s t with unknown degrees of freedom $\begin{array}{l} \log σ_{t}^{2} = κ + γ_{1} \log σ_{t - 1}^{2} + \dots \\ α_{1} [\frac{\| ε_{t - 1} \|}{σ_{t - 1}} - E {\frac{\| ε_{t - 1} \|}{σ_{t - 1}}}] + ξ_{1} (\frac{ε_{t - 1}}{σ_{t - 1}}) \end{array}$	`egarch('Offset',NaN,'GARCH',NaN,... 'ARCH',NaN,'Leverage',NaN,... 'Distribution','t')`
$y_{t} = ε_{t}$ $ε_{t} = σ_{t} z_{t}$ z_t Student’s t with eight degrees of freedom $\begin{array}{l} \log σ_{t}^{2} = - 0.1 + 0.4 \log σ_{t - 1}^{2} + \dots \\ 0.3 [\frac{\| ε_{t - 1} \|}{σ_{t - 1}} - E {\frac{\| ε_{t - 1} \|}{σ_{t - 1}}}] - 0.1 (\frac{ε_{t - 1}}{σ_{t - 1}}) \end{array}$	`egarch('Constant',-0.1,'GARCH',0.4,... 'ARCH',0.3,'Leverage',-0.1,... 'Distribution',struct('Name','t','DoF',8))`

Here is a full description of the name-value arguments you can use to specify EGARCH models.

Note

You cannot assign values to the properties P and Q. egarch sets P equal to the largest GARCH lag, and Q equal to the largest lag with a nonzero standardized innovation coefficient, including ARCH and leverage coefficients.

Name-Value Arguments for EGARCH Models

Name	Corresponding EGARCH Model Term(s)	When to Specify
`Offset`	Mean offset, μ	To include a nonzero mean offset. For example, `'Offset',0.2`. If you plan to estimate the offset term, specify `'Offset',NaN`. By default, `Offset` has value `0` (meaning, no offset).
`Constant`	Constant in the conditional variance model, κ	To set equality constraints for κ. For example, if a model has known constant –0.1, specify `'Constant',-0.1`. By default, `Constant` has value `NaN`.
`GARCH`	GARCH coefficients, $γ_{1}, \dots, γ_{P}$	To set equality constraints for the GARCH coefficients. For example, to specify an EGARCH(1,1) model with $γ_{1} = 0.6,$ specify `'GARCH',0.6`. You only need to specify the nonzero elements of `GARCH`. If the nonzero coefficients are at nonconsecutive lags, specify the corresponding lags using `GARCHLags`. Any coefficients you specify must satisfy all stationarity constraints.
`GARCHLags`	Lags corresponding to nonzero GARCH coefficients	`GARCHLags` is not a model property. Use this argument as a shortcut for specifying `GARCH` when the nonzero GARCH coefficients correspond to nonconsecutive lags. For example, to specify nonzero GARCH coefficients at lags 1 and 3, e.g., nonzero $γ_{1}$ and $γ_{3},$ specify `'GARCHLags',[1,3]`. Use `GARCH` and `GARCHLags` together to specify known nonzero GARCH coefficients at nonconsecutive lags. For example, if $γ_{1} = 0.3$ and $γ_{3} = 0.1,$ specify `'GARCH',{0.3,0.1},'GARCHLags',[1,3]`
`ARCH`	ARCH coefficients, $α_{1}, \dots, α_{Q}$	To set equality constraints for the ARCH coefficients. For example, to specify an EGARCH(1,1) model with $α_{1} = 0.3,$ specify `'ARCH',0.3`. You only need to specify the nonzero elements of `ARCH`. If the nonzero coefficients are at nonconsecutive lags, specify the corresponding lags using `ARCHLags`.
`ARCHLags`	Lags corresponding to nonzero ARCH coefficients	`ARCHLags` is not a model property. Use this argument as a shortcut for specifying `ARCH` when the nonzero ARCH coefficients correspond to nonconsecutive lags. For example, to specify nonzero ARCH coefficients at lags 1 and 3, e.g., nonzero $α_{1}$ and $α_{3},$ specify `'ARCHLags',[1,3]`. Use `ARCH` and `ARCHLags` together to specify known nonzero ARCH coefficients at nonconsecutive lags. For example, if $α_{1} = 0.4$ and $α_{3} = 0.2,$ specify `'ARCH',{0.4,0.2},'ARCHLags',[1,3]`
`Leverage`	Leverage coefficients, $ξ_{1}, \dots, ξ_{Q}$	To set equality constraints for the leverage coefficients. For example, to specify an EGARCH(1,1) model with $ξ_{1} = - 0.1,$ specify `'Leverage',-0.1`. You only need to specify the nonzero elements of `Leverage`. If the nonzero coefficients are at nonconsecutive lags, specify the corresponding lags using `LeverageLags`.
`LeverageLags`	Lags corresponding to nonzero leverage coefficients	`LeverageLags` is not a model property. Use this argument as a shortcut for specifying `Leverage` when the nonzero leverage coefficients correspond to nonconsecutive lags. For example, to specify nonzero leverage coefficients at lags 1 and 3, e.g., nonzero $ξ_{1}$ and $ξ_{3},$ specify `'LeverageLags',[1,3]`. Use `Leverage` and `LeverageLags` together to specify known nonzero leverage coefficients at nonconsecutive lags. For example, if $ξ_{1} = - 0.2$ and $ξ_{3} = - 0.1,$ specify `'Leverage',{-0.2,-0.1},'LeverageLags',[1,3]`.
`Distribution`	Distribution of the innovation process	Use this argument to specify a Student’s t innovation distribution. By default, the innovation distribution is Gaussian. For example, to specify a t distribution with unknown degrees of freedom, specify `'Distribution','t'`. To specify a t innovation distribution with known degrees of freedom, assign `Distribution` a data structure with fields `Name` and `DoF`. For example, for a t distribution with nine degrees of freedom, specify `'Distribution',struct('Name','t','DoF',9)`.

Specify EGARCH Model Using Econometric Modeler App

You can specify the lag structure, innovation distribution, and leverages of EGARCH models using the Econometric Modeler app. The app treats all coefficients as unknown and estimable, including the degrees of freedom parameter for a t innovation distribution.

At the command line, open the Econometric Modeler app.

econometricModeler

Alternatively, open the app from the apps gallery (see Econometric Modeler).

In the app, you can see all supported models by selecting a time series variable for the response in the Data Browser. Then, on the Econometric Modeler tab, in the Models section, click the arrow to display the models gallery.

The GARCH Models section contains all supported conditional variance models. To specify an EGARCH model, click EGARCH. The EGARCH Model Parameters dialog box appears.

Adjustable parameters include:

GARCH Degree – The order of the GARCH polynomial.
ARCH Degree – The order of the ARCH polynomial. The value of this parameter also specifies the order of the leverage polynomial.
Include Offset – The inclusion of a model offset.
Innovation Distribution – The innovation distribution.

As you adjust parameter values, the equation in the Model Equation section changes to match your specifications. Adjustable parameters correspond to input and name-value pair arguments described in the previous sections and in the egarch reference page.

For more details on specifying models using the app, see Fitting Models to Data and Specifying Lag Operator Polynomials Interactively.

Specify EGARCH Model with Mean Offset

Open Live Script

This example shows how to specify an EGARCH(P, Q) model with a mean offset. Use name-value pair arguments to specify a model that differs from the default model.

Specify an EGARCH(1,1) model with a mean offset,

$y_{t} = μ + ε_{t},$

where $ε_{t} = σ_{t} z_{t}$ and

$\log σ_{t}^{2} = κ + γ_{1} \log σ_{t - 1}^{2} + α_{1} [\frac{| ε_{t - 1} |}{σ_{t - 1}} - E {\frac{| ε_{t - 1} |}{σ_{t - 1}}}] + ξ_{1} (\frac{ε_{t - 1}}{σ_{t - 1}}) .$

Mdl = egarch('Offset',NaN,'GARCHLags',1,'ARCHLags',1,...
    'LeverageLags',1)

Mdl = 
  egarch with properties:

     Description: "EGARCH(1,1) Conditional Variance Model with Offset (Gaussian Distribution)"
    Distribution: Name = "Gaussian"
               P: 1
               Q: 1
        Constant: NaN
           GARCH: {NaN} at lag [1]
            ARCH: {NaN} at lag [1]
        Leverage: {NaN} at lag [1]
          Offset: NaN

The mean offset appears in the output as an additional parameter to be estimated or otherwise specified.

Specify EGARCH Model with Nonconsecutive Lags

Open Live Script

This example shows how to specify an EGARCH model with nonzero coefficients at nonconsecutive lags.

Specify an EGARCH(3,1) model with nonzero GARCH terms at lags 1 and 3. Include a mean offset.

Mdl = egarch('Offset',NaN,'GARCHLags',[1,3],'ARCHLags',1,...
    'LeverageLags',1)

Mdl = 
  egarch with properties:

     Description: "EGARCH(3,1) Conditional Variance Model with Offset (Gaussian Distribution)"
    Distribution: Name = "Gaussian"
               P: 3
               Q: 1
        Constant: NaN
           GARCH: {NaN NaN} at lags [1 3]
            ARCH: {NaN} at lag [1]
        Leverage: {NaN} at lag [1]
          Offset: NaN

The unknown nonzero GARCH coefficients correspond to lagged log variances at lags 1 and 3. The output shows only the nonzero coefficients.

Display the value of GARCH:

Mdl.GARCH

ans=1×3 cell array
    {[NaN]}    {[0]}    {[NaN]}

The GARCH cell array returns three elements. The first and third elements have value NaN, indicating these coefficients are nonzero and need to be estimated or otherwise specified. By default, egarch sets the interim coefficient at lag 2 equal to zero to maintain consistency with MATLAB® cell array indexing.

Specify EGARCH Model with Known Parameter Values

Open Live Script

This example shows how to specify an EGARCH model with known parameter values. You can use such a fully specified model as an input to simulate or forecast.

Specify the EGARCH(1,1) model

$\log σ_{t}^{2} = 0.1 + 0.6 \log σ_{t - 1}^{2} + 0.2 [\frac{| ε_{t - 1} |}{σ_{t - 1}} - E {\frac{| ε_{t - 1} |}{σ_{t - 1}}}] - 0.1 (\frac{ε_{t - 1}}{σ_{t - 1}})$

with a Gaussian innovation distribution.

Mdl = egarch('Constant',0.1,'GARCH',0.6,'ARCH',0.2,...
    'Leverage',-0.1)

Mdl = 
  egarch with properties:

     Description: "EGARCH(1,1) Conditional Variance Model (Gaussian Distribution)"
    Distribution: Name = "Gaussian"
               P: 1
               Q: 1
        Constant: 0.1
           GARCH: {0.6} at lag [1]
            ARCH: {0.2} at lag [1]
        Leverage: {-0.1} at lag [1]
          Offset: 0

Because all parameter values are specified, the created model has no NaN values. The functions simulate and forecast don't accept input models with NaN values.

Specify EGARCH Model with t Innovation Distribution

Open Live Script

This example shows how to specify an EGARCH model with a Student's t innovation distribution.

Specify an EGARCH(1,1) model with a mean offset,

$y_{t} = μ + ε_{t},$

where $ε_{t} = σ_{t} z_{t}$ and

$\log σ_{t}^{2} = κ + γ_{1} \log σ_{t - 1}^{2} + α_{1} [\frac{| ε_{t - 1} |}{σ_{t - 1}} - E {\frac{| ε_{t - 1} |}{σ_{t - 1}}}] + ξ_{1} (\frac{ε_{t - 1}}{σ_{t - 1}}) .$

Assume $z_{t}$ follows a Student's t innovation distribution with 10 degrees of freedom.

tDist = struct('Name','t','DoF',10);
Mdl = egarch('Offset',NaN,'GARCHLags',1,'ARCHLags',1,...
    'LeverageLags',1,'Distribution',tDist)

Mdl = 
  egarch with properties:

     Description: "EGARCH(1,1) Conditional Variance Model with Offset (t Distribution)"
    Distribution: Name = "t", DoF = 10
               P: 1
               Q: 1
        Constant: NaN
           GARCH: {NaN} at lag [1]
            ARCH: {NaN} at lag [1]
        Leverage: {NaN} at lag [1]
          Offset: NaN

The value of Distribution is a struct array with field Name equal to 't' and field DoF equal to 10. When you specify the degrees of freedom, they aren't estimated if you input the model to estimate.

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