GARCH conditional variance time series model

Use `garch`

to specify a univariate GARCH (generalized autoregressive conditional heteroscedastic) model. The `garch`

function returns a `garch`

object specifying the functional form of a GARCH(*P*,*Q*) model, and stores its parameter values.

The key components of a `garch`

model include the:

GARCH polynomial, which is composed of lagged conditional variances. The degree is denoted by

*P*.ARCH polynomial, which is composed of the lagged squared innovations. The degree is denoted by

*Q*.

*P* and *Q* are the maximum nonzero lags in the GARCH and ARCH polynomials, respectively. Other model components include an innovation mean model offset, a conditional variance model constant, and the innovations distribution.

All coefficients are unknown (`NaN`

values) and estimable unless you specify their values using name-value pair argument syntax. To estimate models containing all or partially unknown parameter values given data, use `estimate`

. For completely specified models (models in which all parameter values are known), simulate or forecast responses using `simulate`

or `forecast`

, respectively.

returns a zero-degree conditional variance `Mdl`

= garch`garch`

object.

creates a GARCH conditional variance model object (`Mdl`

= garch(`P`

,`Q`

)`Mdl`

) with a GARCH polynomial with a degree of `P`

and an ARCH polynomial with a degree of `Q`

. The GARCH and ARCH polynomials contain all consecutive lags from 1 through their degrees, and all coefficients are `NaN`

values.

This shorthand syntax enables you to create a template in which you specify the polynomial degrees explicitly. The model template is suited for unrestricted parameter estimation, that is, estimation without any parameter equality constraints. However, after you create a model, you can alter property values using dot notation.

sets properties or additional options using name-value pair arguments. Enclose each name in quotes. For example, `Mdl`

= garch(`Name,Value`

)`'ARCHLags',[1 4],'ARCH',{0.2 0.3}`

specifies the two ARCH coefficients in `ARCH`

at lags `1`

and `4`

.

This longhand syntax enables you to create more flexible models.

The shorthand syntax provides an easy way for you to create model templates that are suitable for unrestricted parameter estimation. For example, to create a GARCH(1,2) model containing unknown parameter values, enter:

Mdl = garch(1,2);

`P`

— GARCH polynomial degreenonnegative integer

GARCH polynomial degree, specified as a nonnegative integer. In the GARCH polynomial and at time *t*, MATLAB^{®} includes all consecutive conditional variance terms from lag *t* – 1 through lag *t* – `P`

.

You can specify this argument using the
`garch`

`(P,Q)`

shorthand syntax only.

If `P`

> 0, then you must specify `Q`

as a positive integer.

**Example: **`garch(1,1)`

**Data Types: **`double`

`Q`

— ARCH polynomial degreenonnegative integer

ARCH polynomial degree, specified as a nonnegative integer. In the ARCH polynomial and at time *t*, MATLAB includes all consecutive squared innovation terms from lag *t* – 1 through lag *t* – `Q`

.

You can specify this argument using the
`garch`

`(P,Q)`

shorthand syntax only.

If `P`

> 0, then you must specify `Q`

as a positive integer.

**Example: **`garch(1,1)`

**Data Types: **`double`

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

The longhand syntax
enables you to create models in which some or all coefficients are known. During estimation,
`estimate`

imposes equality constraints on any known parameters.

`'ARCHLags',[1 4],'ARCH',{NaN NaN}`

specifies a GARCH(0,4) model and unknown, but nonzero, ARCH coefficient matrices at lags `1`

and `4`

.`'GARCHLags'`

— GARCH polynomial lags`1:P`

(default) | numeric vector of unique positive integersGARCH polynomial lags, specified as the comma-separated pair consisting of
`'GARCHLags'`

and a numeric vector of unique positive
integers.

`GARCHLags(`

is the lag corresponding to
the coefficient * j*)

`GARCH{``j`

}

. The lengths of
`GARCHLags`

and `GARCH`

must be equal.Assuming all GARCH coefficients (specified by the `GARCH`

property)
are positive or `NaN`

values, `max(GARCHLags)`

determines the value of the `P`

property.

**Example: **`'GARCHLags',[1 4]`

**Data Types: **`double`

`'ARCHLags'`

— ARCH polynomial lags `1:Q`

(default) | numeric vector of unique positive integersARCH polynomial lags, specified as the comma-separated pair consisting of `'ARCHLags'`

and a numeric vector of unique positive integers.

`ARCHLags(`

is the lag corresponding to the coefficient * j*)

`ARCH{``j`

}

. The lengths of `ARCHLags`

and `ARCH`

must be equal.Assuming all ARCH coefficients (specified by the `ARCH`

property) are positive or `NaN`

values, `max(ARCHLags)`

determines the value of the `Q`

property.

**Example: **`'ARCHLags',[1 4]`

**Data Types: **`double`

You can set writable property values when you create the model object by using name-value pair argument syntax, or after you create the model object by using dot notation. For example, to create a GARCH(1,1) model with unknown coefficients, and then specify a *t* innovation distribution with unknown degrees of freedom, enter:

Mdl = garch('GARCHLags',1,'ARCHLags',1); Mdl.Distribution = "t";

`P`

— GARCH polynomial degreenonnegative integer

This property is read-only.

GARCH polynomial degree, specified as a nonnegative integer. `P`

is
the maximum lag in the GARCH polynomial with a coefficient that is positive or
`NaN`

. Lags that are less than `P`

can have
coefficients equal to 0.

`P`

specifies the minimum number of presample conditional variances
required to initialize the model.

If you use name-value pair arguments to create the model, then MATLAB implements one of these alternatives (assuming the coefficient of the
largest lag is positive or `NaN`

):

If you specify

`GARCHLags`

, then`P`

is the largest specified lag.If you specify

`GARCH`

, then`P`

is the number of elements of the specified value. If you also specify`GARCHLags`

, then`garch`

uses`GARCHLags`

to determine`P`

instead.Otherwise,

`P`

is`0`

.

**Data Types: **`double`

`Q`

— ARCH polynomial degreenonnegative integer

This property is read-only.

ARCH polynomial degree, specified as a nonnegative integer. `Q`

is the maximum lag in the ARCH polynomial with a coefficient that is positive or `NaN`

. Lags that are less than `Q`

can have coefficients equal to 0.

`Q`

specifies the minimum number of presample innovations required to initiate the model.

If you use name-value pair arguments to create the model, then MATLAB implements one of these alternatives (assuming the coefficient of the largest lag is positive or `NaN`

):

If you specify

`ARCHLags`

, then`Q`

is the largest specified lag.If you specify

`ARCH`

, then`Q`

is the number of elements of the specified value. If you also specify`ARCHLags`

, then`garch`

uses its value to determine`Q`

instead.Otherwise,

`Q`

is`0`

.

**Data Types: **`double`

`Constant`

— Conditional variance model constant`NaN`

(default) | positive scalarConditional variance model constant, specified as a positive scalar or `NaN`

value.

**Data Types: **`double`

`GARCH`

— GARCH polynomial coefficientscell vector of positive scalars or

`NaN`

valuesGARCH polynomial coefficients, specified as a cell vector of positive scalars or `NaN`

values.

If you specify

`GARCHLags`

, then the following conditions apply.The lengths of

`GARCH`

and`GARCHLags`

are equal.`GARCH{`

is the coefficient of lag}`j`

`GARCHLags(`

.)`j`

By default,

`GARCH`

is a`numel(GARCHLags)`

-by-1 cell vector of`NaN`

values.

Otherwise, the following conditions apply.

The length of

`GARCH`

is`P`

.`GARCH{`

is the coefficient of lag}`j`

.`j`

By default,

`GARCH`

is a`P`

-by-1 cell vector of`NaN`

values.

The coefficients in `GARCH`

correspond to coefficients in an underlying `LagOp`

lag operator polynomial, and are subject to a near-zero tolerance exclusion test. If you set a coefficient to `1e–12`

or below, `garch`

excludes that coefficient and its corresponding lag in `GARCHLags`

from the model.

**Data Types: **`cell`

`ARCH`

— ARCH polynomial coefficientscell vector of positive scalars or

`NaN`

valuesARCH polynomial coefficients, specified as a cell vector of positive scalars or `NaN`

values.

If you specify

`ARCHLags`

, then the following conditions apply.The lengths of

`ARCH`

and`ARCHLags`

are equal.`ARCH{`

is the coefficient of lag}`j`

`ARCHLags(`

.)`j`

By default,

`ARCH`

is a`numel(ARCHLags)`

-by-1 cell vector of`NaN`

values.

Otherwise, the following conditions apply.

The length of

`ARCH`

is`Q`

.`ARCH{`

is the coefficient of lag}`j`

.`j`

By default,

`ARCH`

is a`Q`

-by-1 cell vector of`NaN`

values.

The coefficients in `ARCH`

correspond to coefficients in an underlying `LagOp`

lag operator polynomial, and are subject to a near-zero tolerance exclusion test. If you set a coefficient to `1e–12`

or below, `garch`

excludes that coefficient and its corresponding lag in `ARCHLags`

from the model.

**Data Types: **`cell`

`UnconditionalVariance`

— Model unconditional variancepositive scalar

This property is read-only.

The model unconditional variance, specified as a positive scalar.

The unconditional variance is

$${\sigma}_{\epsilon}^{2}=\frac{\kappa}{(1-{\displaystyle {\sum}_{i=1}^{P}{\gamma}_{i}}-{\displaystyle {\sum}_{j=1}^{Q}{\alpha}_{j}})}.$$

*κ* is the conditional variance model constant (`Constant`

).

**Data Types: **`double`

`Offset`

— Innovation mean model offset`0`

(default) | numeric scalar | `NaN`

Innovation mean model offset, or additive constant, specified as a numeric scalar or `NaN`

value.

**Data Types: **`double`

`Distribution`

— Conditional probability distribution of innovation process`"Gaussian"`

(default) | `"t"`

| structure arrayConditional probability distribution of the innovation process, specified as a string or structure array. `garch`

stores the value as a structure array.

Distribution | String | Structure Array |
---|---|---|

Gaussian | `"Gaussian"` | `struct('Name',"Gaussian")` |

Student’s t | `"t"` | `struct('Name',"t",'DoF',DoF)` |

The `'DoF'`

field specifies the *t* distribution degrees of freedom parameter.

`DoF`

> 2 or`DoF`

=`NaN`

.`DoF`

is estimable.If you specify

`"t"`

,`DoF`

is`NaN`

by default. You can change its value by using dot notation after you create the model. For example,`Mdl.Distribution.DoF = 3`

.If you supply a structure array to specify the Student's

*t*distribution, then you must specify both the`'Name'`

and`'DoF'`

fields.

**Example: **`struct('Name',"t",'DoF',10)`

`Description`

— Model descriptionstring scalar | character vector

Model description, specified as a string scalar or character vector. `garch`

stores the value as a string scalar. The default value describes the parametric form of the model, for example
`"GARCH(1,1) Conditional Variance Model (Gaussian Distribution)"`

.

**Example: **`'Description','Model 1'`

**Data Types: **`string`

| `char`

All `NaN`

-valued model parameters, which
include coefficients and the *t*-innovation-distribution degrees of freedom (if
present), are estimable. When you pass the resulting `garch`

object and
data to `estimate`

, MATLAB estimates all `NaN`

-valued parameters. During estimation,
`estimate`

treats known parameters as equality constraints, that
is,`estimate`

holds any known parameters fixed at their values.

`estimate` | Fit conditional variance model to data |

`filter` | Filter disturbances through conditional variance model |

`forecast` | Forecast conditional variances from conditional variance models |

`infer` | Infer conditional variances of conditional variance models |

`simulate` | Monte Carlo simulation of conditional variance models |

`summarize` | Display estimation results of conditional variance model |

Create a default `garch`

model object and specify its parameter values using dot notation.

Create a GARCH(0,0) model.

Mdl = garch

Mdl = garch with properties: Description: "GARCH(0,0) Conditional Variance Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 0 Q: 0 Constant: NaN GARCH: {} ARCH: {} Offset: 0

`Mdl`

is a `garch`

model. It contains an unknown constant, its offset is `0`

, and the innovation distribution is `'Gaussian'`

. The model does not have a GARCH or ARCH polynomial.

Specify two unknown ARCH coefficients for lags one and two using dot notation.

Mdl.ARCH = {NaN NaN}

Mdl = garch with properties: Description: "GARCH(0,2) Conditional Variance Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 0 Q: 2 Constant: NaN GARCH: {} ARCH: {NaN NaN} at lags [1 2] Offset: 0

The `Q`

and `ARCH`

properties are updated to `2`

and `{NaN NaN}`

. The two ARCH coefficients are associated with lags 1 and 2.

Create a `garch`

model using the shorthand notation `garch(P,Q)`

, where `P`

is the degree of the GARCH polynomial and `Q`

is the degree of the ARCH polynomial.

Create a GARCH(3,2) model.

Mdl = garch(3,2)

Mdl = garch with properties: Description: "GARCH(3,2) Conditional Variance Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 3 Q: 2 Constant: NaN GARCH: {NaN NaN NaN} at lags [1 2 3] ARCH: {NaN NaN} at lags [1 2] Offset: 0

`Mdl`

is a `garch`

model object. All properties of `Mdl`

, except `P`

, `Q`

, and `Distribution`

, are `NaN`

values. By default, the software:

Includes a conditional variance model constant

Excludes a conditional mean model offset (i.e., the offset is

`0`

)Includes all lag terms in the ARCH and GARCH lag-operator polynomials up to lags

`Q`

and`P`

, respectively

`Mdl`

specifies only the functional form of a GARCH model. Because it contains unknown parameter values, you can pass `Mdl`

and the time-series data to `estimate`

to estimate the parameters.

Create a `garch`

model using name-value pair arguments.

Specify a GARCH(1,1) model. By default, the conditional mean model offset is zero. Specify that the offset is `NaN`

.

Mdl = garch('GARCHLags',1,'ARCHLags',1,'Offset',NaN)

Mdl = garch with properties: Description: "GARCH(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] Offset: NaN

`Mdl`

is a `garch`

model object. The software sets all parameters (the properties of the model object) to `NaN`

, except `P`

, `Q`

, and `Distribution`

.

Since `Mdl`

contains `NaN`

values, `Mdl`

is only appropriate for estimation only. Pass `Mdl`

and time-series data to `estimate`

.

Create a GARCH(1,1) model with mean offset,

$${y}_{t}=0.5+{\epsilon}_{t},$$

where $${\epsilon}_{t}={\sigma}_{t}{z}_{t},$$

$${\sigma}_{t}^{2}=0.0001+0.75{\sigma}_{t-1}^{2}+0.1{\epsilon}_{t-1}^{2},$$

and $${z}_{t}$$ is an independent and identically distributed standard Gaussian process.

Mdl = garch('Constant',0.0001,'GARCH',0.75,... 'ARCH',0.1,'Offset',0.5)

Mdl = garch with properties: Description: "GARCH(1,1) Conditional Variance Model with Offset (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 1 Q: 1 Constant: 0.0001 GARCH: {0.75} at lag [1] ARCH: {0.1} at lag [1] Offset: 0.5

`garch`

assigns default values to any properties you do not specify with name-value pair arguments.

Access the properties of a `garch`

model object using dot notation.

Create a `garch`

model object.

Mdl = garch(3,2)

Mdl = garch with properties: Description: "GARCH(3,2) Conditional Variance Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 3 Q: 2 Constant: NaN GARCH: {NaN NaN NaN} at lags [1 2 3] ARCH: {NaN NaN} at lags [1 2] Offset: 0

Remove the second GARCH term from the model. That is, specify that the GARCH coefficient of the second lagged conditional variance is `0`

.

Mdl.GARCH{2} = 0

Mdl = garch with properties: Description: "GARCH(3,2) Conditional Variance Model (Gaussian Distribution)" Distribution: Name = "Gaussian" P: 3 Q: 2 Constant: NaN GARCH: {NaN NaN} at lags [1 3] ARCH: {NaN NaN} at lags [1 2] Offset: 0

The GARCH polynomial has two unknown parameters corresponding to lags 1 and 3.

Display the distribution of the disturbances.

Mdl.Distribution

`ans = `*struct with fields:*
Name: "Gaussian"

The disturbances are Gaussian with mean 0 and variance 1.

Specify that the underlying I.I.D. disturbances have a *t* distribution with five degrees of freedom.

Mdl.Distribution = struct('Name','t','DoF',5)

Mdl = garch with properties: Description: "GARCH(3,2) Conditional Variance Model (t Distribution)" Distribution: Name = "t", DoF = 5 P: 3 Q: 2 Constant: NaN GARCH: {NaN NaN} at lags [1 3] ARCH: {NaN NaN} at lags [1 2] Offset: 0

Specify that the ARCH coefficients are 0.2 for the first lag and 0.1 for the second lag.

Mdl.ARCH = {0.2 0.1}

Mdl = garch with properties: Description: "GARCH(3,2) Conditional Variance Model (t Distribution)" Distribution: Name = "t", DoF = 5 P: 3 Q: 2 Constant: NaN GARCH: {NaN NaN} at lags [1 3] ARCH: {0.2 0.1} at lags [1 2] Offset: 0

To estimate the remaining parameters, you can pass `Mdl`

and your data to `estimate`

and use the specified parameters as equality constraints. Or, you can specify the rest of the parameter values, and then simulate or forecast conditional variances from the GARCH model by passing the fully specified model to `simulate`

or `forecast`

, respectively.

Fit a GARCH model to an annual time series of Danish nominal stock returns from 1922-1999.

Load the `Data_Danish`

data set. Plot the nominal returns (`nr`

).

load Data_Danish; nr = DataTable.RN; figure; plot(dates,nr); hold on; plot([dates(1) dates(end)],[0 0],'r:'); % Plot y = 0 hold off; title('Danish Nominal Stock Returns'); ylabel('Nominal return (%)'); xlabel('Year');

The nominal return series seems to have a nonzero conditional mean offset and seems to exhibit volatility clustering. That is, the variability is smaller for earlier years than it is for later years. For this example, assume that a GARCH(1,1) model is appropriate for this series.

Create a GARCH(1,1) model. The conditional mean offset is zero by default. To estimate the offset, specify that it is `NaN`

.

Mdl = garch('GARCHLags',1,'ARCHLags',1,'Offset',NaN);

Fit the GARCH(1,1) model to the data.

EstMdl = estimate(Mdl,nr);

GARCH(1,1) Conditional Variance Model with Offset (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ _________ Constant 0.0044476 0.007814 0.56918 0.56923 GARCH{1} 0.84932 0.26495 3.2056 0.0013477 ARCH{1} 0.07325 0.14953 0.48986 0.62423 Offset 0.11227 0.039214 2.8629 0.0041974

`EstMdl`

is a fully specified `garch`

model object. That is, it does not contain `NaN`

values. You can assess the adequacy of the model by generating residuals using `infer`

, and then analyzing them.

To simulate conditional variances or responses, pass `EstMdl`

to `simulate`

.

To forecast innovations, pass `EstMdl`

to `forecast`

.

Simulate conditional variance or response paths from a fully specified `garch`

model object. That is, simulate from an estimated `garch`

model or a known `garch`

model in which you specify all parameter values.

Load the `Data_Danish`

data set.

```
load Data_Danish;
nr = DataTable.RN;
```

Create a GARCH(1,1) model with an unknown conditional mean offset. Fit the model to the annual nominal return series.

Mdl = garch('GARCHLags',1,'ARCHLags',1,'Offset',NaN); EstMdl = estimate(Mdl,nr);

GARCH(1,1) Conditional Variance Model with Offset (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ _________ Constant 0.0044476 0.007814 0.56918 0.56923 GARCH{1} 0.84932 0.26495 3.2056 0.0013477 ARCH{1} 0.07325 0.14953 0.48986 0.62423 Offset 0.11227 0.039214 2.8629 0.0041974

Simulate 100 paths of conditional variances and responses for each period from the estimated GARCH model.

numObs = numel(nr); % Sample size (T) numPaths = 100; % Number of paths to simulate rng(1); % For reproducibility [VSim,YSim] = simulate(EstMdl,numObs,'NumPaths',numPaths);

`VSim`

and `YSim`

are `T`

-by- `numPaths`

matrices. Rows correspond to a sample period, and columns correspond to a simulated path.

Plot the average and the 97.5% and 2.5% percentiles of the simulated paths. Compare the simulation statistics to the original data.

VSimBar = mean(VSim,2); VSimCI = quantile(VSim,[0.025 0.975],2); YSimBar = mean(YSim,2); YSimCI = quantile(YSim,[0.025 0.975],2); figure; subplot(2,1,1); h1 = plot(dates,VSim,'Color',0.8*ones(1,3)); hold on; h2 = plot(dates,VSimBar,'k--','LineWidth',2); h3 = plot(dates,VSimCI,'r--','LineWidth',2); hold off; title('Simulated Conditional Variances'); ylabel('Cond. var.'); xlabel('Year'); subplot(2,1,2); h1 = plot(dates,YSim,'Color',0.8*ones(1,3)); hold on; h2 = plot(dates,YSimBar,'k--','LineWidth',2); h3 = plot(dates,YSimCI,'r--','LineWidth',2); hold off; title('Simulated Nominal Returns'); ylabel('Nominal return (%)'); xlabel('Year'); legend([h1(1) h2 h3(1)],{'Simulated path' 'Mean' 'Confidence bounds'},... 'FontSize',7,'Location','NorthWest');

Forecast conditional variances from a fully specified `garch`

model object. That is, forecast from an estimated `garch`

model or a known `garch`

model in which you specify all parameter values. The example follows from Estimate GARCH Model.

Load the `Data_Danish`

data set.

```
load Data_Danish;
nr = DataTable.RN;
```

Create a GARCH(1,1) model with an unknown conditional mean offset, and fit the model to the annual, nominal return series.

Mdl = garch('GARCHLags',1,'ARCHLags',1,'Offset',NaN); EstMdl = estimate(Mdl,nr);

GARCH(1,1) Conditional Variance Model with Offset (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ _________ Constant 0.0044476 0.007814 0.56918 0.56923 GARCH{1} 0.84932 0.26495 3.2056 0.0013477 ARCH{1} 0.07325 0.14953 0.48986 0.62423 Offset 0.11227 0.039214 2.8629 0.0041974

Forecast the conditional variance of the nominal return series 10 years into the future using the estimated GARCH model. Specify the entire returns series as presample observations. The software infers presample conditional variances using the presample observations and the model.

numPeriods = 10; vF = forecast(EstMdl,numPeriods,nr);

Plot the forecasted conditional variances of the nominal returns. Compare the forecasts to the observed conditional variances.

v = infer(EstMdl,nr); figure; plot(dates,v,'k:','LineWidth',2); hold on; plot(dates(end):dates(end) + 10,[v(end);vF],'r','LineWidth',2); title('Forecasted Conditional Variances of Nominal Returns'); ylabel('Conditional variances'); xlabel('Year'); legend({'Estimation sample cond. var.','Forecasted cond. var.'},... 'Location','Best');

A *GARCH model* is a dynamic model that addresses conditional heteroscedasticity, or volatility clustering, in an innovations process. Volatility clustering occurs when an innovations process does not exhibit significant autocorrelation, but the variance of the process changes with time.

A GARCH model posits that the current conditional variance is the sum of these linear processes, with coefficients for each term:

Past conditional variances (the GARCH component or polynomial)

Past squared innovations (the ARCH component or polynomial)

Constant offsets for the innovation mean and conditional variance models

Consider the time series

$${y}_{t}=\mu +{\epsilon}_{t},$$

where $${\epsilon}_{t}={\sigma}_{t}{z}_{t}.$$ The GARCH(*P*,*Q*) conditional variance process, $${\sigma}_{t}^{2}$$, has the form

$${\sigma}_{t}^{2}=\kappa +{\gamma}_{1}{\sigma}_{t-1}^{2}+\dots +{\gamma}_{P}{\sigma}_{t-P}^{2}+{\alpha}_{1}{\epsilon}_{t-1}^{2}+\dots +{\alpha}_{Q}{\epsilon}_{t-Q}^{2}.$$

In lag operator notation, the model is

$$\left(1-{\gamma}_{1}L-\dots -{\gamma}_{P}{L}^{P}\right){\sigma}_{t}^{2}=\kappa +\left({\alpha}_{1}L+\dots +{\alpha}_{Q}{L}^{Q}\right){\epsilon}_{t}^{2}.$$

The table shows how the variables correspond to the properties of the `garch`

model object.

Variable | Description | Property |
---|---|---|

μ | Innovation mean model constant offset | `'Offset'` |

κ > 0 | Conditional variance model constant | `'Constant'` |

$${\gamma}_{i}\ge 0$$ | GARCH component coefficients | `'GARCH'` |

$${\alpha}_{j}\ge 0$$ | ARCH component coefficients | `'ARCH'` |

z_{t} | Series of independent random variables with mean 0 and variance 1 | `'Distribution'` |

For stationarity and positivity, GARCH models use these constraints:

$$\kappa >0$$

$${\gamma}_{i}\ge 0,\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{\alpha}_{j}\ge 0$$

$${\sum}_{i=1}^{P}{\gamma}_{i}+{\displaystyle {\sum}_{j=1}^{Q}{\alpha}_{j}}<1$$

Engle’s original ARCH(*Q*) model is equivalent to a GARCH(0,*Q*) specification.

GARCH models are appropriate when positive and negative shocks of equal magnitude contribute equally to volatility [1].

You can specify a `garch`

model as part of a composition of conditional mean and variance models. For details, see `arima`

.

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

- Specify GARCH Models
- Modify Properties of Conditional Variance Models
- Specify Conditional Mean and Variance Models
- Infer Conditional Variances and Residuals
- Compare Conditional Variance Models Using Information Criteria
- Simulate GARCH Models
- Forecast a Conditional Variance Model
- Conditional Variance Models
- GARCH Model

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