garch

GARCH conditional variance time series model

Description

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.

Creation

Syntax

Mdl = garch

Mdl = garch(P,Q)

Mdl = garch(Name,Value)

Description

Mdl = garch returns a zero-degree conditional variance garch object.

Mdl = garch(P,Q) creates a GARCH conditional variance model object (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.

Mdl = garch(Name,Value) sets properties or additional options using name-value pair arguments. Enclose each name in quotes. For example, '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.

Input Arguments

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);

To impose equality constraints on parameter values during estimation, set the appropriate property values using dot notation.

P — GARCH polynomial degree

nonnegative 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 degree

nonnegative 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

Name-Value Pair Arguments

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.

Example: '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 integers

GARCH polynomial lags, specified as the comma-separated pair consisting of 'GARCHLags' and a numeric vector of unique positive integers.

GARCHLags(j) is the lag corresponding to the coefficient 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 integers

ARCH polynomial lags, specified as the comma-separated pair consisting of 'ARCHLags' and a numeric vector of unique positive integers.

ARCHLags(j) is the lag corresponding to the coefficient 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

'Distribution' — Conditional probability distribution of innovation process

'Gaussian' (default) | 't' | structure array

Conditional probability distribution of the innovation process, specified as the comma-separated pair consisting of 'Distribution' and a string or 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 want estimate to estimate this parameter along with all other unknown parameters, then its value must be NaN.

• If you specify "t" for Distribution, then DoF is NaN. 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 for Distribution to specify the Student's t distribution, then you must specify both the 'Name' and 'DoF' fields.

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

Data Types: char | string | struct

Properties

You can set writable property values when you create the model object by using name-value pair argument syntax, or after you create 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 degree

nonnegative 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 degree

nonnegative 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 scalar

Conditional variance model constant, specified as a positive scalar or NaN value.

Data Types: double

GARCH — GARCH polynomial coefficients

cell vector of positive scalars or NaN values

GARCH 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{j} is the coefficient of lag 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{j} is the coefficient of lag j.

  • By default, GARCH is a P-by-1 cell vector of NaN values.

Data Types: cell

ARCH — ARCH polynomial coefficients

cell vector of positive scalars or NaN values

ARCH 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{j} is the coefficient of lag 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{j} is the coefficient of lag j.

  • By default, ARCH is a Q-by-1 cell vector of NaN values.

Data Types: cell

UnconditionalVariance — Model unconditional variance

positive 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

structure array

Conditional probability distribution of the innovations process, specified as a structure array.

The Name field stores the name of the distribution, either "Gaussian" for the Gaussian distribution or "t" for the t distribution.

If Name is "t", then Distribution also contains the DoF field, which stores the t-distribution degrees of freedom.

By default, Distribution is struct('Name',"Gaussian"). When you create the object, if you specify that the underlying innovation process has a t distribution by using the Distribution name-value pair argument, then the DoF field is NaN by default.

Data Types: struct

Description — Model description

string scalar | character vector

Model description, specified as a string scalar or character vector. By default, this property 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

Note

• 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.

• All GARCH and ARCH coefficients are subject to a near-zero tolerance exclusion test. That is, the software:

  1. Creates lag operator polynomials for each of the GARCH and ARCH components.

  2. Compares each coefficient to the default lag operator zero tolerance, 1e-12.

  3. Includes a coefficient in the model if its magnitude is greater than 1e-12, and excludes the coefficient otherwise. In other words, the software considers excluded coefficients to be sufficiently close to zero.

  For details, see LagOp.

Object Functions

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

Examples

Create Default GARCH 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 GARCH Model Using Shorthand Syntax

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 GARCH Model Using Longhand Syntax

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 GARCH Model with Known Coefficients

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 GARCH Model Properties

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.

Estimate GARCH Model

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 GARCH Model Observations and Conditional Variances

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 GARCH Model Conditional Variances

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');

More About

GARCH Model

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'
zt	Series of independent random variables with mean 0 and variance 1	'Distribution'

For stationarity and positivity, GARCH models use these constraints:

• $$\kappa >0$$

• $${\gamma }_i\ge 0,{\alpha }_j\ge 0$$

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

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].

Tips

You can specify a garch model as part of a composition of conditional mean and variance models. For details, see arima.

References

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