# forecast

Forecast conditional variances from conditional variance models

## Syntax

## Description

returns a numeric array containing paths of minimum mean squared error (MMSE),
consecutive forecasted conditional variances `V`

= forecast(`Mdl`

,`numperiods`

,`Y0`

)`V`

of the fully
specified, univariate conditional variance model `Mdl`

, over
a `numperiods`

forecast horizon. The model
`Mdl`

can be a `garch`

, `egarch`

, or `gjr`

model object. The forecasts
represent the continuation of the presample data in the numeric array
`Y0`

.

returns the table or timetable `Tbl2`

= forecast(`Mdl`

,`numperiods`

,`Tbl1`

)`Tbl2`

containing the paths of
MMSE conditional variance variable forecasts of the model
`Mdl`

over a `numperiods`

forecast
horizon. `forecast`

uses the table or timetable of
presample data `Tbl1`

to initialize the response
series.* (since R2023a)*

To initialize the forecast, `forecast`

selects the
response variable named in `Mdl.SeriesName`

or the sole
variable in `Tbl1`

. To select a different response variable
in `Tbl1`

to initialize the forecasts, use the
`PresampleResponseVariable`

name-value argument.

`[___] = forecast(___,`

specifies options using one or more name-value arguments in
addition to any of the input argument combinations in previous syntaxes.
`Name,Value`

)`forecast`

returns the output argument combination for the
corresponding input arguments. For example, `forecast(Mdl,10,Y0,V0=v0)`

initializes the conditional variances for the forecast using the presample data
in `v0`

.

## Examples

### Specify Numeric Presample Response Data to Forecast GARCH Model Conditional Variances

Forecast the conditional variance of simulated data over a 30-period horizon. Supply a vector of presample response data.

Simulate 100 observations from a GARCH(1,1) model with known parameters.

Mdl = garch(Constant=0.02,GARCH=0.8,ARCH=0.1); rng("default") % For reproducibility [v,y] = simulate(Mdl,100);

Forecast the conditional variances over a 30-period horizon. Specify the simulated response data. Plot the forecasts.

vF = forecast(Mdl,30,y); figure plot(v,"-b") hold on plot(101:130,vF,"r--",LineWidth=2); title("Forecasted Conditional Variances") legend("Simulated presample","Forecasts") hold off

Forecasts converge asymptotically to the unconditional innovation variance.

### Forecast EGARCH Model Conditional Variances

Forecast the conditional variance of simulated data over a 30-period horizon.

Simulate 100 observations from an EGARCH(1,1) model with known parameters.

Mdl = egarch(Constant=0.01,GARCH=0.6,ARCH=0.2, ... Leverage=-0.2); rng("default") % For reproducibility [v,y] = simulate(Mdl,100);

Forecast the conditional variance over a 30-period horizon. Specify the simulated data as presample responses. Plot the forecasts.

VF1 = forecast(Mdl,30,y); figure plot(v,"r-") hold on plot(101:130,VF1,"b--",LineWidth=2); title("Forecasted Conditional Variances") legend("Simulated responses","Forecasts") hold off

### Forecast GJR Model Conditional Variances

Forecast the conditional variance of simulated data over a 30-period horizon.

Simulate 100 observations from a GJR(1,1) model with known parameters.

Mdl = gjr(Constant=0.01,GARCH=0.6,ARCH=0.2, ... Leverage=0.2); rng("default") % For reproducibility [v,y] = simulate(Mdl,100);

Forecast the conditional variances over a 30-period horizon. Specify the simulated presample responses. Plot the forecasts.

vF = forecast(Mdl,30,y); figure plot(v,"r") hold on plot(101:130,vF,'b--',LineWidth=2); title("Forecasted Conditional Variances") legend("Observed","Forecasts") hold off

### Compare Conditional Variance Forecasts of NYSE Returns

*Since R2023a*

Forecast the conditional variance of the average weekly closing NASDAQ returns from fitted GARCH(1,1), EGARCH(1,1) and GJR(1,1) models.

Load the U.S. equity indices data `Data_EquityIdx.mat`

.

`load Data_EquityIdx`

The timetable `DataTimeTable`

contains the daily NASDAQ closing prices, among other indices.

Compute the weekly average closing prices of all timetable variables.

`DTTW = convert2weekly(DataTimeTable,Aggregation="mean");`

Compute the weekly percent returns and their sample mean.

DTTRet = price2ret(DTTW); DTTRet.Interval = []; DTTRet.NASDAQ = DTTRet.NASDAQ*100; T = height(DTTRet)

T = 626

meanRet = mean(DTTRet.NASDAQ)

meanRet = 0.0330

figure plot(DTTRet.Time,100*DTTRet.NASDAQ); hold on yline(100*meanRet,'--r') title("Daily NASDAQ Returns"); xlabel("Date"); ylabel("Return (%)");

The variance of the series seems to change. This change is an indication of volatility clustering. The conditional mean model offset is very close to zero.

When you plan to supply a timetable, you must ensure it has all the following characteristics:

The selected response variable is numeric and does not contain any missing values.

The timestamps in the

`Time`

variable are regular, and they are ascending or descending.

Remove all missing values from the timetable, relative to the NASDAQ returns series.

```
DTTRet = rmmissing(DTTRet,DataVariables="NASDAQ");
numobs = height(DTTRet)
```

numobs = 626

Because all sample times have observed NASDAQ returns, `rmmissing`

does not remove any observations.

Determine whether the sampling timestamps have a regular frequency and are sorted.

`areTimestampsRegular = isregular(DTTRet,"weeks")`

`areTimestampsRegular = `*logical*
1

areTimestampsSorted = issorted(DTTRet.Time)

`areTimestampsSorted = `*logical*
1

`areTimestampsRegular = 1`

indicates that the timestamps of `DTTRet`

represent a regular weekly sample. `areTimestampsSorted = 1`

indicates that the timestamps are sorted.

Fit GARCH(1,1), EGARCH(1,1), and GJR(1,1) models to the data. By default, the software sets the conditional mean model offset to zero.

```
MdlGARCH = garch(1,1);
MdlEGARCH = egarch(1,1);
MdlGJR = gjr(1,1);
EstMdlGARCH = estimate(MdlGARCH,DTTRet,ResponseVariable="NASDAQ");
```

GARCH(1,1) Conditional Variance Model (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ ___________ Constant 0.0030629 0.0011827 2.5897 0.0096065 GARCH{1} 0.86501 0.02911 29.715 4.8912e-194 ARCH{1} 0.11835 0.024582 4.8144 1.4765e-06

`EstMdlEGARCH = estimate(MdlEGARCH,DTTRet,ResponseVariable="NASDAQ"); `

EGARCH(1,1) Conditional Variance Model (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ __________ Constant -0.081262 0.030237 -2.6875 0.0071983 GARCH{1} 0.95557 0.01335 71.579 0 ARCH{1} 0.2768 0.052237 5.299 1.1645e-07 Leverage{1} -0.10519 0.025542 -4.1185 3.8142e-05

`EstMdlGJR = estimate(MdlGJR,DTTRet,ResponseVariable="NASDAQ");`

GJR(1,1) Conditional Variance Model (Gaussian Distribution): Value StandardError TStatistic PValue _________ _____________ __________ __________ Constant 0.0069063 0.0020036 3.447 0.0005668 GARCH{1} 0.78545 0.043862 17.907 1.0334e-71 ARCH{1} 0.090637 0.034313 2.6415 0.0082543 Leverage{1} 0.18663 0.054402 3.4305 0.0006025

Forecast the conditional variance for 20 weeks using the fitted models. Use the observed returns as presample innovations for the forecasts.

fh = 20; DTTVFGARCH = forecast(EstMdlGARCH,fh,DTTRet, ... PresampleResponseVariable="NASDAQ"); DTTVFEGARCH = forecast(EstMdlEGARCH,fh,DTTRet, ... PresampleResponseVariable="NASDAQ"); DTTVFGJR= forecast(EstMdlGJR,fh,DTTRet, ... PresampleResponseVariable="NASDAQ");

The forecasted conditional variance variables are called `Y_Variance`

in each returned timetable.

Plot the forecasts along with the conditional variances inferred from the data.

DTTVGARCH = infer(EstMdlGARCH,DTTRet,ResponseVariable="NASDAQ"); DTTVEGARCH = infer(EstMdlEGARCH,DTTRet,ResponseVariable="NASDAQ"); DTTVGJR = infer(EstMdlGJR,DTTRet,ResponseVariable="NASDAQ"); figure tiledlayout(3,1) nexttile plot(DTTRet.Time(end-100:end),DTTVGARCH.Y_Variance(end-100:end), ... "r",DTTVFGARCH.Time,DTTVFGARCH.Y_Variance,"b--") legend("Inferred","Forecast",Location="northeast") title("GARCH(1,1) Conditional Variances") nexttile plot(DTTRet.Time(end-100:end),DTTVEGARCH.Y_Variance(end-100:end),"r", ... DTTVFEGARCH.Time,DTTVFEGARCH.Y_Variance,"b--") legend("Inferred","Forecast",Location="northeast") title("EGARCH(1,1) Conditional Variances") nexttile plot(DTTRet.Time(end-100:end),DTTVGJR.Y_Variance(end-100:end),"r", ... DTTVFGJR.Time,DTTVFGJR.Y_Variance,'b--') legend("Inferred","Forecast",Location="northeast") title("GJR(1,1) Conditional Variances")

Plot conditional variance forecasts for the next 500 weeks after the sample.

fh = 500; DTTVF1000GARCH = forecast(EstMdlGARCH,fh,DTTRet, ... PresampleResponseVariable="NASDAQ"); DTTVF1000EGARCH = forecast(EstMdlEGARCH,fh,DTTRet, ... PresampleResponseVariable="NASDAQ"); DTTVF1000GJR= forecast(EstMdlGJR,fh,DTTRet, ... PresampleResponseVariable="NASDAQ"); figure plot(DTTVF1000GARCH.Time,DTTVF1000GARCH.Y_Variance,'b',... DTTVF1000EGARCH.Time,DTTVF1000EGARCH.Y_Variance,'r',... DTTVF1000GJR.Time,DTTVF1000GJR.Y_Variance,'k') legend("GARCH Forecast","EGARCH Foecast","GJR Forecast",Location="northeast") title("Long-Run Conditional Variance Forecast")

The forecasts converge asymptotically to the unconditional variances of their respective processes.

## Input Arguments

`numperiods`

— Forecast horizon

positive integer

Forecast horizon, or the number of time points in the forecast period, specified as a positive integer.

**Data Types: **`double`

`Y0`

— Presample response data *y*_{t}

numeric column vector | numeric matrix

_{t}

Presample response data *y _{t}* used
to infer presample innovations

*ε*, and whose conditional variance process

_{t}*σ*

_{t}^{2}is forecasted, specified as a

`numpreobs`

-by-1 numeric
column vector or a
`numpreobs`

-by-`numpaths`

numeric
matrix. When you supply `Y0`

, supply all optional data as
numeric arrays, and `forecast`

returns results in
numeric arrays.`numpreobs`

is the number of presample
observations.

`Y0`

can represent a mean 0 presample innovations series
with a variance process characterized by the conditional variance model
`Mdl`

. `Y0`

can also represent a
presample innovations series plus an offset (stored in
`Mdl.Offset`

). For more details, see Algorithms.

Each row is a presample observation, and measurements in each row occur
simultaneously. The last row contains the latest presample observation.
`numpreobs`

must be at least `Mdl.Q`

to initialize the conditional variance model. If
`numpreobs`

> `Mdl.Q`

,
`forecast`

uses only the latest
`Mdl.Q`

rows. For more details, see Time Base Partitions for Forecasting.

Columns of `Y0`

correspond to separate, independent
paths.

If

`Y0`

is a column vector, it represents a single path of the response series.`forecast`

applies it to each forecasted path. In this case, all forecast paths`Y`

derive from the same initial responses.If

`Y0`

is a matrix, each column represents a presample path of the response series.`numpaths`

is the maximum among the second dimensions of the specified presample observation matrices`Y0`

and`V0`

.

**Data Types: **`double`

`Tbl1`

— Presample data

table | timetable

*Since R2023a*

Presample data containing the response variable
*y _{t}* and, optionally, the
conditional variance variable

*σ*

_{t}^{2}used to initialize the model for the forecast, specified as a table or timetable, the same type as

`Tbl1`

, with
`numprevars`

variables and `numpreobs`

rows. You can select a response variable or conditional variance variable
from `Tbl1`

by using the
`PresampleResponseVariable`

or
`PresampleVarianceVariable`

name-value argument,
respectively.Each selected variable is single path (`numpreobs`

-by-1
vector) or multiple paths
(`numpreobs`

-by-`numpaths`

matrix) of
presample response or conditional variance data. Each row is a presample
observation, and measurements in each row occur simultaneously.
`numpreobs`

must be one of the following values:

`Mdl.Q`

when`Tbl1`

provides only presample responses`max([Mdl.P Mdl.Q])`

when`Tbl1`

also provides presample conditional variances

If you supply more rows than necessary,
`forecast`

uses the latest required number of
observations only.

If `Tbl1`

is a timetable, all the following conditions
must be true:

`Tbl1`

must represent a sample with a regular datetime time step (see`isregular`

).The datetime vector of sample timestamps

`Tbl1.Time`

must be ascending or descending.

If `Tbl1`

is a table, the last row contains the latest
presample observation.

Although `forecast`

requires presample response
data, `forecast`

sets default presample conditional
variance data in one of the following ways:

If

`numpreobs`

≥`max([Mdl.P Mdl.Q]) + Mdl.P`

,`forecast`

infers presample conditional variances from the presample response data (see`infer`

).Otherwise:

If

`Mdl`

is a GARCH(*P*,*Q*) or GJR(*P*,*Q*) model,`forecast`

sets all required conditional variances to the unconditional variance of the conditional variance process.If

`Mdl`

is a EGARCH(*P*,*Q*) model,`forecast`

sets all required conditional variances to the exponentiated, unconditional mean of the logarithm of the EGARCH(*P*,*Q*) variance process.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **`forecast(Mdl,10,Y0,V0=[1 0.5;1 0.5])`

specifies two
different presample paths of conditional variances.

`V0`

— Presample conditional variances *σ*_{t}^{2}

positive column vector | positive matrix

_{t}

Presample conditional variances
*σ _{t}*

^{2}used to initialize the conditional variance model, specified as a

`numpreobs`

-by-1 positive column vector or
`numpreobs`

-by-`numpaths`

positive
matrix. Use `V0`

only when you supply the numeric array
of presample response data `Y0`

.Rows of `V0`

correspond to periods in the presample,
and the last row contains the latest presample conditional variance.

For GARCH(

*P*,*Q*) and GJR(*P*,*Q*) models,`numpreobs`

must be at least`Mdl.P`

to initialize the variance equation.For EGARCH(

*P*,*Q*) models,`numpreobs`

must be at least`max([Mdl.P Mdl.Q])`

to initialize the variance equation.

If `numpreobs`

exceeds the minimum number,
`forecast`

uses only the latest
observations.

Columns of `V0`

correspond to separate, independent paths.

If

`V0`

is a column vector,`forecast`

applies it to each forecasted path. In this case, the conditional variance model of all forecast paths`V`

derives from the same initial conditional variances.If

`V0`

is a matrix, it must have`numpaths`

columns, the same number of columns as`Y0`

.

`forecast`

sets default presample conditional
variance data in one of the following ways:

If the number of rows of

`Y0`

`numpreobs`

≥`max([Mdl.P Mdl.Q]) + Mdl.P`

,`forecast`

infers`V0`

from`Y0`

(see`infer`

).Otherwise:

If

`Mdl`

is a GARCH(*P*,*Q*) or GJR(*P*,*Q*) model,`forecast`

sets all required conditional variances to the unconditional variance of the conditional variance process.If

`Mdl`

is a EGARCH(*P*,*Q*) model,`forecast`

sets all required conditional variances to the exponentiated, unconditional mean of the logarithm of the EGARCH(*P*,*Q*) variance process.

**Data Types: **`double`

`PresampleResponseVariable`

— Variable of `Tbl1`

containing presample response paths *y*_{t}

string scalar | character vector | integer | logical vector

_{t}

*Since R2023a*

Variable of `Tbl1`

containing presample response
paths *y _{t}*, specified as one of
the following data types:

String scalar or character vector containing a variable name in

`Tbl1.Properties.VariableNames`

Variable index (integer) to select from

`Tbl1.Properties.VariableNames`

A length

`numprevars`

logical vector, where`PresampleResponseVariable(`

selects variable) = true`j`

from`j`

`Tbl1.Properties.VariableNames`

, and`sum(PresampleResponseVariable)`

is`1`

The selected variable must be a numeric matrix and cannot contain
missing values (`NaN`

).

If `Tbl1`

has one variable, the default specifies
that variable. Otherwise, the default matches the variable to name in
`Mdl.SeriesName`

.

**Example: **`PresampleResponseVariable="StockRate"`

**Example: **```
PresampleResponseVariable=[false false true
false]
```

or `PresampleResponseVariable=3`

selects the third table variable as the presample response
variable.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`PresampleVarianceVariable`

— Variable of `Tbl1`

containing presample conditional variance paths
*σ*_{t}^{2}

string scalar | character vector | integer | logical vector

_{t}

*Since R2023a*

Variable of `Tbl1`

containing presample conditional
variance paths
*σ _{t}*

^{2}, specified as one of the following data types:

String scalar or character vector containing a variable name in

`Tbl1.Properties.VariableNames`

Variable index (integer) to select from

`Tbl1.Properties.VariableNames`

A length

`numprevars`

logical vector, where`PresampleVarianceVariable(`

selects variable) = true`j`

from`j`

`Tbl1.Properties.VariableNames`

, and`sum(PresampleVarianceVariable)`

is`1`

The selected variable must be a numeric vector and cannot contain
missing values (`NaN`

).

To use presample conditional variance data in
`Tbl1`

, you must specify
`PresampleVarianceVariable`

.

**Example: **`PresampleVarianceVariable="StockRateVar"`

**Example: **```
PresampleVarianceVariable=[false false true
false]
```

or `PresampleVarianceVariable=3`

selects the third table variable as the presample conditional variance
variable.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

**Note**

`NaN`

values in numeric presample data sets`Y0`

and`V0`

indicate missing data.`forecast`

removes missing data from the presample data sets following this procedure:`forecast`

horizontally concatenates`Y0`

and`V0`

such that the latest observations occur simultaneously. The result can be a jagged array because the presample data sets can have a different number of rows. In this case,`forecast`

prepads variables with an appropriate amount of zeros to form a matrix.`forecast`

applies list-wise deletion to the combined presample matrix by removing all rows containing at least one`NaN`

.`forecast`

extracts the processed presample data sets from the result of step 2, and removes all prepadded zeros.

List-wise deletion reduces the sample size and can create irregular time series.

For numeric data inputs,

`forecast`

assumes that you synchronize the presample data such that the latest observations occur simultaneously.`forecast`

issues an error when any table or timetable input contains missing values.

## Output Arguments

`V`

— Paths of MMSE forecasts of conditional variances *σ*_{t}^{2}
of future model innovations
*ε*_{t}

numeric column vector | numeric matrix

_{t}

_{t}

Paths of MMSE forecasts of conditional variances
*σ _{t}*

^{2}of future model innovations

*ε*, returned as a

_{t}`numperiods`

-by-1 numeric column vector or
a `numperiods`

-by-`numpaths`

numeric
matrix. `forecast`

returns `V`

only
when you supply the input `Y0`

.`V`

represents a continuation of `V0`

(`V(1,:)`

occurs in the next time point after
`V0(end,:)`

).

`V(`

contains the * j*,

*)*

`k`

*-period-ahead forecasted conditional variance of path*

`j`

*.*

`k`

`forecast`

determines `numpaths`

from the number of columns in the presample data sets
`Y0`

and `V0`

. For details, see
Algorithms. If each
presample data set has one column, then `V`

is a column
vector.

`Tbl2`

— Paths of MMSE forecasts of conditional variances *σ*_{t}^{2}
of future model innovations
*ε*_{t}

table | timetable

_{t}

_{t}

*Since R2023a*

Paths of MMSE forecasts of conditional variances
*σ _{t}*

^{2}of future model innovations

*ε*, returned as a table or timetable, the same data type as

_{t}`Tbl1`

. `forecast`

returns
`Tbl2`

only when you supply the input
`Tbl1`

.`Tbl2`

contains a variable for all forecasted conditional
variance paths, which are in a
`numperiods`

-by-`numpaths`

numeric
matrix, with rows representing periods in the forecast horizon and columns
representing independent paths, each corresponding to the input presample
response and conditional variance paths in `Tbl1`

.
`forecast`

names the forecasted conditional
variance variable in `Tbl2`

, where
* responseName*_Variance

`responseName`

is
`Mdl.SeriesName`

. For example, if
`Mdl.SeriesName`

is `StockReturns`

,
`Tbl2`

contains a variable for the corresponding
forecasted conditional variance paths with the name
`StockReturns_Variance`

.`Tbl2.`

represents a continuation of the presample conditional variance process,
either supplied by * responseName*_Variance

`Tbl1`

or set by default
(`Tbl2.``responseName`

_Variance(1,:)

occurs in the next time point, with respect to the periodicity
`Tbl1`

, after the last presample conditional
variance).`Tbl2.`

contains the * responseName*_Variance(

*,*

`j`

*)*

`k`

*-period-ahead forecasted conditional variance of path*

`j`

*.*

`k`

If `Tbl1`

is a timetable, the following conditions hold:

The row order of

`Tbl2`

, either ascending or descending, matches the row order of`Tbl1`

.`Tbl2.Time(1)`

is the next time after`Tbl1.Time(end)`

relative the sampling frequency, and`Tbl2.Time(2:numobs)`

are the following times relative to the sampling frequency.

## More About

### Time Base Partitions for Forecasting

*Time base partitions for forecasting* are
two disjoint, contiguous intervals of the time base; each interval contains time
series data for forecasting a dynamic model. The *forecast
period* (forecast horizon) is a `numperiods`

length partition at the end of the time base during which
`forecast`

generates forecasts `V`

from
the dynamic model `Mdl`

. The *presample
period* is the entire partition occurring before the forecast period.
`forecast`

can require observed responses (or innovations)
`Y0`

or conditional variances `V0`

in the
presample period to initialize the dynamic model for forecasting. The model
structure determines the types and amounts of required presample
observations.

A common practice is to fit a dynamic model to a portion of the data set, then
validate the predictability of the model by comparing its forecasts to observed
responses. During forecasting, the presample period contains the data to which the
model is fit, and the forecast period contains the holdout sample for validation.
Suppose that *y _{t}* is an observed response
series. Consider forecasting conditional variances from a dynamic model of

*y*

_{t}

`numperiods`

= *K*periods. Suppose that the dynamic model is fit to the data in the interval [1,

*T*–

*K*] (for more details, see

`estimate`

). This figure shows the time base partitions for
forecasting.For example, to generate forecasts `Y`

from a GARCH(0,2) model,
`forecast`

requires presample responses (innovations)
`Y0`

= $${\left[\begin{array}{cc}{y}_{T-K-1}& {y}_{T-K}\end{array}\right]}^{\prime}$$ to initialize the model. The 1-period-ahead forecast requires both
observations, whereas the 2-periods-ahead forecast requires
*y*_{T –
K} and the 1-period-ahead forecast
`V(1)`

. `forecast`

generates all other
forecasts by substituting previous forecasts for lagged responses in the
model.

Dynamic models containing a GARCH component can require presample conditional
variances. Given enough presample responses, `forecast`

infers
the required presample conditional variances. This figure shows the arrays of
required observations for this case, with corresponding input and output
arguments.

## Algorithms

If the conditional variance model

`Mdl`

has an offset (`Mdl.Offset`

),`forecast`

subtracts it from the specified presample responses to obtain presample innovations. Subsequently,`forecast`

uses to initialize the conditional variance model for forecasting.`forecast`

sets the number of sample paths to forecast`numpaths`

to the maximum number of columns among the specified presample response and conditional variance data sets. All presample data sets must have either`numpaths`

> 1 columns or one column. Otherwise,`forecast`

issues an error. For example, if`Y0`

has five columns, representing five paths, then`V0`

can either have five columns or one column. If`V0`

has one column, then`forecast`

applies`V0`

to each path.

## References

[1] Bollerslev, T. “Generalized Autoregressive Conditional
Heteroskedasticity.” *Journal of Econometrics*. Vol. 31,
1986, pp. 307–327.

[2] Bollerslev, T. “A Conditionally Heteroskedastic Time Series Model for
Speculative Prices and Rates of Return.” *The Review of Economics and
Statistics*. Vol. 69, 1987, pp. 542–547.

[3] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. *Time Series
Analysis: Forecasting and Control*. 3rd ed. Englewood Cliffs, NJ:
Prentice Hall, 1994.

[4] Enders, W. *Applied Econometric Time Series*. Hoboken, NJ:
John Wiley & Sons, 1995.

[5] Engle, R. F. “Autoregressive Conditional Heteroskedasticity with
Estimates of the Variance of United Kingdom Inflation.”
*Econometrica*. Vol. 50, 1982, pp. 987–1007.

[6] Glosten, L. R., R. Jagannathan, and D. E. Runkle. “On
the Relation between the Expected Value and the Volatility of the Nominal Excess Return
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[7] Hamilton, J. D. *Time Series Analysis*. Princeton, NJ:
Princeton University Press, 1994.

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Approach.” *Econometrica*. Vol. 59, 1991, pp.
347–370.

## Version History

**Introduced in R2012a**

### R2023a: `forecast`

accepts input data in tables and timetables, and returns results in tables and timetables

In addition to accepting input presample data in numeric arrays,
`forecast`

accepts input data in tables or regular
timetables. When you supply data in a table or timetable, the following conditions
apply:

`forecast`

chooses the default series on which to operate, but you can use the specified optional name-value argument to select a different variable.`forecast`

returns results in a table or timetable.

Name-value arguments to support tabular workflows include:

`PresampleResponseVariable`

specifies the variable name of the response paths in the input presample data`Tbl1`

to initialize the response series for the forecast.`PresampleVarianceVariable`

specifies the variable name of the conditional variance paths in the input presample data`Tbl1`

to initialize the conditional variance series for the forecast.

### R2019a: Models require specification of presample response data to forecast conditional variances

`forecast`

now has a third input argument for you to supply
presample response data.

forecast(Mdl,numperiods,Y0) forecast(Mdl,numperiods,Y0,Name,Value)

Before R2019a, the syntaxes were:

forecast(Mdl,numperiods) forecast(Mdl,numperiods,Name,Value)

`Y0`

name-value argument.There are no plans to remove the previous syntaxes or the `Y0`

name-value argument at this time. However, you are encouraged to supply presample
responses because, to forecast conditional variances from a conditional variance
model, `forecast`

must initialize models containing lagged
variables. Without specified presample responses, `forecast`

initializes models by using reasonable default values, but the default might not
support all workflows. This table describes the default values for each conditional
variance model object.

Model Object | Presample Default |
---|---|

`garch` | All presample responses are the unconditional standard deviation of the process. |

`egarch` | All presample responses are `0` . |

`gjr` | All presample responses are the unconditional standard deviation of the process. |

**Update Code**

Update your code by specifying presample response data in the third input argument.

If you do not supply presample responses, `forecast`

provides default presample values that might not support all workflows.

## See Also

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### Functions

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