# GARCH_FORESD - Volatility Forecast of GARCH Model

(deprecated) Calculates the estimated error/standard deviation of the conditional mean forecast.

## Syntax

GARCH_FORESD(X, Sigmas, Order, mean, alphas, betas, T, Local)
X
is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last p observations.
Sigmas
is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last q realized volatilities.
Order
is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description
1 ascending (the first data point corresponds to the earliest date) (default)
0 descending (the first data point corresponds to the latest date)
mean
is the GARCH model mean (i.e. mu).
alphas
are the parameters of the ARCH(p) component model (starting with the lowest lag).
betas
are the parameters of the GARCH(q) component model (starting with the lowest lag).
T
is the forecast time/horizon (expressed in terms of steps beyond the end of the time series X). If missing, t=1 is assumed.
Local
is the type of desired volatility output (Term Structure=0, Local/Step=1). If missing, local volatility is assumed.

## Remarks

1. The underlying model is described here.
2. Warning: GARCH_FORESD() function is deprecated as of version 1.63: use GARCH_FORE function instead.

3. The time series is homogeneous or equally spaced.
4. The time series may include missing values (e.g. #N/A) at either end.
5. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
6. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
7. For GARCH(1,1), the squared of the forecast standard error (i.e. conditional variance) is expressed as follow: $$E[\sigma_{T+k}^2]=\alpha_o\times \frac{1-(\alpha_1+\beta_1)^k}{1-(\alpha_1+\beta_1)}+(\alpha_1+\beta_1)^k\sigma_T^2$$
8. The forecast standard error (i.e. conditional volatility) converges monotonically to its long-run average. For the case of GARCH(1,1): $$E[\sigma_{T+k\rightarrow \infty}^2]=\frac{\alpha_o}{1-(\alpha_1+\beta_1)}$$

## Examples

Example 1:

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A B C D
Date Data
January 10, 2008 -2.827 GARCH(1,1)
January 11, 2008 -0.947 Mean -0.160
January 12, 2008 -0.877 Alpha_0 0.608
January 14, 2008 1.209 Alpha_1 0.00
January 13, 2008 -1.669 Beta_1 0.391
January 15, 2008 0.835
January 16, 2008 -0.266
January 17, 2008 1.361
January 18, 2008 -0.343
January 19, 2008 0.475
January 20, 2008 -1.153
January 21, 2008 1.144
January 22, 2008 -1.070
January 23, 2008 -1.491
January 24, 2008 0.686
January 25, 2008 0.975
January 26, 2008 -1.316
January 27, 2008 0.125
January 28, 2008 0.712
January 29, 2008 -1.530
January 30, 2008 0.918
January 31, 2008 0.365
February 1, 2008 -0.997
February 2, 2008 -0.360
February 3, 2008 1.347
February 4, 2008 -1.339
February 5, 2008 0.481
February 6, 2008 -1.270
February 7, 2008 1.710
February 8, 2008 -0.125
February 9, 2008 -0.940

Formula Description (Result)
=GARCH_FORESD($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,1) Forecasted conditional mean at T+1 (0.9992)
=GARCH_FORESD($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,2) Forecasted conditional mean at T+2 (0.9992)
=GARCH_FORESD($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,3) Forecasted conditional mean at T+3 (0.9992)