# EGARCH_FORE - Forecasting for EGARCH Model

Calculates the out-of-sample forecast statistics: mean, volatility and confidence interval.

## Syntax

EGARCH_FORE(X, Sigmas, Order, mean, alphas, gammas, betas, innovation, Nu, T, Type, alpha)
X
is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).
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 E-GARCH model mean (i.e. mu). If missing, a default of 0 is assumed.
alphas
are the parameters of the ARCH(p) component model (starting with the lowest lag).
gammas
are the leverage parameters (starting with the lowest lag).
betas
are the parameters of the GARCH(q) component model (starting with the lowest lag).
innovation
is the probability distribution function of the innovations/residuals (1=Gaussian (default), 2=t-Distribution, 3=GED).
value Description
1 Gaussian or Normal Distribution (default)
2 Student's t-Distribution
3 Generalized Error Distribution (GED)
Nu
is the shape parameter (or degrees of freedom) of the innovations/residuals probability distribution function.
T
is the forecast time/horizon (expressed in terms of steps beyond end of the time series).
Type
is an integer switch to select the forecast output type: (1=mean (default), 2=Std. Error, 3=Term Struct, 4=LL, 5=UL)
Value Description
1 Mean forecast value (default)
2 Forecast standard error (aka local volatility)
3 Volatility term structure
4 Lower limit of the forecast confidence interval.
5 Upper limit of the forecast confidence interval.
alpha
is the statistical significance level. If missing, a default of 5% is assumed.

## Remarks

1. The underlying model is described here.
2. The time series is homogeneous or equally spaced.
3. The time series may include missing values (e.g. #N/A) at either end.
4. The number of gamma-coefficients must match the number of alpha-coefficients.
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. By definition, the EGARCH_FORE function returns a constant value equal to the model mean (i.e. $\mu$) for all horizons.

## Examples

Example 1:

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A B C D
Date Data
January 10, 2008 -2.827 EGARCH(1,1)
January 11, 2008 -0.947 Mean -0.266
January 12, 2008 -0.877 Alpha_0 1.583
January 14, 2008 1.209 Alpha_1 -1.755
January 13, 2008 -1.669 Gamma_1 0.286
January 15, 2008 0.835 Beta_1 0.470
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)
=EGARCH_FORE($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7,1) Forecasted conditional mean at T+1 (-0.266)
=EGARCH_FORE($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7,2) Forecasted conditional mean at T+2 (-0.266)