# EGARCH_FORECI - Forecasting confidence interval of EGARCH Model

(deprecated) Returns the confidence interval limits of the conditional mean forecast.

## Syntax

EGARCH_FORECI(X, Sigmas, Order, mean, alphas, gammas, betas, innovation, v, T, alpha-level, upper)
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).
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 model for 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)
v
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).
alpha-level
is the statistical significance level. If missing, a default of 5% is assumed.
upper
If true, returns the upper confidence interval limit. Otherwise, returns lower limit.
upper description
0 return lower limit
1 return upper limit

Warning

EGARCH_FORECI() function is deprecated as of version 1.63: use EGARCH_FORE function instead.

## 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. If the time series has intermediate missing values/points (i.e. #N/A), the function returns #N/A.
5. The significance level (i.e. $\alpha$) must be greater than zero and less than one. Otherwise, a #VALUE! is returned
6. The number of gamma-coefficients must match the number of alpha-coefficients.
7. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
8. The number of parameters in the input argument - beta - determines the order of the GARCH component model.