(deprecated) Calculates the estimated error/standard deviation of the conditional mean forecast.
Syntax
EGARCH_FORESD(X, Sigmas, Order, mean, alphas, gammas, betas, innovation, v, 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 EGARCH 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=tDistribution, 3=GED).
value Description 1 Gaussian or Normal Distribution (default) 2 Student's tDistribution 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 the end of the time series X). If missing, t=1 is assumed.
 Local
 is the type of desired volatility output (0=Term Structure, 1=Local Volatility). If missing, local volatility is assumed.
Remarks
 The underlying model is described here.

Warning: EGARCH_FORESD() function is deprecated as of version 1.63: use EGARCH_FORE function instead.
 The time series is homogeneous or equally spaced.
 The time series may include missing values (e.g. #N/A) at either end.
 The number of gammacoefficients must match the number of alphacoefficients.
 The number of parameters in the input argument  alpha  determines the order of the ARCH component model.
 The number of parameters in the input argument  beta  determines the order of the GARCH component model.
Examples
Example 1:


Formula  Description (Result) 

=EGARCH_FORE($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7,1)  Forecasted conditional mean at T+1 (0.266) 
=EGARCH_FORESD($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7,1)  Forecasted conditional volatility at T+1 (1.918) 
Files Examples
References
 Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0691042896
 Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0471690740
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