(deprecated) Calculates the estimated error/standard deviation of the conditional mean forecast.
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)).
|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).
|1||Gaussian or Normal Distribution (default)|
|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.
- 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 gamma-coefficients must match the number of alpha-coefficients.
- 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.
|=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)|