(deprecated) Calculates the estimated error/standard deviation of the conditional mean forecast.
EGARCH_FORESD(X, Sigmas, Order, mean, alphas, gammas, betas, innovation, v, T, Local)
- is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last p observations.
- is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last q realized volatilities.
- 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)
- is the E-GARCH model mean (i.e. mu).
- are the parameters of the ARCH(p) component model (starting with the lowest lag).
- are the leverage parameters (starting with the lowest lag).
- are the parameters of the GARCH(q) component model (starting with the lowest lag).
- 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)
- is the shape parameter (or degrees of freedom) of the innovations/residuals probability distribution function.
- is the forecast time/horizon (expressed in terms of steps beyond the end of the time series X). If missing, t=1 is assumed.
- 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)|