(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)).
Value Order 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 Innovation 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.
EGARCH_FORESD() function is deprecated as of version 1.63: use the EGARCH_FORE function instead.
- The underlying model is described here.
- 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.
- Hamilton, J.D.; Time Series Analysis, Princeton University Press (1994), ISBN 0-691-04289-6.
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740.
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