(**Deprecated**) Calculates the estimated error/standard deviation of the conditional mean forecast.

## Syntax

**EGARCH_FORESD** (**[x]**, [σ], order, µ, **[α]**, [γ], [β], f, ν, t, local)

**[X]**- Required. Is the univariate time series data (a one-dimensional array of cells (e.g., rows or columns)).
**[σ]**- Optional. Is the univariate time series data (a one-dimensional array of cells (e.g., rows or columns)) of the last q realized volatilities.
**Order**- Optional. 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). **µ**- Optional. Is the GARCH model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.
**[α]**- Required. Are the parameters of the ARCH(p) component model: [αo α1, α2 … αp] (starting with the lowest lag).
**[γ]**- Optional. Are the leverage parameters: [γ1, γ2 … γp] (starting with the lowest lag).
**[β]**- Optional. Are the parameters of the GARCH(q) component model: [β1, β2 … βq] (starting with the lowest lag).
**F**- Optional. Is the probability distribution function of the innovations/residuals (1 = Gaussian (default), 2 = t-Distribution, 3 = GED).
Value Probability Distribution 1 Gaussian or Normal Distribution ( **default**).2 Student's t-Distribution. 3 Generalized Error Distribution (GED). **ν**- Optional. Is the shape parameter (or degrees of freedom) of the innovations/residuals’ probability distribution function.
**T**- Optional. Is the forecast time/horizon (expressed in terms of steps beyond the end of the time series).
**Local**- Optional. Is the type of desired volatility output (0 = Term Structure, 1 = Local Volatility). If missing, local volatility is assumed.

* *Warning

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

## Remarks

- 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 (minus one).
- The number of parameters in the input argument - [α
_{o }α_{1,}α_{2 }… α_{p}] - determines the order of the ARCH component model. - The number of parameters in the input argument - [β
_{1,}β_{2 }… β_{q}] - determines the order of the GARCH component model.

## Files Examples

## Related Links

## References

- James Douglas Hamilton; Time Series Analysis, Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896.
- Tsay, Ruey S.; Analysis of Financial Time Series, John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0-471-690740.

## Comments

Article is closed for comments.