Calculates the out-of-sample conditional mean forecast.

## Syntax

**GARCHM_FORE** (**[x]**, **[σ]**, order, µ, λ, **[α]**, [β], f, ν, t, return, α)

**[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.
**λ**- Optional. Is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium. If missing, a default of 0 is assumed.
**[α]**- Required. Are the parameters of the ARCH(p) component model: [αo α1, α2 … αp] (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). If missing, a default of 1 is assumed.
**Return**- Optional. Is an integer switch to select the forecast output type: (1 = Mean (default), 2 = Std. Error, 3 = Term Struct, 4 = LL, 5 = UL).
Value Return 1 Mean forecast value ( **default**).2 Forecast standard error (aka local volatility). 3 Volatility term structure. 4 The lower limit of the forecast confidence interval. 5 The upper limit of the forecast confidence interval. **α**- Optional. Is the statistical significance level (i.e., alpha). If missing or omitted, an alpha value of 5% is assumed.

## 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 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.

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