EGARCH_FORESD - Volatility Forecast of EGARCH Model

(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

  1. The underlying model is described here.
  2. The time series is homogeneous or equally spaced.
  3. The time series may include missing values (e.g., #N/A) at either end.
  4. The number of gamma coefficients must match the number of alpha coefficients (minus one).
  5. The number of parameters in the input argument - [αo α1, α2 … αp] - determines the order of the ARCH component model.
  6. The number of parameters in the input argument - [β1, β2 … βq] - determines the order of the GARCH component model.

Files Examples

Related Links

References

Comments

Article is closed for comments.

Was this article helpful?
0 out of 0 found this helpful