NDK_GARCH_LRVAR

Calculates the long-run average volatility for the given GARCH model.

Return Value

a value from NDK_RETCODE enumeration for the status of the call. 

NDK_SUCCESS	operation successful
Error	Error Code

Parameters

[in]	mu	is the GARCH model conditional mean (i.e. mu).
[in]	Alphas	are the parameters of the ARCH(p) component model (starting with the lowest lag).
[in]	p	is the number of elements in Alphas array
[in]	Betas	are the parameters of the GARCH(q) component model (starting with the lowest lag).
[in]	q	is the number of elements in Betas array
[in]	nInnovationType	is the probability distribution function of the innovations/residuals (see INNOVATION_TYPE) INNOVATION_GAUSSIAN Gaussian Distribution (default) INNOVATION_TDIST Student's T-Distribution, INNOVATION_GED Generalized Error Distribution (GED)
[in]	nu	is the shape factor (or degrees of freedom) of the innovations/residuals probability distribution function.
[out]	retVal	is the calculated long run value

Remarks

1. The underlying model is described here.
2. The GARCH long-run average variance is defined as: \[V_L=\frac{\alpha_o}{1-\sum_{i=1}^{max(p,q)}\left(\alpha_i+\beta_i\right)}\]
3. The time series is homogeneous or equally spaced.
4. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
5. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
6. GARCH_CHECK examines the model's coefficients for:
   • Coefficients are all positive

Exceptions

Exception Type	Condition
None	N/A

Requirements

Namespace	NumXLAPI
Class	SFSDK
Scope	Public
Lifetime	Static
Package	NumXLAPI.DLL

Examples