NDK_GARCH_FORE

 int __stdcall NDK_GARCH_FORE ( double * pData, size_t nSize, double * sigmas, size_t nSigmaSize, double mu, const double * Alphas, size_t p, const double * Betas, size_t q, WORD nInnovationType, double nu, size_t nStep, WORD retType, double alpha, double * retVal )

Calculates the out-of-sample forecast statistics.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
 [in] pData is the univariate time series data (a one dimensional array). [in] nSize is the number of observations in pData. [in] sigmas is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last q realized volatilities. [in] nSigmaSize is the number of elements in sigmas. Only the latest q observations are used. [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 or Normal Distribution 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. [in] nStep is the forecast time/horizon (expressed in terms of steps beyond end of the time series). [in] retType is a switch to select the type of value returned Mean forecast Forecast Error Volatility term structure Confidence interval lower limit Confidence interval upper limit (see FORECAST_RETVAL_FUNC) [in] alpha is the statistical significance level. If missing, a default of 5% is assumed. [out] retVal is the calculated forecast value
Remarks
1. The underlying model is described here.
2. By definition, the GARCH_FORE function return a constant value equal to the model mean (i.e. $$\mu$$) for all horizons.
3. The time series is homogeneous or equally spaced.
4. The time series may include missing values (e.g. #N/A) at either end.
5. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
6. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
Requirements
 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int __stdcall NDK_GARCH_FORE ( double * pData, size_t nSize, double * sigmas, size_t nSigmaSize, double mu, const double * Alphas, size_t p, const double * Betas, size_t q, WORD nInnovationType, double nu, size_t nStep, WORD retType, double alpha, double * retVal )

Calculates the out-of-sample forecast statistics.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
Parameters
 [in] pData is the univariate time series data (a one dimensional array). [in] nSize is the number of observations in pData. [in] sigmas is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last q realized volatilities. [in] nSigmaSize is the number of elements in sigmas. Only the latest q observations are used. [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 or Normal Distribution 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. [in] nStep is the forecast time/horizon (expressed in terms of steps beyond end of the time series). [in] retType is a switch to select the type of value returned Mean forecast Forecast Error Volatility term structure Confidence interval lower limit Confidence interval upper limit (see FORECAST_RETVAL_FUNC) [in] alpha is the statistical significance level. If missing, a default of 5% is assumed. [out] retVal is the calculated forecast value
Remarks
1. The underlying model is described here.
2. By definition, the GARCH_FORE function return a constant value equal to the model mean (i.e. $$\mu$$) for all horizons.
3. The time series is homogeneous or equally spaced.
4. The time series may include missing values (e.g. #N/A) at either end.
5. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
6. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
Examples

References
* 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
* D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
* Box, Jenkins and Reisel; Time Series Analysis: Forecasting and Control; John Wiley & SONS.; 4th edition(Jun 30, 2008), ISBN: 470272848