NDK_EGARCH_FORE

Name: NumXL SDK
Rating: 4.9 (12 reviews)

C/C++
.Net

int __stdcall NDK_EGARCH_FORE	(	double *	pData,
		size_t	nSize,
		double *	sigmas,
		size_t	nSigmaSize,
		double	mu,
		const double *	Alphas,
		size_t	p,
		const double *	Gammas,
		size_t	g,
		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,out]	Gammas	are the leverage parameters (starting with the lowest lag).
[in]	g	is the number of elements in Gammas. Must be equal to (p-1).
[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.
[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 simulated value for the GARCH process.

Remarks

The underlying model is described here.
By definition, the EGARCH_FORE function returns a constant value equal to the model mean (i.e. \mu) for all horizons.
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.
The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
The number of parameters in the input argument - beta - determines the order of the GARCH component model.

Requirements

Header	SFSDK.H
Library	SFSDK.LIB
DLL	SFSDK.DLL

Namespace:	NumXLAPI
Class:	SFSDK
Scope:	Public
Lifetime:	Static

int NDK_EGARCH_FORE	(	double[]	pData,
		UIntPtr	nSize,
		double	mu,
		double[]	Alphas,
		UIntPtr	p,
		double[]	Gammas,
		double[]	Betas,
		UIntPtr	q,
		short	nInnovationType,
		double	nu,
		UIntPtr	nStep,
		short	retType,
		ref double	retVal
	)

Calculates the out-of-sample forecast statistics.

Return Value

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

NDK_SUCCESS	operation successful
Error	Error Code

Parameters

[in]	pData	is the univariate time series data (a one dimensional array).
[in]	nSize	is the number of observations in pData.
[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,out]	Gammas	are the leverage parameters (starting with the lowest lag).
[in]	g	is the number of elements in Gammas. Must be equal to (p-1).
[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.
[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 simulated value for the GARCH process.

Remarks

The underlying model is described here.
By definition, the EGARCH_FORE function returns a constant value equal to the model mean (i.e. \mu) for all horizons.
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.
The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
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
Class	SFSDK
Scope	Public
Lifetime	Static
Package	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

See Also