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
  1. Mean forecast
  2. Forecast Error
  3. Volatility term structure
  4. Confidence interval lower limit
  5. 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
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
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
  1. Mean forecast
  2. Forecast Error
  3. Volatility term structure
  4. Confidence interval lower limit
  5. 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
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