NDK_GARCH_PARAM

int __stdcall NDK_GARCH_PARAM ( double *  pData,
size_t  nSize,
double *  mu,
double *  Alphas,
size_t  p,
double *  Betas,
size_t  q,
WORD  nInnovationType,
double *  nu,
WORD  retType,
size_t  maxIter 
)

Returns an array of cells for the initial (non-optimal), optimal or standard errors of the model's parameters.

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,out] mu is the GARCH model conditional mean (i.e. mu).
[in,out] 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] 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,out] nu is the shape factor (or degrees of freedom) of the innovations/residuals probability distribution function.
[in] retType is a switch to select the type of value returned: 1= Quick Guess, 2=Calibrated, 3= Std. Errors ( see MODEL_RETVAL_FUNC)
[in] maxIter is the maximum number of iterations used to calibrate the model. If missing or less than 100, the default maximum of 100 is assumed.
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.
Requirements
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Namespace:  NumXLAPI
Class:  SFSDK
Scope:  Public
Lifetime:  Static
int NDK_GARCH_PARAM ( double[]  pData,
UIntPtr  nSize,
ref double  mu,
double[]  Alphas,
UIntPtr  p,
double[]  Betas,
UIntPtr  q,
short  nInnovationType,
ref double  nu,
short  retType,
UIntPtr  maxIter 
)

Returns an array of cells for the initial (non-optimal), optimal or standard errors of the model's parameters.

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,out] mu is the GARCH model conditional mean (i.e. mu).
[in,out] 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] 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,out] nu is the shape factor (or degrees of freedom) of the innovations/residuals probability distribution function.
[in] retType is a switch to select the type of value returned: 1= Quick Guess, 2=Calibrated, 3= Std. Errors ( see MODEL_RETVAL_FUNC)
[in] maxIter is the maximum number of iterations used to calibrate the model. If missing or less than 100, the default maximum of 100 is assumed.
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.
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