| 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
-
- The underlying model is described here.
- The time series is homogeneous or equally spaced.
- 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
-
- The underlying model is described here.
- The time series is homogeneous or equally spaced.
- 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
