NDK_GARCHM_GOF

int __stdcall NDK_GARCHM_GOF ( double *  pData,
size_t  nSize,
double  mu,
double  flambda,
const double *  Alphas,
size_t  p,
const double *  Betas,
size_t  q,
WORD  nInnovationType,
double  nu,
WORD  retType,
double *  retVal 
)

Computes the log-likelihood ((LLF), Akaike Information Criterion (AIC) or other goodness of fit function of the GARCH model.

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] mu is the GARCH model conditional mean (i.e. mu).
[in] flambda is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium.
[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 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] retType is a switch to select a fitness measure ( see GOODNESS_OF_FIT_FUNC)
[out] retVal is the calculated goodness of fit value.
Remarks
  1. The underlying model is described here.
  2. The Log-Likelihood Function (LLF) is described here.
  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 maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.
  6. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
  7. 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_GARCHM_GOF ( double[]  pData,
size_t  nSize,
double  mu,
double  flambda,
double[]  Alphas,
UIntPtr  p,
double[]  Betas,
UIntPtr  q,
short  nInnovationType,
double  nu,
short  retType,
double[]  retVal 
)

Computes the log-likelihood ((LLF), Akaike Information Criterion (AIC) or other goodness of fit function of the GARCH model.

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] flambda is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium.
[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 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] retType is a switch to select a fitness measure ( see GOODNESS_OF_FIT_FUNC)
[out] retVal is the calculated goodness of fit value.
Remarks
  1. The underlying model is described here.
  2. The Log-Likelihood Function (LLF) is described here.
  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 maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.
  6. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
  7. 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