NDK_GARCH_SIM

int __stdcall NDK_GARCH_SIM ( double  mu,
const double *  Alphas,
size_t  p,
const double *  Betas,
size_t  q,
WORD  nInnovationType,
double  nu,
double *  pData,
size_t  nSize,
double *  sigmas,
size_t  nSigmaSize,
UINT  nSeed,
double *  retArray,
size_t  nSteps 
)

Returns a simulated data series the underlying GARCH process.

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[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)
[in] nu is the shape factor (or degrees of freedom) of the innovations/residuals probability distribution function.
[in] pData is the univariate time series of the latest observations (a one dimensional array).
[in] nSize is the number of observations in pData.
[in] sigmas is the univariate time series of the latest observations (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] nSeed is an unsigned integer for setting up the random number generators
[out] retArray is the calculated simulation value
[in] nSteps is the number of future steps to simulate for.
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.
  4. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
  5. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
  6. By definition, the GARCH_FORE function returns a constant value equal to the model mean (i.e. \(\mu\)) for all horizons.
  7. The function GARCH_SIM was added in version 1.63 SHAMROCK.
Requirements
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Namespace:  NumXLAPI
Class:  SFSDK
Scope:  Public
Lifetime:  Static
int NDK_GARCH_SIM ( double  mu,
double[]  Alphas,
UIntPtr  p,
double[]  Betas,
UIntPtr  q,
short  nInnovationType,
double  nu,
double[]  pData,
UIntPtr  nSize,
UIntPtr  nSeed,
ref double  retVal,
UIntPtr  nSteps 
)

Returns a simulated data series the underlying GARCH process.

Return Value

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

NDK_SUCCESS  operation successful
Error  Error Code
Parameters
[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)
[in] nu is the shape factor (or degrees of freedom) of the innovations/residuals probability distribution function.
[in] pData is the univariate time series of the latest observations (a one dimensional array).
[in] nSize is the number of observations in pData.
[in] nSeed is an unsigned integer for setting up the random number generators
[out] retArray is the calculated simulation value
[in] nSteps is the number of future steps to simulate for.
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.
  4. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
  5. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
  6. By definition, the GARCH_FORE function returns a constant value equal to the model mean (i.e. \(\mu\)) for all horizons.
  7. The function GARCH_SIM was added in version 1.63 SHAMROCK.
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