GARCH_CALIBRATE - Optimal Values for Model's Parameters

Computes the maximum likelihood estimated (MLE) model's parameters.



GARCH_CALIBRATE(X, Order, Model, Mask, Method, maxIter)

X is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).

Order is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).

Order Description
1 ascending (the first data point corresponds to the earliest date) (default)
0 descending (the first data point corresponds to the latest date)

Model is the GARCH model representation array (a one dimensional array of cells (e.g. rows or columns)) (see GARCH function).

Mask is an array of 0's and 1's to specify which parameters to calibrate for. If missing, all parameters are included in the calibration.

Method is the calibration/fitting method (1=MLE, 2=Bayesian). If missing, a Maximum Likelihood Estimate (MLE) is assumed.

Method Description
1 Maximum Likelihood Estimate (MLE)
2 Bayesian

maxIter is the maximum number of iterations used to calibrate the model. If missing, the default maximum of 100 is assumed.



  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 maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.

Files Examples


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