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

## Syntax

**GARCH_CALIBRATE** (**[x]**, order, µ, **[α]**, [β], f, ν, mask, method, maxiter)

**[X]**- Required. Is the univariate time series data (a one-dimensional array of cells (e.g., rows or columns)).
**Order**- Optional. Is the time order in the data series (i.e., the first data point's corresponding date (earliest date = 1 (default), latest date = 0)).
Value Order 1 Ascending (the first data point corresponds to the earliest date) ( **default**).0 Descending (the first data point corresponds to the latest date). **µ**- Optional. Is the GARCH model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.
**[α]**- Required. Are the parameters of the ARCH(p) component model: [αo α1, α2 … αp] (starting with the lowest lag).
**[β]**- Optional. Are the parameters of the GARCH(q) component model: [β1, β2 … βq] (starting with the lowest lag).
**F**- Optional. Is the probability distribution function of the innovations/residuals (1 = Gaussian (default), 2 = t-Distribution, 3 = GED).
Value Probability Distribution 1 Gaussian or Normal Distribution ( **default**).2 Student's t-Distribution. 3 Generalized Error Distribution (GED). **ν**- Optional. Is the shape parameter (or degrees of freedom) of the innovations/residuals’ probability distribution function.
**Mask**- Optional. 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**- Optional. Is the calibration/fitting method (1 = MLE, 2 = Bayesian). If missing, a Maximum Likelihood Estimate (MLE) is assumed.
Value Method 1 Maximum Likelihood Estimate (MLE) ( **default**).2 Bayesian. **MaxIter**- Optional. Is the maximum number of iterations used to calibrate the model. If missing, 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.
- The maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.
- For the input argument - ([α]) (parameters of the ARCH component):
- The input argument is not optional.
- The value in the first element must be positive.
- The order of the parameters starts with the lowest lag.
- One or more parameters may have missing values or error codes (i.e., #NUM!, #VALUE!, etc.).
- In the case where alpha has one non-missing entry/element (first), no ARCH component is included.
- The order of the ARCH component model is solely determined by the order (minus one) of the last value in the array with a numeric value (vs. missing or error).

- For the input argument - ([β]) (parameters of the GARCH component):
- The input argument is optional and can be omitted, in which case no GARCH component is included.
- The order of the parameters starts with the lowest lag.
- One or more parameters may have missing values or error codes (i.e., #NUM!, #VALUE!, etc.).
- The order of the GARCH component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).

- The shape parameter (ν) is only used for non-Gaussian distributions and is otherwise ignored.
- For the student's t-distribution, the shape parameter’s value must be greater than four.
- For GED distribution, the shape parameter’s value must be greater than one.
- GARCH_CALIBRATE returns the values for the model's parameters in the following order:
- $\mu $.
- ${\alpha _o},{\phi _1},...,{\phi _p}$.
- ${\beta _1},{\beta _2},...,{\theta _q}$.
- $\nu $.

## Files Examples

## Related Links

## References

- James Douglas Hamilton; Time Series Analysis, Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896.
- Tsay, Ruey S.; Analysis of Financial Time Series, John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0-471-690740.

## Comments

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