Examines the model's parameters for stability constraints (e.g., stationary, positive variance, etc.).

## Syntax

**GARCHM_CHECK** (µ, λ, **[α]**, [β], f, ν)

**µ**- Optional. Is the GARCH model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.
**λ**- Optional. Is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium. If missing, a default of 0 is assumed.
**[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.

## Remarks

- The underlying model is described here.
- The time series is homogeneous or equally spaced.
- To ensure positive conditional variance and finite unconditional variance, the model's coefficient must meet the following:
- $\alpha_o \gt 0$.
- $\alpha_i \geq 0$.
- $\beta_i \geq 0$.
- $\sum_{i=1}^{max(p,q}(\alpha_i+\beta_i) \lt 1$.

- The number of parameters in the input argument - [α
_{o }α_{1,}α_{2 }… α_{p}] - determines the order of the ARCH component model. - The number of parameters in the input argument - [β
_{1,}β_{2 }… β_{q}] - determines the order of the GARCH component model.

## 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

Article is closed for comments.