Returns an array for the fitted GARCH-M model standardized residuals.

## Syntax

**GARCHM_RESID** (**[x]**, order, µ, λ, **[α]**, [β], f, ν)

**[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.
**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.
- The time series may include missing values (e.g., #N/A) at either end.
- The standardized residuals have a mean of zero and a variance of one (1).
- The GARCH-M model's standardized residuals are defined as:$$\epsilon_t = \frac{a_t}{\sigma_t}$$ $$a_t = x_t - \mu -\lambda \sigma_t$$
Where:

- $\epsilon$ is the GARCH-M model's standardized residual at time $t$.
- $a_t$ is the GARCH-M model's residual at time $t$.
- $x_t$ is the value of the time series at time $t$.
- $\mu$ is the GARCH-M mean.
- $\sigma_t$ is the GARCH-M conditional volatility at time $t$.
- $\lambda$ is the volatility coefficient in the conditional mean.

- 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.

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