Returns an array for the fitted GARCHM model standardized residuals.
Syntax
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) 
mean is the GARCHM model mean (i.e. mu).
lambda is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium.
alphas are the parameters of the ARCH(p) component model (starting with the lowest lag).
betas are the parameters of the GARCH(q) component model (starting with the lowest lag).
innovation is the probability distribution function of the innovations/residuals (1=Gaussian (default), 2=tDistribution, 3=GED).
value  Description 

1  (default) Gaussian or Normal Distribution 
2  Student's tDistribution 
3  Generalized Error Distribution (GED) 
v is the shape factor (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 GARCHM model's standardized residuals is defined as:
$$\epsilon_t = \frac{a_t}{\sigma_t} $$
$$a_t = x_t  \mu \lambda \sigma_t $$
Where:
 $\epsilon $ is the GARCHM model's standardized residual at time t.
 $a_t$ is the GARCHM model's residual at time t.
 $x_t$ is the value of the time series at time t.
 $\mu$ is the GARCHM mean.
 $\sigma_t$ is the GARCHM conditional volatility at time t.
 $\lambda$ is the volatility coefficient in the conditional mean.
 The number of parameters in the input argument  alpha  determines the order of the ARCH component model.
 The number of parameters in the input argument  beta  determines the order of the GARCH component model.
Examples
Example 1:


Files Examples
References
 Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0691042896
 Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0471690740
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