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GARCHM_RESID
Returns an array for the fitted GARCHM^{i} 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 of the data series (i.e. whether the first data point corresponds to the earliest or latest date (earliest date=1 (default), latest date=0)).
Order  Description 

1  ascending (the first data point corresponds to the earliest date) 
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^{i}).
betas
are the parameters of the GARCH(q) component model (starting with the lowest lag).
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:
Where:
 is the GARCHM model's standardized residual at time t.
 is the GARCHM model's residual at time t.
 is the value of the time series at time t.
 is the GARCHM mean.
 is the GARCHM conditional volatility at time t.
 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:
A  B  C  D  E  

1  Date  Data 
GARCHM_RESID"=GARCHM_RESID($B$2:$B$32,1,$E$3,$E$4,$E$5:$E$6,$E$7)" 

2  January 10, 2008  2.827  2.760 
GARCHM(1,1) 

3  January 11, 2008  0.947  1.019  Mean  0.076 
4  January 12, 2008  0.877  0.949  Lambda  0.145 
5  January 13, 2008  1.209  1.144  Alpha_0  0.593 
6  January 14, 2008  1.669  1.743  Alpha_1  0.000 
7  January 15, 2008  0.835  0.769  Beta_1  0.403 
8  January 16, 2008  0.266  0.336  
9  January 17, 2008  1.361  1.297  
10  January 18, 2008  0.343  0.413  
11  January 19, 2008  0.475  0.408  
12  January 20, 2008  1.153  1.226  
13  January 21, 2008  1.144  1.079  
14  January 22, 2008  1.070  1.142  
15  January 23, 2008  1.491  1.565  
16  January 24, 2008  0.686  0.620  
17  January 25, 2008  0.975  0.910  
18  January 26, 2008  1.316  1.389  
19  January 27, 2008  0.125  0.057  
20  January 28, 2008  0.712  0.646  
21  January 29, 2008  1.530  1.604  
22  January 30, 2008  0.918  0.852  
23  January 31, 2008  0.365  0.297  
24  February 1, 2008  0.997  1.069  
25  February 2, 2008  0.360  0.430  
26  February 3, 2008  1.347  1.283  
27  February 4, 2008  1.339  1.412  
28  February 5, 2008  0.481  0.414  
29  February 6, 2008  1.270  1.343  
30  February 7, 2008  1.710  1.647  
31  February 8, 2008  0.125  0.194  
32  February 9, 2008  0.940  1.012 
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