Calculates the loglikelihood function for the fitted GARCHM model.
Syntax
GARCHM_LLF(X, Order, mean, lambda, alphas, betas, innovation, v)
 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 model for the innovations/residuals (1=Gaussian (default), 2=tDistribution, 3=GED).
value Description 1 Gaussian or Normal Distribution (default) 2 Student's tDistribution 3 Generalized Error Distribution (GED)  v
 is the shape parameter (or degrees of freedom) of the innovations/residuals probability distribution function.
Remarks
 The underlying model is described here.
 The LogLikelihood Function (LLF) 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.
 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:


Formula  Description (Result) 

=GARCHM_LLF($B$2:$B$32,1,$D$3,$D$4,$D$5:$D$6,$D$7)  LogLikelihood Function for Guassian Distribution (45.482) 
=GARCHM_LLF($B$2:$B32,1,$D$3,$D$4,$D$5:$D$6,$D$7,2,5)  LogLikelihood Function for tDistribution when v = 5 (49.122) 
=GARCHM_LLF($B$2:$B32,1,$D$3,$D$4,$D$5:$D$6,$D$7,3,2)  LogLikelihood Function for GED when v = 2 (45.482) 
=GARCHM_AIC($B$2:$B$32,1,$D$3,$D$4,$D$5:$D$6,$D$7)  Akaike's information criterion (96.964) 
=GARCHM_CHECK($D$3,$D$4,$D$5:$D$6,$D$7)  The GARCHM(1,1) model is stable? (1) 
Files Examples
Related Links
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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