Calculates the Akaike's information criterion (AIC) of a given estimated EGARCH model (with corrections for small sample sizes).
Syntax
EGARCH_AIC(X, Order, mean, alphas, gammas, 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 EGARCH model mean (i.e. mu).
 alphas
 are the parameters of the ARCH(p) component model (starting with the lowest lag).
 gammas
 are the leverage parameters (starting with the lowest lag). The number of gammacoefficients must match the number of alphacoefficients.
 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.
 Akaike's Information Criterion (AIC) 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.
 Given a fixed data set, several competing models may be ranked according to their AIC, the model with the lowest AIC being the best.
 EGARCH(p,q) model has 2p+q+2 estimated 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) 

=EGARCH_AIC($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7,3,30)  Akaike's information criterion (AIC) with GED(df=30) shocks (106.375) 
=EGARCH_AIC($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7,2,5)  Akaike's information criterion (AIC) with tdist(df=5) innovation/shocks (95.390) 
=EGARCH_AIC($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7)  Akaike's information criterion (AIC) with default gaussian shocks/innovations (90.471) 
=EGARCH_LLF($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,$D$7)  LogLikelihood function FOR Normal Distribution (42.235) 
=EGARCH_CHECK($D$3,$D$4:$D$5,$D$6,$D$7)  The EGARCH(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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