Calculates the log-likelihood function for the fitted GARCH-M 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 GARCH-M 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=t-Distribution, 3=GED).
value Description 1 Gaussian or Normal Distribution (default) 2 Student's t-Distribution 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 Log-Likelihood 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:
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| Formula | Description (Result) |
|---|---|
| =GARCHM_LLF($B$2:$B$32,1,$D$3,$D$4,$D$5:$D$6,$D$7) | Log-Likelihood Function for Guassian Distribution (-45.482) |
| =GARCHM_LLF($B$2:$B32,1,$D$3,$D$4,$D$5:$D$6,$D$7,2,5) | Log-Likelihood Function for t-Distribution when v = 5 (-49.122) |
| =GARCHM_LLF($B$2:$B32,1,$D$3,$D$4,$D$5:$D$6,$D$7,3,2) | Log-Likelihood Function for GED when v = 2 (-45.482) |
| =GARCHM_AIC($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 GARCH-M(1,1) model is stable? (1) |
Files Examples
Related Links
References
- Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
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