Calculates the log-likelihood function for the fitted GARCH-M model.
Syntax
GARCHM_LLF ([x], order, µ, λ, [α], [β], f, ν)
- [X]
- Required. Is the univariate time series data (a one-dimensional array of cells (e.g., rows or columns)).
- Order
- Optional. Is the time order in the data series (i.e., the first data point's corresponding date (earliest date = 1 (default), latest date = 0)).
Value Order 1 Ascending (the first data point corresponds to the earliest date) (default). 0 Descending (the first data point corresponds to the latest date). - µ
- Optional. Is the GARCH model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.
- λ
- Optional. Is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium. If missing, a default of 0 is assumed.
- [α]
- Required. Are the parameters of the ARCH(p) component model: [αo α1, α2 … αp] (starting with the lowest lag).
- [β]
- Optional. Are the parameters of the GARCH(q) component model: [β1, β2 … βq] (starting with the lowest lag).
- F
- Optional. Is the probability distribution function of the innovations/residuals (1 = Gaussian (default), 2 = t-Distribution, 3 = GED).
Value Probability Distribution 1 Gaussian or Normal Distribution (default). 2 Student's t-Distribution. 3 Generalized Error Distribution (GED). - ν
- Optional. 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 - [αo α1, α2 … αp] - determines the order of the ARCH component model.
- The number of parameters in the input argument - [β1, β2 … βq] - determines the order of the GARCH component model.
Files Examples
Related Links
References
- James Douglas Hamilton; Time Series Analysis, Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896.
- Tsay, Ruey S.; Analysis of Financial Time Series, John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0-471-690740.
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