Examines the model's parameters for stability constraints (e.g. stationary, positive variance, etc.).
Syntax
EGARCH_CHECK(mean, alphas, gammas, betas, innovation, v)
mean is the E-GARCH 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).
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 time series is homogeneous or equally spaced.
- The number of gamma-coefficients must match the number of alpha-coefficients.
- 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.
- EGARCH_CHECK examines the model's coefficients for:
- Coefficients are all positive
Examples
Example 1:
|
|
| Formula | Description (Result) | |
|---|---|---|
| =EGARCH_CHECK($B$3,$B$4:$B$5,$B$6,$B$7) | The model is stable? (1) |
Files Examples
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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