EGARCH_GUESS - Initial Values for Model's Parameters

Returns an initial/quick guess of a given model's parameters.

Syntax

EGARCH_GUESS(X, Order, p, q, Innovation)

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)).

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).

p

is the ARCH model component order.

q

is the GARCH model component order.

Innovation

is the probability distribution function of the innovations/residuals (1 = Gaussian (default), 2 = t-Distribution, 3 = GED).

Value	Innovation
1	Gaussian or Normal Distribution (default).
2	Student's t-Distribution.
3	Generalized Error Distribution (GED).

Remarks

1. The underlying model is described here.
2. The time series is homogeneous or equally spaced.
3. The time series may include missing values (e.g., #N/A) at either end.
4. The number of gamma coefficients must match the number of alpha coefficients.
5. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
6. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
7. EGARCH_GUESS returns the model's parameters in the following order:
   
   1. $\mu$.
   2. $\alpha_o,\phi_1,...,\phi_p$.
   3. $\gamma_1,\gamma_2,...,\gamma_p$.
   4. $\beta_1,\beta_2,...,\beta_q$.
   5. $\nu$.

Files Examples

Related Links

• Wikipedia - Autoregressive conditional heteroskedasticity.

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.