Here is the number of observations in the sequence, is the observed value of the GARCH process at time , is the conditional variance at time , and the information set of all information up to time . Symmetric GARCH sequences are generated when is zero, otherwise asymmetric GARCH sequences are generated with specifying the amount by which negative shocks are to be enhanced.
4 References
Bollerslev T (1986) Generalised autoregressive conditional heteroskedasticity Journal of Econometrics31 307–327
Engle R (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation Econometrica50 987–1008
Engle R and Ng V (1993) Measuring and testing the impact of news on volatility Journal of Finance48 1749–1777
Glosten L, Jagannathan R and Runkle D (1993) Relationship between the expected value and the volatility of nominal excess return on stocks Journal of Finance48 1779–1801
Hamilton J (1994) Time Series Analysis Princeton University Press
5 Arguments
1:
num – IntegerInput
On entry: , the number of terms in the sequence.
Constraints:
;
.
2:
p – IntegerInput
On entry: the GARCH argument .
Constraint:
.
3:
q – IntegerInput
On entry: the GARCH argument .
Constraint:
.
4:
theta[] – const doubleInput
On entry: the first element contains the coefficient , the next q elements contain the coefficients , for . The remaining p elements are the coefficients , for .
5:
gamma – doubleInput
On entry: the asymmetry argument for the GARCH sequence.