IGARCHIntegrated Generalized Autoregressive Conditional Heteroskedasticity
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Finally, the IGARCH process captures the temporal pattern of volatility for the Netherlands and Spain, implying that the shocks to the conditional variance persist indefinitely.
Bentes, "Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: New evidence," PhysicaA: Statistical Mechanics and its Applications, vol.
Tabla de condiciones modelos autorregresivos Modelo P Q [lambda] Condicion ARCH >0 0 0 GARCH >0 >0 0 TARCH >0 >0 Binaria [lambda] = 1 o O EGARCH >0 >0 0 In([[sigma].sup.2]) IGARCH >0 >0 0 [[SIGMA].sup.p.sub.i=1] [[alfa].sub.i] + [[SIGMA].sup.p.sub.j=1] [[alfa].sub.j] = 1 Fuente: elaboracion propia.
The sum of ARCH and GARCH effects were equal to (-0.026+1.17) 1.144, therefore the GARCH model can be represented by an IGARCH model, as the sum of ARCH+ GARCH effects were more than the value of 1.
For instance, Engle and Bollerslev (1986) propose the Integrated GARCH (IGARCH) model to capture the impact of a shock on the future volatility over an infinite horizon.
"GETIP" models are GARCH (1,1), EGARCH (1,1), TARCH (1,1), IGARCH (1,1) and PARCH (1,1).
Then after, the Integrated GARCH (IGARCH) Engle and Bollerslev (1994) and the exponential GARCH (EGARCH) Nelson (1991) were significant one wherever re-specification of variance equation was considered.