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Autoregressive Conditional Heteroskedasticity
(redirected from ARCH models)

   Also found in: Wikipedia 0.01 sec.
Autoregressive Conditional Heteroskedasticity (ARCH)
A nonlinear stochastic process, where the variance is time-varying, and a function of the past variance. ARCH processes have frequency distributions which have high peaks at the mean and fat-tails, much like fractal distributions. The ARCH model was invented by Robert Engle. The Generalized ARCH (GARCH) model is the most widely used and was pioneered by Tim Bollerslev. See: Fractal Distributions.

Autoregressive Conditional Heteroskedasticity
A statistical measure of the average error between a best fit line and actual data that uses past data to predict future performance. General Autoaggressive Conditional Heteroskedasticity is the most common way of doing this. See also: Fractal Distribution.


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9780955707605 Estimating and forecasting ARCH models using G@RCH 5.
It is clear from these statistics, however, that various ARCH models may be appropriate in the study of the JEI and KLEI series.
5) Lastrapes (1989) showed that changes in the unconditional variance should receive consideration when specifying ARCH models.
 
 
 
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