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Autoregressive Conditional Heteroskedasticity |
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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. How to thank TFD for its existence? Tell a friend about us, add a link to this page, add the site to iGoogle, or visit webmaster's page for free fun content. |
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The most popular one is the class of autoregressive conditional heteroskedasticity (ARCH) models, introduced by Engle (1982). Engle received the prize for his research on the concept of autoregressive conditional heteroskedasticity (ARCH). The first autoregressive conditional heteroskedasticity (ARCH) model, introduced by Engle (1982), was applied to economic data. |
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