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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.
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The most popular one is the class of autoregressive conditional heteroskedasticity (ARCH) models, introduced by Engle (1982).
Risk management: an analysis of the low-tail behavior of high ... by Alvarez, Susana / Journal of Academy of Business and Economics
Engle received the prize for his research on the concept of autoregressive conditional heteroskedasticity (ARCH).
NYU Stern's Engle Wins 2003 Nobel Prize in Economics by Business Wire
The first autoregressive conditional heteroskedasticity (ARCH) model, introduced by Engle (1982), was applied to economic data.
Heteroskedastic behavior of the Indian stock market: evidence and ... by Karmakar, Madhusudan / Journal of Academy of Business and Economics
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